CN114666800A - Signal demodulation method, device, baseband chip, terminal equipment and storage medium - Google Patents

Abstract

The embodiments of the present application disclose a signal demodulation method, an apparatus, a baseband chip, a terminal device and a storage medium, which belong to the technical field of communications. The method includes: performing channel estimation and noise estimation on a received signal to obtain a channel matrix and an initial noise matrix; determining a noise matrix adjustment factor based on the channel matrix; adjusting the initial noise matrix based on the noise matrix adjustment factor to obtain Target noise matrix; based on the channel matrix and the target noise matrix, perform signal demodulation on the received signal through a target demodulation algorithm to obtain a signal demodulation result. In the process of determining the noise matrix adjustment factor, the baseband chip selects the channel matrix as the adjustment basis, which can change the noise matrix adjustment factor correspondingly in different channel environments, thereby enhancing the stability of the matrix inversion in the target demodulation algorithm, thereby improving the signal Demodulation performance and demodulation quality.

Description

Signal demodulation method, device, baseband chip, terminal equipment and storage medium

technical field

The embodiments of the present application relate to the field of communication technologies, and in particular, to a signal demodulation method, apparatus, baseband chip, terminal device, and storage medium.

Background technique

In a wireless communication system, a terminal needs to demodulate a received signal through a signal demodulation algorithm to obtain modulated information in the received signal.

During the signal demodulation process, the stability of the signal demodulation algorithm will directly affect the demodulation performance and demodulation quality.

SUMMARY OF THE INVENTION

Embodiments of the present application provide a signal demodulation method, apparatus, baseband chip, terminal device, and storage medium. The technical solution is as follows:

On the one hand, the embodiment of the present application provides a signal demodulation method, the method includes:

Perform channel estimation and noise estimation on the received signal to obtain a channel matrix and an initial noise matrix;

determining a noise matrix adjustment factor based on the channel matrix;

Adjusting the initial noise matrix based on the noise matrix adjustment factor to obtain a target noise matrix;

Based on the channel matrix and the target noise matrix, the received signal is demodulated by a target demodulation algorithm to obtain a signal demodulation result. The target demodulation algorithm involves a matrix inversion operation of the target matrix, The target matrix is determined based on the channel matrix and the target noise matrix.

On the other hand, an embodiment of the present application provides a signal demodulation apparatus, and the apparatus includes:

The estimation module is used for channel estimation and noise estimation on the received signal to obtain a channel matrix and an initial noise matrix;

an adjustment module, configured to determine a noise matrix adjustment factor based on the channel matrix;

The adjustment module is further configured to adjust the initial noise matrix based on the noise matrix adjustment factor to obtain a target noise matrix;

The demodulation module is configured to perform signal demodulation on the received signal through a target demodulation algorithm based on the channel matrix and the target noise matrix to obtain a signal demodulation result, wherein the target demodulation algorithm involves the target matrix The target matrix is determined based on the channel matrix and the target noise matrix.

On the other hand, an embodiment of the present application provides a baseband chip, where the baseband chip includes a programmable logic circuit and/or program instructions, and when the baseband chip is running, is used to implement the signal demodulation as described in the above aspects method.

On the other hand, an embodiment of the present application provides a terminal device, where the terminal device is provided with the baseband chip according to the above aspect.

On the other hand, an embodiment of the present application provides a computer-readable storage medium, where the storage medium stores at least one piece of program, and the at least one piece of program is used to be executed by a processor to implement the signal demodulation according to the above aspect method.

On the other hand, an embodiment of the present application provides a computer program product, where the computer program product includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the signal demodulation method provided by the above aspects.

In the embodiment of the present application, the baseband chip obtains the channel matrix and the initial noise matrix by performing channel estimation and noise estimation on the received signal, and determines the noise matrix adjustment factor according to the channel matrix, and adjusts the initial noise matrix with the noise matrix adjustment factor to obtain The target noise matrix, so as to realize the signal demodulation of the received signal through the target demodulation algorithm according to the channel matrix and the target noise matrix. In the process of determining the noise matrix adjustment factor, the baseband chip uses the channel matrix as the adjustment basis, which can dynamically change the noise matrix adjustment factor in different channel environments, thereby enhancing the stability of the matrix inversion in the target demodulation algorithm, thereby improving the signal Demodulation performance and demodulation quality.

