CN115987727A - Signal transmission method and device - Google Patents

Abstract

The present application provides a signal transmission method and device, which can improve the accuracy rate of decision feedback equalizer training, thereby improving communication quality. The method includes: receiving a signal from a sending end, the signal including an actual received signal and a service signal of a sample signal; training a decision feedback equalizer based on the actual received signal and the sample signal; The first change trend and the first corresponding relationship between the processing result of the received signal and the error between the sample signal determine the adjustment coefficient of the parameter of the decision feedback equalizer, and the first corresponding relationship includes a plurality of change trends and a plurality of adjustment coefficients. According to the corresponding relationship, multiple changing trends include the first changing trend; based on the adjustment coefficient, adjust the parameters of the decision feedback equalizer, and continue to train the decision feedback equalizer; use the completed decision feedback equalizer to perform Interference cancellation processing.

Description

Signal transmission method and device

technical field

The present application relates to the technical field of communications, and in particular to a signal transmission method and device.

Background technique

In the process of transmitting a signal from the transmitting end to the receiving end, for example, the baseband signal specified in 802.11b, multipath signal intersymbol interference exists. The receiving end can reduce the intersymbol interference of the multipath signal through the decision feedback equalizer, thereby improving the communication quality between the sending end and the receiving end. Before the receiving end eliminates intersymbol interference of multipath signals through the decision feedback equalizer, it needs to train the decision feedback equalizer through sample signals, so as to determine the parameter values of the decision feedback equalizer.

At present, during the training process of the decision feedback equalizer, the parameter value of the decision feedback equalizer will continue to change according to the error between the output value of the decision feedback equalizer and the expected value, until the error between the output value of the decision feedback equalizer and the expected value tends to 0.

However, the accuracy rate of the current decision feedback equalizer training is low, and the packet error rate of the data packets received by the receiving end is high, resulting in poor communication quality between the sending end and the receiving end, affecting user experience.

Contents of the invention

The present application provides a signal transmission method and device, which can improve the accuracy of decision feedback equalizer training, reduce the packet error rate of data packets received by the receiving end, thereby improving the communication quality between the sending end and the receiving end, and improving user experience feel.

In the first aspect, a signal transmission method is provided, including: receiving a signal from a sending end, the signal including an actual received signal and a service signal of a sample signal; Carry out training; during the training process, determine the decision feedback equalizer according to the first variation trend and the first corresponding relationship of the error between the processing result of the actual received signal and the sample signal by the decision feedback equalizer The adjustment coefficient of the parameter, the first corresponding relationship includes a plurality of change trends and the corresponding relationship between a plurality of adjustment coefficients, the plurality of change trends include the first change trend; based on the adjustment coefficient, the adjust the parameters of the decision feedback equalizer, and continue to train the decision feedback equalizer; use the decision feedback equalizer that has been trained to perform interference cancellation processing on the service signal.

In the signal transmission method of the present application, during the training process of the decision feedback equalizer, the error between the processing result of the actual received signal and the sample signal by the cumulative preset number of decision feedback equalizers is continuously determined to determine the variation trend of the error, And further adjust the adjustment coefficient of the parameters of the decision feedback equalizer according to the change trend, so that the receiving end can adjust the parameters of the decision feedback equalizer in real time according to the change trend of the error, so that when the number of actual received signals of the decision feedback equalizer is the same In some cases, the error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal can be reduced, and the accuracy of the decision feedback equalizer can be improved, thereby improving the communication quality between the sending end and the receiving end, and improving the user experience. .

It should be understood that the signal sent by the sending end to the receiving end may be a baseband signal specified in 802.11b. The sample signal may refer to the data information agreed by the sending end and the receiving end for the receiving end to train the decision feedback equalizer, that is, the signal sent by the sending end to the receiving end includes the sample signal. The actual received signal of the sample signal refers to the sample signal actually received by the terminal device. After the decision feedback equalizer performs interference cancellation processing on the actual received signal at the receiving end, there is an error between the output result and the sample signal, and the decision feedback equalizer is trained based on the error. The process of training the decision feedback equalizer is the process of continuously adjusting the parameters of the decision feedback equalizer.

In some implementations of the first aspect, the method further includes: inputting the actual received signal into the decision feedback equalizer to obtain a processing result of the actual received signal; determining the processing result of the actual received signal An error between the result and the sample signal; based on the error, the first variation trend is determined.

It should be understood that the error between the processing result of the actual received signal and the sample signal may refer to the difference between the processing result of the actual received signal and the sample signal. The first change trend refers to a change trend of errors, therefore, the first change trend is determined according to at least two errors.

In some implementation manners of the first aspect, the determining the first variation trend based on the error includes: whenever the amount of the error accumulates to a first preset amount, Assume that the number of errors is subjected to mean value filtering to obtain a plurality of smoothing errors; according to the difference between each two adjacent smoothing errors in the plurality of smoothing errors, determine that each two phases of the plurality of smoothing errors are A local variation trend of a smoothing error, the local variation trend includes an upward trend and a downward trend; and the first variation trend is determined according to the quantity of the upward trend and the quantity of the downward trend.

It should be understood that the first preset number may be any positive integer, such as 5, 10 and so on. Performing mean value filtering processing on the first preset number of errors may refer to averaging the first preset number of errors, and the average value is the smoothing error. After the receiving end starts the training of the decision feedback equalizer, each time the receiving end inputs an actual received signal to the decision feedback equalizer, the receiving end will have a corresponding processing result, and an error can be determined according to the processing result and the sample signal. During the training process of the decision feedback equalizer, the receiving end continuously inputs the actual received signal into the decision feedback equalizer, and errors will be continuously obtained. Whenever the amount of errors accumulates to a first preset amount, the receiving end will determine the average value of the first preset amount of errors, so as to obtain a smoothed error. As the accumulated amount of errors further increases, the amount of smoothing errors will also continue to increase.

In some implementation manners of the first aspect, the determining the first change trend according to the number of the uptrend and the number of the downtrend includes: if the number of the uptrend is greater than the number of the downtrend , then determine the first trend of change as an upward trend; or, if the quantity of the upward trend is equal to the quantity of the downward trend, then determine the first trend of change as a stable trend; or, if the If the quantity of the upward trend is smaller than the quantity of the downward trend, the first change trend is determined as a downward trend.

It should be understood that the first changing trend includes an upward trend, a downward trend and a stable trend. The first change trend is determined according to the number of uptrends and downtrends in the local change trend, so that the change trend of the error can be determined more efficiently, thereby helping to improve the efficiency and accuracy of the decision feedback equalizer training.

In some implementation manners of the first aspect, the determining the first change trend according to the number of the uptrend and the number of the downtrend includes: when the number of the plurality of smoothing errors accumulates to the first When there are two preset numbers, the first change trend is determined according to the number of the uptrend and the number of the downtrend.

It should be understood that the second preset number may be any positive integer, such as 3, 9, and so on. Exemplarily, the second preset number is 6, and after each 6 smoothing errors are determined at the receiving end, 5 local variation trends are determined according to every two adjacent smoothing errors among the 6 smoothing errors, and in the 5 local In the changing trend, the first changing trend is determined according to the number of uptrends and the number of downtrends.

In some implementation manners of the first aspect, the first variation trend is obtained based on the first part of the actual received signal; and based on the adjustment coefficient, the parameters of the decision feedback equalizer After the adjustment, the method further includes: determining a second variation trend of the error between the sample signal and the adjusted processing result of the decision feedback equalizer on the second part of the actual received signal; The second change trend and the first corresponding relationship determine the readjustment coefficient of the parameter of the decision feedback equalizer; based on the readjustment coefficient, readjust the parameter of the decision feedback equalizer, and continue The decision feedback equalizer is trained.

It should be understood that the first part of the signal and the second part of the signal in the actual received signal may be part of the actual received signal, or may be all the signals in the actual received signal. When the first partial signal and the second partial signal are partial signals in the actual received signal, the actual received signal may also include a third partial signal, a fourth partial signal, and the like. The second change trend can also be one of an upward trend, a downward trend or a stable trend. The receiving end may determine the second changing trend according to the same method as determining the first changing trend. The above steps are performed in a continuous cycle until the preset stop condition is met.

In a second aspect, a signal transmission device is provided, configured to perform the method in any possible implementation manner of the first aspect above. Specifically, the apparatus includes a module configured to execute the method in any possible implementation manner of the foregoing first aspect.

In a third aspect, the present application provides yet another signal transmission device, including a processor, which is coupled to a memory and can be used to execute instructions in the memory, so as to implement the method in any possible implementation manner of the above-mentioned first aspect . Optionally, the device further includes a memory. Optionally, the device further includes a communication interface, and the processor is coupled to the communication interface.

In an implementation manner, the apparatus is a terminal device. When the device is a terminal device, the above-mentioned communication interface may be a transceiver, or an input/output interface.

In another implementation manner, the device is a chip configured in a terminal device. When the device is a chip configured in a terminal device, the aforementioned communication interface may be an input/output interface.

In a fourth aspect, a processor is provided, including: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive a signal through the input circuit and transmit a signal through the output circuit, so that the processor executes the method in any possible implementation manner of the above first aspect.

In a specific implementation process, the above-mentioned processor can be a chip, the input circuit can be an input pin, the output circuit can be an output pin, and the processing circuit can be a transistor, a gate circuit, a flip-flop, and various logic circuits. The input signal received by the input circuit may be received and input by, for example but not limited to, the receiver, the output signal of the output circuit may be, for example but not limited to, output to the transmitter and transmitted by the transmitter, and the input circuit and the output The circuit may be the same circuit, which is used as an input circuit and an output circuit respectively at different times. The embodiment of the present application does not limit the specific implementation manners of the processor and various circuits.

In a fifth aspect, a processing device is provided, including a processor and a memory. The processor is used to read instructions stored in the memory, and may receive signals through the receiver and transmit signals through the transmitter, so as to execute the method in any possible implementation manner of the first aspect above.

Optionally, there are one or more processors, and one or more memories.

Optionally, the memory may be integrated with the processor, or the memory may be set separately from the processor.

In the specific implementation process, the memory can be a non-transitory (non-transitory) memory, such as a read only memory (ROM), which can be integrated with the processor on the same chip, or can be set in different On the chip, the application does not limit the type of the memory and the arrangement of the memory and the processor.

It should be understood that a related data interaction process, for example, sending indication information may be a process of outputting indication information from a processor, and receiving capability information may be a process of receiving input capability information by a processor. In particular, processed output data may be output to the transmitter, and input data received by the processor may be from the receiver. Wherein, the transmitter and the receiver may be collectively referred to as a transceiver.

The processing device in the above-mentioned fifth aspect may be a chip, and the processor may be implemented by hardware or by software. When implemented by hardware, the processor may be a logic circuit, an integrated circuit, etc.; When implemented, the processor may be a general-purpose processor, which is realized by reading the software code stored in the memory, and the memory may be integrated in the processor, or it may be located outside the processor and exist independently.

According to a sixth aspect, a computer program product is provided, and the computer program product includes: a computer program (also referred to as code, or an instruction), which, when the computer program is executed, causes the computer to perform any of the above-mentioned first aspects. A method in one possible implementation.

In a seventh aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program (also referred to as code, or instruction) which, when run on a computer, causes the computer to execute the above-mentioned first aspect. A method in any of the possible implementations.

Description of drawings

FIG. 1 is a communication system applied in the embodiment of the present application;

FIG. 2 is a schematic diagram of a process of a decision feedback equalizer performing interference cancellation processing;

Fig. 3 is a schematic diagram of the training process of the decision feedback equalizer;

Fig. 4 is a schematic diagram of the convergence trend of errors in the training process of the decision feedback equalizer;

FIG. 5 is a schematic flowchart of a signal transmission method provided in an embodiment of the present application;

FIG. 6 is a schematic diagram of the convergence trend of errors before and after mean filtering processing provided by the embodiment of the present application;

FIG. 7 is a schematic diagram of a comparison of error convergence corresponding to an adaptive adjustment of an adjustment coefficient and no adjustment coefficient provided by the embodiment of the present application;

FIG. 8 is a schematic diagram of the comparison of smoothing error convergence corresponding to another adjustment coefficient adaptive adjustment and no adjustment coefficient provided by the embodiment of the present application;

FIG. 9 is a schematic diagram of the comparison of smoothing error convergence corresponding to another adjustment coefficient adaptive adjustment and no adjustment coefficient provided by the embodiment of the present application;

FIG. 10 is a schematic diagram of the comparison of the smoothing error convergence corresponding to the adaptive adjustment of the adjustment coefficient and the non-adjustment coefficient under different sample signals provided by the embodiment of the present application;

FIG. 11 is a schematic diagram of the relationship curve between the packet error rate and the signal-to-noise ratio corresponding to the adaptive adjustment of the adjustment coefficient and the absence of the adjustment coefficient provided by the embodiment of the present application;

FIG. 12 is a schematic structural diagram of a signal transmission device provided by an embodiment of the present application;

FIG. 13 is a schematic structural diagram of another signal transmission device provided by an embodiment of the present application.

Detailed ways

The technical solution in this application will be described below with reference to the accompanying drawings.

In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with basically the same function and effect. For example, the first numerical value and the second numerical value are only used to distinguish different numerical values, and the sequence thereof is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the number and execution order, and words such as "first" and "second" do not necessarily limit the difference.

It should be noted that, in the embodiments of the present application, words such as "exemplarily" or "for example" are used as examples, illustrations or descriptions. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a concrete manner.

In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. "And/or" describes the association relationship of associated objects, indicating that there may be three types of relationships, for example, A and/or B, which can mean: A exists alone, A and B exist simultaneously, and B exists alone, where A, B can be singular or plural. The character "/" generally indicates that the contextual objects are an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one item (piece) of a, b, or c can represent: a, b, c, a-b, a--c, b-c, or a-b-c, where a, b, c can be single or is multiple.

The technical solutions of the embodiments of the present application can be applied to various communication systems, such as: long term evolution (long termevolution, LTE) system, LTE frequency division duplex (frequency division duplex, FDD) system, LTE time division duplex (time division duplex, TDD), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) Communication System, Fifth Generation (5th Generation, 5G) System or New Radio (New Radio, NR), Wireless LAN communication system specified by 802.11b, new systems that may appear in the future, etc.

The terminal equipment in the embodiment of the present application may also be referred to as: user equipment (user equipment, UE), mobile station (mobile station, MS), mobile terminal (mobile terminal, MT), access terminal, user unit, user station, Mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device, etc.

A terminal device may be a device that provides voice/data connectivity to users, for example, a handheld device with a wireless connection function, a vehicle-mounted device, and the like. At present, some examples of terminal devices include: mobile phone, tablet computer, notebook computer, palmtop computer, mobile internet device (mobile internet device, MID), wearable device, virtual reality (virtual reality, VR) device, augmented reality (augmented reality, AR) equipment, wireless terminals in industrial control, wireless terminals in self driving, wireless terminals in remote medical surgery, wireless terminals in smart grid Terminals, wireless terminals in transportation safety, wireless terminals in smart city, wireless terminals in smart home, cellular phones, cordless phones, session initiation protocol (SIP) phones , wireless local loop (wireless local loop, WLL) station, personal digital assistant (personal digital assistant, PDA), handheld device with wireless communication function, computing device or other processing equipment connected to a wireless modem, vehicle-mounted equipment, wearable Devices, terminal devices in the 5G network or terminal devices in the future evolved public land mobile network (PLMN), etc., are not limited in this application.

As an example but not a limitation, in this application, the terminal device may be a terminal device in an Internet of Things (Internet of Things, IoT) system. The Internet of Things is an important part of the development of information technology in the future. Its main technical feature is to connect objects to the network through communication technology, so as to realize the intelligent network of man-machine interconnection and object interconnection. Exemplarily, the terminal device in this embodiment of the present application may be a wearable device. Wearable devices can also be called wearable smart devices, which is a general term for the application of wearable technology to intelligently design daily wear and develop wearable devices, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that can be worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not only a hardware device, but also can achieve powerful functions through software support, data interaction, and cloud interaction. Generalized wearable smart devices include full-featured, large-sized, complete or partial functions without relying on smart phones, such as smart watches or smart glasses, etc., and only focus on a certain type of application functions, and need to cooperate with other devices such as smart phones Use, such as various smart bracelets and smart jewelry for physical sign monitoring.

As an example but not a limitation, in this embodiment of the present application, the terminal device may also be a terminal device in machine type communication (machine type communication, MTC). In addition, the terminal device can also be an on-board module, on-board module, on-board component, on-board chip, or on-board unit built into the vehicle as one or more components or units. The on-board component, on-board chip, or on-board unit can implement the method provided in this application. Therefore, the embodiments of the present application can also be applied to the Internet of Vehicles, such as vehicle to everything (V2X), vehicle-to-vehicle communication long-term evolution technology (longterm evolution-vehicle, LTE-V), vehicle-to-vehicle (vehicle-to-vehicle, V2V) technology, etc.

The network device involved in this application may be a device that communicates with a terminal device. The network device may also be called an access network device or a wireless access network device. It may be a transmission reception point (TRP), or a The evolved base station (evolved NodeB, eNB or eNodeB) in the LTE system can also be a home base station (for example, home evolved NodeB, or homeNode B, HNB), a base band unit (base band unit, BBU), or a cloud wireless The wireless controller in the access network (cloudradio access network, CRAN) scenario, or the network device can be a relay station, an access point, a vehicle device, a wearable device, and a network device in a 5G network or a PLMN network in the future evolution Network equipment, etc., can also be an access point (access point, AP) in WLAN, or a gNB in an NR system. The above-mentioned network equipment can also be a city base station, a micro base station, a pico base station, a femto base station, etc., This application does not limit this.

In a network structure, the network device may include a centralized unit (centralized unit, CU) node, or a distributed unit (distributed unit, DU) node, or a RAN device including a CU node and a DU node, or a control plane CU node (CU -CP node) and RAN equipment of user plane CU node (CU-UP node) and DU node.