Description of drawings

FIG. 1 is a schematic diagram of a system architecture shown in an exemplary embodiment of the present application;

FIG. 2 shows a flowchart of a signal demodulation method provided by an exemplary embodiment of the present application;

FIG. 3 shows a flowchart of a signal demodulation method provided by another exemplary embodiment of the present application;

FIG. 4 is a schematic diagram of the implementation of a signal demodulation process shown in an exemplary embodiment of the present application;

5 is a structural block diagram of an apparatus for signal demodulation provided by an exemplary embodiment of the present application;

FIG. 6 is a structural block diagram of a terminal device according to an exemplary embodiment of the present application.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

As used herein, "plurality" refers to two or more. "And/or", which describes the association relationship of the associated objects, means that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, A and B exist at the same time, and B exists alone. The character "/" generally indicates that the associated objects are an "or" relationship.

In the communication system, the transmission of the signal usually needs to go through the process of modulation and demodulation, and the demodulation algorithm can be used to restore the original signal at the receiving end of the communication system. In the related art, most of the demodulation algorithms are applied to the operation of the matrix.

The demodulation algorithm is described below by taking an example of demodulation based on the Multiple Input Multiple Output (Multiple Input Multiple Output, MIMO) system applying the Minimum Mean Squared Error (Minimum Mean Squared Error, MMSE) theory.

Illustratively, a typical model of a MIMO system is y=Hx+n, where y represents a received signal column vector of length _NRx , _NRx represents the number of receiving antennas; H represents a channel matrix of size _NRx *N _Tx ; x represents the transmitted signal column vector of length N _Tx , N _Tx represents the number of transmitting antennas; n represents the noise column vector of length N _Rx .

The MMSE-based demodulation scheme can be expressed as:

Among them, A=HH ^H , R _nn =E{nn ^H } represents the correlation matrix of the noise vector.

When there is no correlation between the elements of n, the baseband chip usually performs noise whitening processing on the received signal, that is, performing diagonal processing on the correlation matrix of the noise vector, and simplifies it to obtain R _nn =σ ² I, where σ ² is the noise power possessed by a single antenna.

When N _Rx < N _Tx , the matrix A is a positive semi-definite matrix of N _Rx *N _Rx . When N _Rx > N _Tx , the demodulation scheme is deformed to obtain:

Among them, B=H ^H H, and the matrix B is a positive semi-definite matrix of N _Tx *N _Tx . By modifying the demodulation scheme, the complexity of matrix inversion can be effectively reduced when N _Rx > N _Tx .

However, according to the properties of matrix inversion, the above-mentioned demodulation scheme is unstable when the semi-positive definite matrix A or B is close to singular and the signal-to-noise ratio is high.

In order to improve the above-mentioned demodulation scheme, in the related art, the stability of the matrix inversion process is enhanced by adjusting the diagonal elements of the correlation matrix R _nn of the noise vector. The adjustment method is generally to enlarge the diagonal elements or add a smaller value to the diagonal elements. The demodulation scheme is adjusted as:

Among them, α>1 is a multiplicative amplification factor; Δ>0 is an additive incremental factor. Since α and Δ, as noise matrix adjustment factors, are both empirical values generated according to practical applications, they are fixed values. Therefore, in the case of high signal-to-noise ratio, the noise power σ ² itself is regarded as a small value, even if Correspondingly amplifying or increasing it cannot achieve an effective adjustment to the noise matrix, so that the stability of the matrix inversion cannot be improved.

In the embodiment of the present application, the baseband chip obtains the channel matrix and the initial noise matrix by performing channel estimation and noise estimation on the received signal, and determines the noise matrix adjustment factor according to the channel matrix, and adjusts the initial noise matrix with the noise matrix adjustment factor to obtain The target noise matrix, so as to realize the signal demodulation of the received signal through the target demodulation algorithm according to the channel matrix and the target noise matrix. In the process of determining the noise matrix adjustment factor, the baseband chip uses the channel matrix as the adjustment basis, which can dynamically change the noise matrix adjustment factor in different channel environments, thereby enhancing the stability of the matrix inversion in the target demodulation algorithm, thereby improving the signal Demodulation performance and demodulation quality.

Please refer to FIG. 1 , which shows a schematic diagram of a system architecture provided by an exemplary embodiment of the present application. The system architecture may include: a terminal device 10 and a network device 20 .

The number of terminal devices 10 is usually multiple, and one or more terminal devices 10 may be distributed in a cell managed by each network device 20 . The terminal device 10 may include various handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to wireless modems with wireless communication functions, as well as various forms of user equipment (UE), mobile stations (Mobile Station, MS) and so on. For convenience of description, in the embodiments of the present application, the devices mentioned above are collectively referred to as terminal devices.