The network device provides services for the cell, and the terminal device communicates with the cell through the transmission resources (for example, frequency domain resources, or spectrum resources) allocated by the network device. The cell may belong to a macro base station (for example, a macro eNB or a macro gNB, etc.) , can also belong to the base station corresponding to the small cell. The small cell here can include: metrocell, micro cell, pico cell, femtocell, etc. These Small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.

In order to facilitate understanding of the embodiment of the present application, a communication system applicable to the embodiment of the present application is described in detail first with reference to FIG. 1 .

Fig. 1 shows a communication system 100 applied in the embodiment of the present application. The communication system 100 may include at least one network device, such as the network device 110 shown in FIG. 1 ; the communication system 100 may also include at least one terminal device, such as the terminal device 120 shown in FIG. 1 . The network device 110 and the terminal device 120 may communicate through a wireless link. In one possible situation, the network device 110 can be used as the sending end, the terminal device 120 can be used as the receiving end, and the network device 110 can send signals to the terminal device 120; in another possible situation, the network device 110 can be used as the receiving end , the terminal device 120 may serve as a sending end, and the terminal device 120 sends a signal to the network device 110 .

FIG. 1 exemplarily shows a network device 110 and a terminal device 120 . Optionally, the communication system 100 may also include multiple network devices and/or multiple terminal devices. The network device 110 may be a router, a base station, etc., and the terminal device 120 may be a mobile phone, a tablet computer, a smart bracelet, etc., which is not limited in this embodiment of the present application.

Each of the aforementioned communication devices, such as the network device 110 or the terminal device 120 in FIG. 1 , may be configured with multiple antennas. The plurality of antennas may include at least one transmit antenna for transmitting signals and at least one receive antenna for receiving signals. In addition, each communication device additionally includes a transmitter chain and a receiver chain, and those of ordinary skill in the art can understand that they may include a plurality of components related to signal transmission and reception (such as processors, modulators, multiplexers, etc.) , demodulator, demultiplexer or antenna, etc.). Therefore, the network device 110 and the terminal device 120 may communicate through the multi-antenna technology.

Optionally, the foregoing communication system 100 may further include other network entities such as a network controller and a mobility management entity, to which this embodiment of the present application is not limited.

The quality of signal transmission between the sender and receiver will directly affect the experience of end users. Since the signal sent from the sender to the receiver, such as the baseband signal specified in 802.11b, is subject to inter-symbol interference of multipath signals, the packet error rate at the receiver is high, which affects user experience. Exemplarily, the sending end is a WiFi router, and the receiving end is a mobile phone. The mobile phone receives the WiFi signal from the WiFi router. When the WiFi signal received by the mobile phone is interfered, the communication quality between the mobile phone and the router is poor, which will affect the mobile phone User's online experience.

In order to eliminate multi-path signal intersymbol interference, the receiving end usually uses a decision feedback equalizer to restore the received aliased signal to an approximate single-path signal, improving the accuracy of signal transmission, thereby reducing the packet error rate at the receiving end. Before the decision feedback equalizer performs interference cancellation processing on the received service signal each time, the receiving end will use sample signals to train the decision feedback equalizer. During the training process of the decision feedback equalizer at the receiving end, the parameter values of the decision feedback equalizer will be continuously changed according to the error between the output value of the decision feedback equalizer and the expected value, so as to improve the accuracy of the decision feedback equalizer.

In order to facilitate the understanding of the embodiment of the present application, the process of performing interference cancellation processing by the decision feedback equalizer and the training process of the decision feedback equalizer will be described in detail below with reference to FIG. 2 to FIG. 4 .

Fig. 2 is the schematic diagram of the process that the decision feedback equalizer carries out the interference elimination processing, as shown in Fig. 2, the decision feedback equalizer includes feedforward filter, feedback filter and decision device; The process of interference elimination processing includes process 1, process 2 and Process 3.

In process 1, the receiving end inputs the discrete data x[n]~x[n+L] from time n to n+L into the feedforward filter. Among them, n is the time, n≥0; n+L is the time after n, n+L>n, L is a positive integer; x[n]~x[n+L] are n to n+L respectively Discrete data at time, x[n]~x[n+L] are complex numbers. The discrete data at each moment in x[n]~x[n+L] corresponds to a parameter (W ₀ ~W _-L ), for example, the parameter corresponding to x[n] is W ₀ , x[n+L] The corresponding parameter is W _-L , and W ₀ ~W _-L are any rational numbers. Through the feed-forward filter, x[n]~x[n+L] and W ₀ ~W _-L are weighted and summed to eliminate the effect of x[n+1]~x[n+L] on x[n] Crosstalk, the formula for weighted summation is x[n]*W ₀ +x[n+1]*W _-1 +x[n+2]*W _-2 +…+x[n+L]*W _-L . The feedback filter stores the post-decision discrete data d[n-1]~d[nM] from n-1 to nM, where M is a positive integer, and n-1 to nM are all less than n; d[n-1] ~d[nM] are respectively the discrete data after judgment at time n-1 to nM respectively; d[n-1]~d[nM] respectively correspond to a parameter (W ₁ ~W _M ) in turn, through the feedback filter, d[n-1]~d[nM] and W ₁ ~W _M carry out weighted summation to eliminate the crosstalk caused by d[n-1]~d[nM]. The formula of the weighted summation is d[n-1 ]*W ₁ +d[n-2]*W ₂ +d[n-3]*W ₃ +…+d[nM]*W _M . Carry out the weighted sum of x[n]~x[n+L] and W ₀ ~W _-L with the weighted sum of d[n-1]~d[nM] and W ₁ ~W _M The summation results in discrete data y[n] at time [n] after eliminating intersymbol interference. After y[n] undergoes hard decision processing by the decider, the discrete data d[n] after decision at time n is obtained.

In process 2, the receiving end inputs d[n] into the feedback filter, and shifts d[n-1]~d[n-M] to the right for the discrete data x[n+1] at time n+1 Perform interference cancellation processing. At the same time, the receiving end inputs the discrete data x[n+L+1] at time n+L+1 into the feedforward filter, and shifts x[n]~x[n+L] to the right.

In process 3, the discrete data x[n+1] at time n+1 is subjected to interference elimination processing, and the discrete data y[n+1] at time [n+1] after the intersymbol interference is eliminated is obtained. After y[n+1] undergoes hard decision processing by the decider, the post-judgment discrete data d[n+1] at time [n+1] is obtained. After d[n+1] is input into the feedback filter, it is used to perform interference elimination processing on x[n+2] and the discrete data after time n+2.

Fig. 3 is a schematic diagram of the training process of the decision feedback equalizer. As shown in Figure 3, after the decision feedback equalizer performs interference elimination processing on discrete data x[n] at time n, it obtains discrete data y[n] at time [n] after intersymbol interference is eliminated. According to the discrete data y[n] at time n and the sample data y[n, e] at time n after intersymbol interference is eliminated, the receiving end can determine an error. The receiving end can adjust the parameters of the decision feedback equalizer according to the error.

It can be understood that error (e _n )=y[n]-y[n, e], where y[n, e] is the sample data at time n, and e _n is the discrete data x of the decision feedback equalizer at time n [n] The difference between the discrete data y[n] obtained after eliminating inter-symbol interference and the sample data y[n, e] at time n.

According to the process of the decision feedback equalizer performing interference elimination on x[n], it can be concluded that: . The least mean square error (least-mean-square, LMS) is , where E _n is the minimum mean square error, and e _n ^H and e _n are conjugate complex numbers.

According to the gradient descent method, the update formula of W ₀ ~W _-L in the feedforward filter is: , where W _f is the parameter W ₀ ~W _-L in the feed-forward filter, W _f,update is the updated W _f , β is the learning rate, 1＞ β ＞0. The update formula of W ₁ ~W _M in the feedback filter is: , where W _b is the parameter W ₁ ~W _M in the feedback filter, W _b,update is the updated W _b , β is the learning rate, and 1> β >0.

It can be understood that during the training process of the decision feedback equalizer, the parameters W ₀ ~W _-L in the feedforward filter and the parameters W ₁ ~W _M in the feedback filter will be continuously updated. The training process of the decision feedback equalizer is the process of determining the parameters W ₀ ~W _-L in the feedforward filter and the parameters W ₁ ~W _M in the feedback filter. Moreover, β may also be referred to as a convergence step size, and the value of β will determine the convergence speed of errors generated during the training process of the decision feedback equalizer. The convergence of the error refers to the state that the error obtained during the training process of the decision feedback equalizer is dynamically reduced and tends to a dynamic stable state.

The convergence trend of errors during the training process of the decision feedback equalizer will be described in detail below with reference to FIG. 4 .

FIG. 4 is a schematic diagram of the convergence trend of errors during the training process of the decision feedback equalizer. As shown in FIG. 4 , in the schematic diagrams of the three convergence trends, the abscissa is the number of errors, that is, the number of iterations in the training process of the decision feedback equalizer, or the number of sample signals, and the ordinate is the error. In the case that the number of sample signals is 1330, the convergence step size is different, and the convergence speed of the error is also different. As shown in the convergence trend (a) in Figure 4, when the convergence step size is moderate, the convergence speed of the error is also moderate; as shown in the convergence trend (b) in Figure 4, when the convergence step size is small The convergence speed of the error is also slower when the convergence step is faster; as shown in the convergence trend (c) in Figure 4, the convergence speed of the error is also faster when the convergence step is faster. However, when the error convergence speed is slow, the error still has a downward trend, indicating that there is still room for error convergence. When the error convergence speed is fast, the convergence trend of the error quickly reaches a stable level, but the convergence error has a large oscillation, which easily leads to poor training accuracy.

However, at present, the training effect of the decision feedback equalizer is poor, so that the packet error rate of the data packets received by the receiving end is high, resulting in poor communication quality between the sending end and the receiving end, which affects user experience.

In order to solve the above technical problems, the present application provides a signal transmission method and device. During the training process of the decision feedback equalizer, the difference between the processing result of the actual received signal and the sample signal is continuously based on the cumulative preset number of decision feedback equalizers. to determine the variation trend of the error, and further adjust the adjustment coefficient of the parameters of the decision feedback equalizer according to the variation trend, so that the receiving end can adjust the parameters of the decision feedback equalizer in real time according to the variation trend of the error. In this way, in the decision feedback When the number of actual received signals of the equalizer is the same, it helps to reduce the error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal, and can improve the accuracy of the decision feedback equalizer, thereby improving the The quality of communication with the receiving end improves the user experience.

The signal transmission method of the present application will be described in detail below with reference to FIG. 5 to FIG. 11 . The embodiments shown in this application show the information transmission method provided by this application from the perspective of device interaction. The specific form and quantity of each device shown therein are only examples, and should not constitute any limitation to the implementation of the method provided in this application. In the following, the information transmission method in the embodiment of the present application will be described in detail by taking the sending end and the receiving end as execution bodies as examples.

It should be understood that the sending end in the embodiment of the present application is a device that sends a signal, which may be a terminal device or a network device itself, or a chip, a chip system or a processor that supports a terminal device or a network device to implement a signal transmission method, or a It is a logical module or software that can realize all or part of terminal equipment or network equipment; the receiving end in the embodiment of this application is a device that receives signals, which can be a terminal equipment or network equipment itself, or can be realized by supporting terminal equipment or network equipment The chip, chip system, or processor of the signal transmission method may also be a logic module or software capable of realizing all or part of terminal equipment or network equipment; this application does not specifically limit this.

FIG. 5 is a schematic flowchart of a signal transmission method 500 provided by an embodiment of the present application. The method 500 is applicable to the communication system 100, and the method 500 includes the following steps:

S501. The sending end sends a signal to the receiving end, where the signal includes an actual received signal of a sample signal and a service signal. Correspondingly, the receiving end receives the signal from the sending end.

It should be understood that the receiving end may be a network device or a terminal device provided with a decision feedback equalizer, such as a mobile phone, a smart bracelet, a WiFi router, and the like. The signal sent by the sending end to the receiving end may be a baseband signal specified in 802.11b. The sample signal may refer to the data information agreed by the sending end and the receiving end for the receiving end to train the decision feedback equalizer, that is, the signal sent by the sending end to the receiving end includes the sample signal. The actual received signal of the sample signal refers to the sample signal actually received by the terminal device. It can be understood that after the sample signal is transmitted from the sending end to the receiving end, the sample signal actually received by the receiving end will change due to the influence of multipath signal intersymbol interference, that is, the actual received signal of the sample signal received by the receiving end is different from the sample signal may be different. Exemplarily, the sample signal sent by the transmitting end to the receiving end is "1234", and the actual received signal of the sample signal received by the receiving end is a sample signal subject to multipath signal intersymbol interference, for example, may be "2224". The service signal is the service data that the receiving end needs to obtain. However, the service data that the receiving end needs to obtain is affected by multipath signal inter-code interference during the process of being transmitted from the sending end to the receiving end, so that the service data that the receiving end needs to obtain will be changes, that is, the service signal received by the receiving end. In the case that the receiving end needs to obtain the correct service signal, the receiving end can perform interference elimination processing on the actually received service signal through the trained decision feedback equalizer.

In a possible implementation manner, the signal includes header information (header) and a physical layer service data unit (physical service data unit, PSDU), where the header information is an actual received signal of the sample signal, and the PSDU is a service signal.

S502. Train the decision feedback equalizer based on the actual received signal and the sample signal.

It should be understood that the sample signal is an accurate signal agreed upon by the sending end and the receiving end, and the actual received signal is the signal actually received by the receiving end. After the decision feedback equalizer performs interference cancellation processing on the actual received signal at the receiving end, there is an error (error) between the output result and the sample signal, and the decision feedback equalizer is trained based on the error. The process of training the decision feedback equalizer is the process of continuously adjusting the parameters of the decision feedback equalizer.

S503. During the training process, determine the adjustment coefficient of the parameter of the decision feedback equalizer according to the first variation trend and the first corresponding relationship between the processing result of the decision feedback equalizer on the actual received signal and the error between the sample signals, the first correspondence The relationship includes correspondence between multiple changing trends and multiple adjustment coefficients, and the multiple changing trends include the first changing trend.

It should be understood that the error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal may refer to the difference between the processing result and the sample signal. The first variation trend may refer to variation trends of at least two errors. Exemplarily, the receiving end continuously obtains three errors, and the first variation trend may be determined according to the three errors.

The parameters of the decision feedback equalizer refer to the parameters in the feedforward filter and the parameters in the feedback filter, such as W ₀ ~W _-L and W ₁ ~W _M in process 1 shown in FIG. 2 . The adjustment coefficient of the parameter of the decision feedback equalizer is a coefficient for adjusting the parameter of the decision feedback equalizer.

The first corresponding relationship includes the corresponding relationship between multiple changing trends and multiple adjustment coefficients. Exemplarily, the first corresponding relationship includes changing trend 1, changing trend 2, and changing trend 3; it also includes adjusting coefficient 1 and adjusting coefficient 2. and an adjustment factor of 3. Wherein, the change trend 1 corresponds to the adjustment coefficient 1, the change trend 2 corresponds to the adjustment coefficient 2, and the change trend 3 corresponds to the adjustment coefficient 3. In a case where the first change trend is change trend 1, the receiving end may determine that the adjustment coefficient is adjustment coefficient 1.

S504. Based on the adjustment coefficient, adjust the parameters of the decision feedback equalizer, and continue to train the decision feedback equalizer.

It should be understood that during the training process of the decision feedback equalizer, the error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal will be continuously obtained, and the number of errors is the number of sample signals. Moreover, during the training process of the decision feedback equalizer, the parameters of the decision feedback equalizer are constantly adjusted. Continuing to train the decision feedback equalizer refers to continuing to repeat S503 to train the decision feedback equalizer after adjusting the parameters of the decision feedback equalizer until the stopping condition is satisfied.

In a possible implementation manner, the stopping condition may be that when the number of iterations of the decision feedback equalizer is equal to the number of actually received signals, the training of the decision feedback equalizer is completed. Exemplarily, the actual received signal includes the first signal, the second signal and the third signal, then the decision feedback equalizer is iteratively trained three times through the first signal, the second signal and the third signal respectively, at this time, the training of the decision feedback equalizer Finish.

In a specific example, the actual received signal includes the first signal, the second signal and the third signal; the sample signal includes the first sample signal, the second sample signal and the third sample signal. The first signal is the actual received signal of the first sample signal; the second signal is the actual received signal of the second sample signal; the third signal is the actual received signal of the third sample signal. The initial state of the parameters of the decision feedback equalizer is a. After the receiving end inputs the first signal into the decision feedback equalizer, the first processing result is obtained, and the first error is determined according to the first processing result and the first sample signal; according to the first The error adjusts the parameters of the decision feedback equalizer. At this time, the parameter of the first error adjustment decision feedback equalizer is a ₁ . The receiving end inputs the second signal into the decision feedback equalizer whose parameter is _a1 , obtains the second processing result, and determines the second error according to the second processing result and the second sample signal. The receiving end can determine the first change trend according to the second error and the first error, and then determine the adjustment coefficient according to the first change trend and the first corresponding relationship, and adjust the parameter of the decision feedback equalizer to a ₂ through the adjustment coefficient. The receiving end inputs the third signal into the decision feedback equalizer whose parameter is a ₂ to obtain a third processing result, and determines a third error according to the third processing result and the third sample signal. The receiving end can determine the second variation trend according to the third error and the second error, then determine the adjustment coefficient according to the second variation tendency and the first corresponding relationship, and adjust the coefficient of the decision feedback equalizer to a ₃ through the adjustment coefficient.

In a possible implementation manner, the parameters of the decision feedback equalizer are determined according to the error between the processing result of the actual received signal and the sample signal, the adjustment coefficient and the learning rate. Optionally, the relationship between the adjustment coefficient and the parameters of the decision feedback equalizer is shown in the following formula: ; , where R is the adjustment coefficient, which can also be represented by ratio; W _f is the parameter W ₀ ~W _-L in the feedforward filter, W _f,update is the updated W _f , and W _b is the parameter in the feedback filter Parameters W ₁ ~W _M , W _b,update is the updated W _b , β is the learning rate, 1> β >0.

It should be understood that the receiving end determines the adjustment coefficient through the first change trend of the error, so that the parameters of the decision feedback equalizer can be changed in real time according to the error, so that the training efficiency of the decision feedback equalizer can be improved, the training effect of the decision feedback equalizer can be improved, and the error of the receiving end can be reduced. Packet rate, improve the communication quality of the sending end and the receiving end, and improve the user experience.

S505. Using the trained decision feedback equalizer, perform interference cancellation processing on the service signal.

It should be understood that the trained decision feedback equalizer is the decision feedback equalizer whose parameters have been determined. In a possible implementation manner, the receiving end includes a decision feedback equalizer, and after the receiving end completes training of the decision feedback equalizer through sample signals, a trained decision feedback equalizer is obtained. In another possible implementation, the receiving end includes a decision feedback equalizer used for training and a decision feedback equalizer used for interference cancellation processing, and the decision feedback equalizer used for training is trained by sample signals at the receiving end After completion, the decision feedback equalizer used for training sends the determined parameters to the decision feedback equalizer used for interference cancellation processing, and the decision feedback equalizer used for interference cancellation processing adopts the decision feedback equalizer used for training. After the parameters, it is the decision feedback equalizer that has been trained.

In a possible implementation manner, the trained parameters of the decision feedback equalizer may be the parameters corresponding to the minimum error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal during the training process. Exemplarily, when the parameter of the decision feedback equalizer is c ₁ , the error between the obtained processing result and the sample signal is g ₁ ; when the parameter of the decision feedback equalizer is c ₂ , the error between the obtained processing result and the sample signal is is g ₂ ; when the parameter of the decision feedback equalizer is c ₃ , the error between the obtained processing result and the sample signal is g ₃ . In the case of g ₁ >g ₂ >g ₃ , the receiving end may determine that the parameter of the trained decision feedback equalizer is c ₃ .

In another possible implementation manner, the trained parameters of the decision feedback equalizer may also be the parameters used in the last iteration of the decision feedback equalizer. Exemplarily, when the parameter of the decision feedback equalizer is c ₁ , the error between the obtained processing result and the sample signal is g ₁ ; when the parameter of the decision feedback equalizer is c ₂ , the error between the obtained processing result and the sample signal is is g ₂ ; when the parameter of the decision feedback equalizer is c ₃ , the error between the obtained processing result and the sample signal is g ₃ , and in the case of g ₁ >g ₃ >g ₂ , the receiving end can determine the decision of training completion The parameter of the feedback equalizer is c ₃ .

In the signal transmission method of the present application, during the training process of the decision feedback equalizer, according to the error between the processing result of the actual received signal and the sample signal accumulated by the predetermined number of decision feedback equalizers, the variation trend of the error is determined, and Further adjust the adjustment coefficient of the parameters of the decision feedback equalizer according to the change trend, so that the receiving end can adjust the parameters of the decision feedback equalizer in real time according to the change trend of the error. In this way, when the actual number of received signals of the decision feedback equalizer is the same In this way, the error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal can be reduced, and the accuracy of the decision feedback equalizer can be improved, thereby improving the communication quality between the sending end and the receiving end, and improving user experience.

As an optional embodiment, the method 500 further includes: the receiving end inputs the actual received signal to the decision feedback equalizer to obtain the processing result of the actual received signal; determining the error between the processing result of the actual received signal and the sample signal; based on Error, determine the first change trend.

It should be understood that the error between the processing result of the actual received signal and the sample signal may refer to the difference between the processing result of the actual received signal and the sample signal. The first change trend refers to a change trend of errors, therefore, the first change trend is determined according to at least two errors.

In a possible implementation manner, based on the error, determining the first trend of change includes: whenever the amount of errors accumulates to a first preset amount, performing mean value filtering on the first preset amount of errors to obtain multiple Smoothing error; according to the difference between every two adjacent smoothing errors in the multiple smoothing errors, determine the local variation trend of each two adjacent smoothing errors in the multiple smoothing errors, the local variation trend includes an upward trend and a downward trend; Based on the number of uptrends and the number of downtrends, the first changing trend is determined.

It should be understood that the first preset number may be any positive integer, such as 5, 10 and so on. Optionally, the first preset quantity may also be represented by a window length (window length). Performing mean filtering processing on the first preset number of errors may refer to taking an average value of the first preset number of errors, and the average value is a smoothed error (smoothed error). After the receiving end starts the training of the decision feedback equalizer, each time the receiving end inputs an actual received signal to the decision feedback equalizer, the receiving end will have a corresponding processing result, and an error can be determined according to the processing result and the sample signal. During the training process of the decision feedback equalizer, the receiving end continuously inputs the actual received signal into the decision feedback equalizer, and errors will be continuously obtained. Whenever the amount of errors accumulates to a first preset amount, the receiving end will determine the average value of the first preset amount of errors, so as to obtain a smoothed error. As the accumulated amount of errors further increases, the amount of smoothing errors will also continue to increase.

The mean filtering process will be further described below in conjunction with FIG. 6 .

FIG. 6 is a schematic diagram of a convergence trend of errors before and after mean filtering processing provided by the embodiment of the present application. As shown in the error convergence trend (a) in Figure 6, the number of errors is 1365, that is, a total of 1365 iterations are performed during the training process of the decision feedback equalizer. The first preset number is 21, that is, during the training process of the decision feedback equalizer, every time 21 errors are obtained, the mean value filtering process will be performed on the 21 errors to obtain a smooth error. After the decision feedback equalizer training is completed, A total of 65 smoothing errors are obtained, and the convergence trend of the 65 smoothing errors is shown in the convergence trend (b) in Figure 6. It can be seen from Fig. 6 that by performing mean value filter processing on the first preset number of errors, it is helpful for the receiving end to determine the variation trend of the errors, thus helping to improve the convergence efficiency of the errors and improve the training of the decision feedback equalizer Effect.

Adjacent smoothing errors refer to two smoothing errors continuously determined by the receiving end, and multiple errors for determining adjacent smoothing errors are continuously output by the decision feedback equalizer. Exemplarily, in the convergence trend (b) shown in Figure 6, every two adjacent points are adjacent smoothing errors, for example, the smoothing error corresponding to point A and the smoothing error corresponding to point B are adjacent smoothing errors ;point. The local variation trend can be defined as the adjacent smoothing error according to the smoothing error corresponding to the adjacent point B and the smoothing error corresponding to the point C. The local change trend can be determined according to the difference between adjacent smoothing errors. When the latter smoothing error is greater than the previous smoothing error among the adjacent smoothing errors, the receiving end can determine that the local change trend is a downward trend; among the adjacent smoothing errors When the latter smoothing error is smaller than the previous smoothing error, the receiver can determine that the local change trend is an upward trend. Exemplarily, in the convergence trend (b) shown in FIG. 6 , the smoothing error corresponding to point A is 1.1, the smoothing error corresponding to point B is 0.8, and the smoothing error corresponding to point C is 0.9. Point A and point B are adjacent smoothing errors, where point B is the latter smoothing error of the two adjacent smoothing errors, and point A is the previous smoothing error, since the smoothing error corresponding to point B is smaller than that corresponding to point A smoothing error, therefore, a local change trend determined by the receiving end according to the adjacent smoothing error is a downward trend. Similarly, point B and point C are adjacent smoothing errors, and according to the two adjacent smoothing errors, the receiving end can determine a local change trend as an upward trend.

In a possible implementation, the local variation trend further includes a stable trend, and when the difference between adjacent smoothing errors is less than or equal to a first preset threshold, it is determined that the local variation trend of the adjacent smoothing errors is Stable trend: when the difference between adjacent smoothing errors is greater than the first preset threshold, and the latter smoothing error in the adjacent smoothing errors is greater than the previous smoothing error, determine the local variation trend of the adjacent smoothing errors is an upward trend; when the difference between adjacent smoothing errors is greater than the first preset threshold, and the latter smoothing error in the adjacent smoothing errors is smaller than the previous smoothing error, determine the local change of the adjacent smoothing errors The trend is downtrend.

It should be understood that the first preset threshold is any value greater than or equal to 0, for example, it may be 0, 0.01 and so on. When the difference between adjacent smoothing errors is less than or equal to the first preset threshold, it means that the two adjacent smoothing errors are close, and the local change trend can be determined as a stable trend. Exemplarily, the first smoothing error and the second smoothing error are adjacent smoothing errors, the second smoothing error and the third smoothing error are adjacent smoothing errors, the first smoothing error is 1.0, the second smoothing error is 0.9, and the third The smoothing error is 0.91, and the first preset threshold is 0.02, then according to the first smoothing error and the second smoothing error, the receiving end can determine that a local change trend is a downward trend; according to the second smoothing error and the third smoothing error, the receiving end A local change trend can be determined as a stable trend.

In a possible implementation manner, determining the first change trend according to the number of uptrends and the number of downtrends includes: determining the first change trend as an uptrend if the number of uptrends is greater than the number of downtrends; Or, if the number of uptrends is equal to the number of downtrends, the first changing trend is determined as a stable trend; or, if the number of uptrends is less than the number of downtrends, then the first changing trend is determined as a downtrend.

It should be understood that the first changing trend includes an upward trend, a downward trend and a stable trend. The first change trend is determined according to the number of uptrends and downtrends in the local change trend, so that the change trend of the error can be determined more efficiently, thereby helping to improve the efficiency and accuracy of the decision feedback equalizer training.

In another possible implementation manner, determining the first change trend according to the number of uptrends and the number of downtrends includes: when the number of multiple smoothing errors accumulates to a second preset number, according to the number of uptrends and the number of downtrends to determine the first changing trend. Optionally, the second preset number may also be represented by a count number (count_num).

It should be understood that the second preset number may be any positive integer, such as 3, 9, and so on. Exemplarily, the second preset number is 6, and after each 6 smoothing errors are determined at the receiving end, 5 local variation trends are determined according to every two adjacent smoothing errors among the 6 smoothing errors, and in the 5 local In the changing trend, the first changing trend is determined according to the number of uptrends and the number of downtrends.

Optionally, determining the first change trend according to the number of uptrends and the number of downtrends includes: when the number of multiple smoothing errors accumulates to a second preset number, if the number of uptrends is greater than the number of downtrends, And the difference between the number of uptrends and the number of downtrends is greater than or equal to the second preset threshold, then the first change trend is determined as an uptrend; or, if the number of uptrends is less than the number of downtrends, and the downtrend If the difference between the number of trends and the number of rising trends is greater than or equal to the third preset threshold, the first changing trend is determined as a downward trend; otherwise, the first changing trend is determined as a stable trend.

It should be understood that both the second preset threshold and the third preset threshold are any preset positive integers. The first changing trend may be an upward trend, a downward trend or a stable trend, and if the first changing trend is not an upward trend or a downward trend, the first changing trend is a stable trend. Exemplarily, the second preset threshold is 2, and the third preset threshold is 3. In the case where the number of uptrends in the local change trend is 3 more than the number of downtrends, the receiving end determines that the first change trend is an uptrend Trend; in the case where the number of downtrends is 3 more than the number of uptrends in the local change trend, the receiving end determines that the first change trend is a downtrend; the number of uptrends in the local change trend is 1 more than the number of downtrends In the case of , the receiver determines that the first change trend is a stable trend.

In a possible implementation manner, the decision feedback equalizer includes an up accumulator and a down accumulator, and determines the first change trend according to the number of up trends and the number of down trends, including: each time the receiving end determines an up trend, the up trend The accumulator accumulates a +1; each time the receiving end determines a downward trend, the descending accumulator accumulates a -1, when the number of multiple smoothing errors accumulates to the third preset number, according to the value accumulated by the ascending accumulator and the descending accumulator The sum of the accumulated values determines the first variation trend. When the sum of the accumulated values of the rising accumulator and the accumulated values of the falling accumulator is greater than or equal to the fourth preset threshold, it is determined that the first change trend is an upward trend; When the sum of is less than or equal to the fifth preset threshold, it is determined that the first change trend is a downward trend; otherwise, it is determined that the first change trend is a stable trend. Optionally, the value accumulated by the rising accumulator can be represented by flag_up, that is, every time the rising accumulator accumulates a local change trend as an upward trend, flag_up will be added with +1; the value accumulated by the falling accumulator can be represented by flag_down, that is, the rising accumulator Each accumulated local change trend is a downward trend, and flag_down is added with -1. Optionally, the fourth preset threshold may also be represented by up_thr, and the fifth preset threshold may also be represented by down_thr.

In a specific example, the third preset number is 6, the fourth preset threshold is 2, and the fifth preset threshold is -3. After 6 smoothing errors are determined at the receiving end, the local change trend includes 3 upward trends , the accumulated value of the rising accumulator is +3; the local change trend includes 2 upward trends, the accumulated value of the falling accumulator is -2, and the sum of the accumulated value of the rising accumulator and the accumulated value of the falling accumulator is -1 , then the receiving end determines that the first changing trend is a stable trend.

If the quantity of the upward trend is greater than the quantity of the downward trend, and the difference between the quantity of the upward trend and the quantity of the downward trend is greater than or equal to the second preset threshold value, then the first change trend is determined as an upward trend; or, if the upward trend If the number of trends is less than the number of downtrends, and the difference between the number of downtrends and the number of uptrends is greater than or equal to the third preset threshold, the first changing trend is determined as a downtrend; otherwise, the first The changing trend is determined as a stable trend.

In a possible implementation, the multiple trends include an upward trend, a downward trend, and a stable trend, and the first correspondence includes: when the first trend is an upward trend, the adjustment coefficient is p times the initial adjustment coefficient ; In the case where the first change trend is a downward trend, the adjustment coefficient is q times the initial adjustment coefficient; when the first change trend is a stable trend, the adjustment coefficient is s times the initial adjustment coefficient; p, q and s are all values greater than 0. The initial adjustment coefficient is the adjustment coefficient before adjustment according to the first variation trend and the first corresponding relationship.

In a specific example, p is 0.65, q is 1.5, and s is 0.85. In the case of an adjustment factor of 2, the receiving end determines 6 smoothing errors, and among the 5 local change trends determined according to the 6 smoothing errors, the number of upward trends is greater than the number of downward trends, and the first change is determined at the receiving end If the trend is upward, the adjustment coefficient change is 1.3. Then, the receiving end determines the parameters of the decision feedback equalizer according to the changed adjustment coefficient, and continues to train the decision feedback equalizer based on the newly determined parameters of the decision feedback equalizer.

As an optional embodiment, the first variation trend is obtained based on the first part of the actual received signal; after adjusting the parameters of the decision feedback equalizer based on the adjustment coefficient, the method 500 further includes: determining the adjusted A second change trend of the error between the processing result of the second part of the actual received signal and the sample signal by the decision feedback equalizer; based on the second change trend and the first corresponding relationship, determine the readjustment of the parameters of the decision feedback equalizer coefficients; based on the readjustment coefficients, the parameters of the decision feedback equalizer are readjusted, and the decision feedback equalizer is continued to be trained.

It should be understood that the first part of the signal and the second part of the signal in the actual received signal may be part of the actual received signal, or may be all the signals in the actual received signal. When the first partial signal and the second partial signal are partial signals in the actual received signal, the actual received signal may also include a third partial signal, a fourth partial signal, and the like. The second change trend can also be one of an upward trend, a downward trend or a stable trend. The receiving end may determine the second changing trend according to the same method as determining the first changing trend. The above steps are performed in a continuous cycle until the preset stop condition is met.

The signal transmission method of the present application will be further introduced below with reference to FIGS. 7 to 10 and specific examples.

FIG. 7 is a schematic diagram of a comparison of error convergence corresponding to an adaptive adjustment of an adjustment coefficient and no adjustment coefficient provided by an embodiment of the present application. As shown in FIG. 7 , when the number of sample signals is 1330, the number of iterations in the training process of the decision feedback equalizer and the number of errors obtained are also 1330. In the current signal transmission method, there is no adjustment coefficient, that is, the convergence step is always 1, and the convergence speed of the error is relatively slow. The convergence trend of the determined error is shown in the variation trend of the error (a) in Figure 7. The decision feedback equalizer is trained through the signal transmission method of the present application, and the adjustment coefficient is constantly adaptively adjusted according to the first variation trend and the first corresponding relationship. In the decision feedback equalizer training process, the initial adjustment coefficient is 2.5, the first preset number above is 11, the second preset number above is 6, the fourth preset threshold above is 2, and above The fifth preset threshold value of is -3, and the convergence trend of the determined error is shown in the change trend (b) in Fig. 7 . The 1330 errors obtained when there is no adjustment coefficient are subjected to mean value filtering, and 65 smoothing errors are obtained. The change trend of the 65 smoothing errors is shown as 701 in Figure 7; The errors are subjected to mean filtering processing to obtain 65 smoothing errors, and the variation trend of the 65 smoothing errors is shown as 702 in FIG. 7 . During the training process of the decision feedback equalizer, every time 5 smoothing errors are obtained, the receiving end will determine 4 local changing trends according to any two adjacent smoothing errors among the 5 smoothing errors. When the number is greater than or equal to 2, determine that the first change trend is an upward trend, and adjust the adjustment coefficient to 0.65 times the initial adjustment coefficient; when the number of the downward trend is greater than or equal to 3 than the number of the upward trend, determine the second If the first change trend is a downward trend, adjust the adjustment coefficient to 1.5 times the initial adjustment coefficient; otherwise, determine that the first change trend is a stable trend, and adjust the adjustment coefficient to 0.85 times the initial adjustment coefficient. In this way, the change process of the adjustment coefficient is shown in the change process (d) in FIG. 7 .

Comparing 701 and 702, it can be seen that the signal transmission method of the present application can improve the convergence efficiency of errors, so that when the number of sample signals is determined, the accuracy of the decision feedback equalizer training can be improved. In this way, through The decision feedback equalizer with higher accuracy can reduce the packet error rate at the receiving end, improve the communication quality between the sending end and the receiving end, and improve the user experience.

FIG. 8 is a schematic diagram of a comparison of smoothing error convergence corresponding to another adjustment coefficient adaptive adjustment and no adjustment coefficient provided by the embodiment of the present application. As shown in Figure 8, the actual received signal at the receiving end comes from the B channel, and a total of 50 smoothing errors are determined at the receiving end. During the training process of the decision feedback equalizer at the receiving end, when there is no adjustment coefficient, the convergence trend of the smoothing error is as shown in 801 It shows that when there are 35 smoothing errors, the smoothing error tends to converge; in the training process of the decision feedback equalizer at the receiving end, in the case of adaptive adjustment of the adjustment coefficient, the first preset number above is 11, and the convergence of the smoothing error The trend is shown in 802, when there are 10 smoothing errors, the smoothing errors tend to converge. Comparing 801 and 802, it can be seen that the signal transmission method of the present application shortens the convergence time of decision feedback equalizer training to 27% of the convergence time of decision feedback equalizer training without adjustment coefficient. It can be seen that, in the signal transmission method of the present application, during the training process of the decision feedback equalizer, the adjustment coefficient is adaptively adjusted, so that the training efficiency and accuracy of the decision feedback equalizer can be improved, thereby improving the equalization result of the decision feedback equalizer the accuracy rate.

FIG. 9 is a schematic diagram of a comparison of smoothing error convergence corresponding to still another adjustment coefficient adaptive adjustment and no adjustment coefficient provided by the embodiment of the present application. As shown in Figure 9, the receiving end determines 75 smoothing errors in total. During the training process of the decision feedback equalizer at the receiving end, under the condition of no adjustment coefficient, the convergence trend of the smoothing error is shown in 901; the decision feedback equalizer at the receiving end During the training process, in the case of adaptive adjustment of the adjustment coefficient, the first preset number above is 11, and the convergence trend of the smoothing error is shown in 902 . Comparing 901 and 902, it can be seen that the training efficiency of the decision feedback equalizer can be improved through the signal transmission method of the embodiment of the present application. Enlarging the region 903 in the convergence trend (a) of the smoothing error in Figure 9, it can be seen that after the smoothing error converges, when there is no adjustment coefficient, the convergence trend of the smoothing error is shown in 904; when the smoothing error converges Finally, in the case of adaptive adjustment of the adjustment coefficient, the convergence trend of the smoothing error is shown in 904 .

Exemplarily, in the embodiment of the present application, the actual received signal at the receiving end may come from one or more channels of the B channel, the C channel, the D channel, the E channel, and the F channel. B channel, C channel, D channel, E channel and F channel are five kinds of analog channels simulated by matrix laboratory (MATLAB). In the process of being transmitted from the sending end to the receiving end, the sample signal will receive different degrees of inter-symbol interference. Therefore, after receiving the actual received signals from the five channels, the receiving end will train the decision feedback equalizer based on the actual received signals, and perform interference cancellation processing on the service signals through the trained decision feedback equalizer.

FIG. 10 is a schematic diagram of the comparison of smoothing error convergence corresponding to the adjustment coefficient adaptive adjustment and no adjustment coefficient under different sample signals provided by the embodiment of the present application. As shown in FIG. 10 , the numbers of smoothing errors determined by the receiving end are all 50. In addition, shown in (a) to (e) in Fig. 10 are the smoothing errors when the decision feedback equalizer is trained according to the actual received signals from the B-channel, C-channel, D-channel, E-channel and F-channel respectively Schematic diagram of the convergence trend. When the actual received signals at the receiving end are all from the B channel, in the case of no adjustment coefficient, the convergence trend of the smoothing error is shown as 1001; in the case of adaptive adjustment of the adjustment coefficient, the first preset number above is 11 , the convergence trend of the smoothing error is shown in 1002 . When the actual received signals at the receiving end are all from the C channel, in the case of no adjustment coefficient, the convergence trend of the smoothing error is shown in 1003; in the case of adaptive adjustment of the adjustment coefficient, the first preset number above is 11 , the convergence trend of the smoothing error is shown in 1004 . When the actual received signals at the receiving end all come from the D channel, in the case of no adjustment coefficient, the convergence trend of the smoothing error is shown in 1005; in the case of adaptive adjustment of the adjustment coefficient, the first preset number above is 11 , the convergence trend of the smoothing error is shown in 1006 . When the actual received signals at the receiving end are all from the E channel, in the case of no adjustment coefficient, the convergence trend of the smoothing error is shown in 1007; in the case of adaptive adjustment of the adjustment coefficient, the first preset number above is 11 , the convergence trend of the smoothing error is shown in 1008 . When the actual received signals at the receiving end all come from the F channel, in the case of no adjustment coefficient, the convergence trend of the smoothing error is shown in 1009; in the case of adaptive adjustment of the adjustment coefficient, the first preset number above is 11 , the convergence trend of the smoothing error is shown in 1010. Convergence trend 1001 and convergence trend 1002, convergence trend 1003 and convergence trend 1004, convergence trend 1005 and convergence trend 1006, convergence trend 1007 and convergence trend 1008, convergence trend 1009 and convergence trend 1010 are compared respectively, it can be seen that the present application In the signal transmission method of the decision feedback equalizer, the adjustment coefficient is adaptively adjusted during the training process of the decision feedback equalizer. In this way, for different actual received signals, the training efficiency and accuracy of the decision feedback equalizer can be improved, thereby improving the decision feedback equalizer. The accuracy of the equilibrium result.

FIG. 11 is a schematic diagram of the relationship curves between the packet error rate and the signal-to-noise ratio when the adjustment coefficient is adaptively adjusted and when there is no adjustment coefficient provided by the embodiment of the present application. As shown in Figure 11, no matter whether the adjustment coefficient is adaptively adjusted or not, the packet error rate (PER-PSDU) shows a decreasing trend as the signal to noise ratio (SNR) increases. In the case of no adjustment coefficient, the relationship curve between the packet error rate and the signal-to-noise ratio is shown in 1101. In the case where the parameters of the feedback equalizer are adaptively adjusted based on the adjustment coefficient, the relationship curve between the packet error rate and the signal-to-noise ratio is as follows 1102 shown. Comparing 1101 and 1102, in the case of the same signal-to-noise ratio, the corresponding packet error rate in 1102 is lower. Exemplarily, when the signal-to-noise ratio is 32dB, the corresponding packet error rate in 1101 is 0.173, and the corresponding packet error rate in 1102 is 0.112. Through the signal transmission method of the embodiment of the present application, the packet error rate at the receiving end is reduced by about 35%.

It should be understood that the serial numbers of the above methods do not mean the order of execution, and the order of execution of the methods should be determined by their functions and internal logic.

The signal transmission method of the embodiment of the present application is described in detail above with reference to FIG. 2 to FIG. 11 , and the signal transmission device of the embodiment of the present application is described in detail below with reference to FIG. 12 and FIG. 13 .

FIG. 12 is a schematic structural diagram of a signal transmission device 1200 provided by an embodiment of the present application. As shown in FIG. 12 , an apparatus 1200 includes: a transceiver module 1201 and a processing module 1202 .

The apparatus 1200 is configured to implement the steps corresponding to the receiving end in the above method embodiments.

The transceiver module 1201 is configured to receive a signal from the sending end, and the signal includes the actual received signal and the service signal of the sample signal;

The processing module 1202 is configured to train the decision feedback equalizer based on the actual received signal and the sample signal; during the training process, according to the first variation trend of the error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal and the first corresponding relationship, determine the adjustment coefficient of the parameter of the decision feedback equalizer, the first corresponding relationship includes the corresponding relationship between a plurality of change trends and a plurality of adjustment coefficients, and the plurality of change trends include the first change trend; based on the adjustment coefficient , adjust the parameters of the decision feedback equalizer, and continue to train the decision feedback equalizer; use the trained decision feedback equalizer to perform interference elimination processing on the service signal.

Optionally, the processing module 1202 is also used to: input the actual received signal into the decision feedback equalizer to obtain the processing result of the actual received signal; determine the error between the processing result of the actual received signal and the sample signal; determine the first A changing trend.

Optionally, the processing module 1202 is specifically configured to: whenever the number of errors accumulates to a first preset number, perform mean value filtering processing on the first preset number of errors to obtain multiple smoothing errors; The difference between every two adjacent smoothing errors determines the local variation trend of each two adjacent smoothing errors in multiple smoothing errors, the local variation trend includes upward trend and downward trend; Quantity, to determine the first trend of change.

Optionally, the processing module 1202 is specifically configured to: if the number of uptrends is greater than the number of downtrends, determine the first changing trend as an uptrend; or, if the number of uptrends is equal to the number of downtrends, determine the first change trend The change trend is determined as a stable trend; or, if the number of the upward trend is less than the number of the downward trend, the first change trend is determined as the downward trend.

Optionally, the processing module 1202 is specifically configured to: when the number of multiple smoothing errors accumulates to a second preset number, determine the first change trend according to the number of uptrends and the number of downtrends.

Optionally, the first variation trend is obtained based on the first part of the signal in the actual received signal; the processing module 1202 is further configured to: determine the adjusted decision feedback equalizer on the second part of the signal in the actual received signal. The second variation trend of the error between the sample signals; based on the second variation trend and the first corresponding relationship, determine the readjustment coefficient of the parameter of the decision feedback equalizer; based on the readjustment coefficient, readjust the parameter of the decision feedback equalizer, And continue to train the decision feedback equalizer.

It should be understood that the device 1200 here is embodied in the form of functional modules. The term "module" here may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group processor, etc.) and memory, incorporated logic, and/or other suitable components to support the described functionality. In an optional example, those skilled in the art can understand that the device 1200 can be specifically the receiving end in the above-mentioned embodiment, and the device 1200 can be used to execute various processes and/or steps corresponding to the receiving end in the above-mentioned method embodiment , to avoid repetition, it will not be repeated here.

The above-mentioned apparatus 1200 has the function of realizing the corresponding steps executed by the receiving end in the above-mentioned method; the above-mentioned functions can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above functions.

In the embodiment of the present application, the device 1200 in FIG. 12 may also be a chip, for example: SOC. Correspondingly, the processing module 1202 may be a transceiver circuit of the chip, which is not limited here.

FIG. 13 is a schematic structural diagram of a signal transmission device 1300 provided by an embodiment of the present application. The apparatus 1300 includes a processor 1301 , a transceiver 1302 and a memory 1303 . Wherein, the processor 1301, the transceiver 1302 and the memory 1303 communicate with each other through an internal connection path, the memory 1303 is used to store instructions, and the processor 1301 is used to execute the instructions stored in the memory 1303 to control the transceiver 1302 to send signals and /or to receive a signal.

It should be understood that the apparatus 1300 may specifically be the receiving end in the foregoing embodiments, and may be configured to execute various steps and/or processes corresponding to the receiving end in the foregoing method embodiments. Optionally, the memory 1303 may include read-only memory and random-access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information. The processor 1301 may be used to execute instructions stored in the memory, and when the processor 1301 executes the instructions stored in the memory, the processor 1301 is used to execute various steps and/or processes of the foregoing method embodiments. The transceiver 1302 may include a transmitter and a receiver, the transmitter may be used to implement various steps and/or processes for performing sending actions corresponding to the above transceiver, and the receiver may be used to implement the application corresponding to the above transceiver Various steps and/or processes for performing receiving actions.

It should be understood that, in the embodiment of the present application, the processor may be a central processing unit (central processing unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC) , field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the methods disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register. The storage medium is located in the memory, and the processor executes the instructions in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, no detailed description is given here.

The present application also provides a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program, and the computer program is used to implement the methods shown in the above method embodiments.

The present application also provides a computer program product. The computer program product includes a computer program (also called code, or instruction). When the computer program is run on a computer, the computer can execute the above-mentioned methods shown in the above method embodiments. method.

Those skilled in the art can appreciate that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described system, device, and module can refer to the corresponding process in the foregoing method embodiment, and details are not repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or modules may be in electrical, mechanical or other forms.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, each module may exist separately physically, or two or more modules may be integrated into one module.

If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), magnetic disk or optical disk and other media that can store program codes. .

The above is only the specific implementation of the application, but the protection scope of the embodiment of the application is not limited thereto, and any skilled person familiar with the technical field can easily think of changes within the technical scope disclosed in the embodiment of the application Or replacement, should be covered within the scope of protection of the embodiments of the present application. Therefore, the scope of protection of the embodiments of the present application should be based on the scope of protection of the claims.

Claims (15)

1. A signal transmission method, comprising:

receiving a signal from a transmitting end, wherein the signal comprises an actual receiving signal of a sample signal and a service signal;

training a decision feedback equalizer based on the actual received signal and the sample signal;

in the training process, determining an adjustment coefficient of a parameter of the decision feedback equalizer according to a first variation trend and a first corresponding relationship of an error between a processing result of the decision feedback equalizer on the actual received signal and the sample signal, wherein the first corresponding relationship comprises a plurality of variation trends and a plurality of corresponding relationships between the adjustment coefficients, and the plurality of variation trends comprise the first variation trend;

adjusting parameters of the decision feedback equalizer based on the adjustment coefficient, and continuing training the decision feedback equalizer;

and carrying out interference elimination processing on the service signal by utilizing the trained decision feedback equalizer.

2. The method of claim 1, further comprising:

inputting the actual received signal to the decision feedback equalizer to obtain a processing result of the actual received signal;

determining an error between a result of the processing of the actual received signal and the sample signal;

based on the error, the first trend of change is determined.

3. The method of claim 2, wherein the determining the first trend of change based on the error comprises:

when the number of the errors is accumulated to a first preset number, carrying out mean value filtering processing on the errors of the first preset number to obtain a plurality of smooth errors;

determining a local variation trend of each two adjacent smooth errors in the plurality of smooth errors according to a difference value between each two adjacent smooth errors in the plurality of smooth errors, wherein the local variation trend comprises an ascending trend and a descending trend;

and determining the first change trend according to the number of the ascending trends and the number of the descending trends.

4. The method of claim 3, wherein determining the first trend of change based on the number of upward trends and the number of downward trends comprises:

if the number of the ascending trends is larger than the number of the descending trends, determining the first change trend as an ascending trend; or,

if the number of the ascending trends is equal to the number of the descending trends, determining the first change trend as a stable trend; or,

and if the number of the ascending trends is smaller than the number of the descending trends, determining the first changing trend as a descending trend.

5. The method according to claim 3 or 4, wherein said determining said first trend of change from said number of upward trends and said number of downward trends comprises:

and when the quantity of the plurality of smoothing errors is accumulated to a second preset quantity, determining the first change trend according to the quantity of the ascending trends and the quantity of the descending trends.

6. The method according to any one of claims 1 to 4, wherein the first trend is obtained based on a first partial signal in the actual received signal;

after the adjusting the parameter of the decision feedback equalizer based on the adjustment coefficient, the method further comprises:

determining a second variation trend of the error between the processing result of the adjusted decision feedback equalizer on the second part of signals in the actual received signals and the sample signals;

determining a readjustment coefficient of the parameter of the decision feedback equalizer based on the second variation trend and the first corresponding relation;

and adjusting the parameters of the decision feedback equalizer again based on the readjustment coefficient, and continuing to train the decision feedback equalizer.

7. A signal transmission apparatus, comprising:

the receiving and sending module is used for receiving signals from a sending end, wherein the signals comprise actual receiving signals of sample signals and service signals;

a processing module for training a decision feedback equalizer based on the actual received signal and the sample signal; in a training process, determining an adjustment coefficient of a parameter of the decision feedback equalizer according to a first variation trend of an error between a processing result of the decision feedback equalizer on the actual received signal and the sample signal and a first corresponding relationship, where the first corresponding relationship includes multiple variation trends and corresponding relationships among multiple adjustment coefficients, and the multiple variation trends include the first variation trend; adjusting parameters of the decision feedback equalizer based on the adjustment coefficient, and continuing training the decision feedback equalizer; and carrying out interference elimination processing on the service signal by using the trained decision feedback equalizer.

8. The apparatus of claim 7, wherein the processing module is further configured to:

inputting the actual received signal to the decision feedback equalizer to obtain a processing result of the actual received signal;

determining an error between a result of the processing of the actual received signal and the sample signal;

based on the error, the first trend of change is determined.

9. The apparatus of claim 8, wherein the processing module is specifically configured to:

when the number of the errors is accumulated to a first preset number, carrying out mean value filtering processing on the errors of the first preset number to obtain a plurality of smooth errors;

determining a local variation trend of each two adjacent smooth errors in the plurality of smooth errors according to a difference value between each two adjacent smooth errors in the plurality of smooth errors, wherein the local variation trend comprises an ascending trend and a descending trend;

and determining the first change trend according to the number of the ascending trends and the number of the descending trends.

10. The apparatus according to claim 9, wherein the processing module is specifically configured to:

if the number of the ascending trends is larger than the number of the descending trends, determining the first change trend as an ascending trend; or,

if the number of the ascending trends is equal to the number of the descending trends, determining the first change trend as a stable trend; or,

and if the number of the ascending trends is smaller than the number of the descending trends, determining the first changing trend as a descending trend.

11. The apparatus according to claim 9 or 10, wherein the processing module is specifically configured to:

and when the quantity of the plurality of smoothing errors is accumulated to a second preset quantity, determining the first change trend according to the quantity of the ascending trends and the quantity of the descending trends.

12. The apparatus according to any one of claims 7 to 10, wherein the first trend is obtained based on a first partial signal in the actual received signal;

the processing module is further configured to:

determining a second variation trend of the error between the processing result of the adjusted decision feedback equalizer on the second part of signals in the actual received signals and the sample signals;

determining a readjustment coefficient of the parameter of the decision feedback equalizer based on the second variation trend and the first corresponding relation;

and adjusting the parameters of the decision feedback equalizer again based on the readjustment coefficient, and continuing to train the decision feedback equalizer.

13. A signal transmission apparatus, comprising: a processor coupled with a memory for storing a computer program that, when invoked by the processor, causes the apparatus to perform the method of any of claims 1 to 6.

14. A computer-readable storage medium for storing a computer program comprising instructions for implementing the method of any one of claims 1 to 6.

15. A computer program product, characterized in that computer program code is included in the computer program product, which, when run on a computer, causes the computer to implement the method according to any one of claims 1 to 6.

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