The network device 20 is a device deployed in an access network to provide a wireless communication function for the terminal device 10 . The network device 20 may include various forms of macro base stations, micro base stations, relay stations, access points, and the like. In systems using different radio access technologies, the names of devices with network device functions may be different. For example, in Long Time Evolution (LTE) systems, they are called eNodeBs or eNBs; in 5G In the NR system, it is called gNodeB or gNB. As communications technology evolves, the name "network equipment" may change. For convenience of description, in the embodiments of the present application, the above-mentioned apparatuses for providing a wireless communication function for the terminal device 10 are collectively referred to as network devices.

In this embodiment of the present application, both the terminal device 10 and the network device 20 support the MIMO function, that is, the communication system is a MIMO system. In order to realize the MIMO function, both the terminal device 10 and the network device 20 are provided with multiple antennas, so as to form a multi-channel antenna system during the sending and receiving process.

Please refer to FIG. 2 , which shows a flowchart of a signal demodulation method provided by an exemplary embodiment of the present application. This embodiment is described by taking the method used for a baseband chip as an example, and the method may include the following steps:

Step 201: Perform channel estimation and noise estimation on the received signal to obtain a channel matrix and an initial noise matrix.

During the transmission of the signal through the channel, the signal will be distorted or added with noise. In order to determine the characteristics of the channel that the signal passes through during the transmission process, the baseband chip needs to perform channel estimation.

Regarding the specific manner of channel estimation, in some embodiments, in the case of known transmitted signals, a mathematical model is set so that the transmitted signals are correlated with the received signals. The mathematical model is a channel matrix composed of channel coefficients, and the channel coefficients are channel gains.

Further, in order to determine the characteristics of the added noise in the signal, the baseband chip needs to perform noise estimation.

Regarding the specific manner of noise estimation, in some embodiments, an initial noise matrix is constructed with a corresponding noise coefficient according to a known initial noise signal. The embodiments of the present application do not limit the specific manners of channel estimation and noise estimation.

Step 202, determining a noise matrix adjustment factor based on the channel matrix.

Different from the related art, the noise matrix adjustment factor is determined by empirical value. In this embodiment, the baseband chip determines the noise matrix adjustment factor based on the channel matrix.

The baseband chip obtains different channel matrices through channel estimation according to the channel transmission environment of different communication systems. Further, the baseband chip correspondingly determines the noise matrix adjustment factor according to the channel matrix.

Illustratively, the baseband chip determines the noise matrix adjustment factor according to special position elements of the channel matrix, such as diagonal elements; or the baseband chip first performs corresponding matrix operation processing on the channel matrix, and then determines the noise according to the special position elements of the obtained matrix. Matrix adjustment factor.

Step 203: Adjust the initial noise matrix based on the noise matrix adjustment factor to obtain the target noise matrix.

According to the rules of matrix operation, the baseband chip adjusts the initial noise matrix based on the noise matrix adjustment factor, and the noise matrix adjustment factor needs to be processed accordingly. For example, according to the properties of the initial noise matrix, a matrix related to the noise matrix adjustment factor is constructed, thus Using matrix operations, the target noise matrix is obtained.

Step 204, based on the channel matrix and the target noise matrix, perform signal demodulation on the received signal through the target demodulation algorithm to obtain a signal demodulation result. The target demodulation algorithm involves a matrix inversion operation of the target matrix, and the target matrix is based on the channel matrix. and the target noise matrix are determined.

The baseband chip constructs the target matrix in the target demodulation algorithm based on the channel matrix and the target noise matrix, so as to demodulate the received signal through the target demodulation algorithm, and obtain the signal demodulation result.

In a possible implementation manner, a matrix inversion operation exists in the target demodulation algorithm for performing signal demodulation on the received signal, such as the MMSE algorithm or other improved algorithms based on MMSE. Of course, any demodulation algorithm involving a matrix inversion operation can be regarded as a target demodulation algorithm, which is not limited in this embodiment of the present application.

It should be noted that, for the convenience of description, the following embodiments take the target demodulation algorithm as an MMSE algorithm as an example for schematic illustration, but the specific demodulation algorithm is not limited.

To sum up, in the embodiment of the present application, the baseband chip obtains the channel matrix and the initial noise matrix by performing channel estimation and noise estimation on the received signal, and determines the noise matrix adjustment factor according to the channel matrix, and uses the noise matrix adjustment factor to adjust the initial noise. The matrix is adjusted to obtain the target noise matrix, so as to realize the signal demodulation of the received signal through the target demodulation algorithm according to the channel matrix and the target noise matrix. In the process of determining the noise matrix adjustment factor, the baseband chip uses the channel matrix as the adjustment basis, which can dynamically change the noise matrix adjustment factor in different channel environments, thereby enhancing the stability of the matrix inversion in the target demodulation algorithm, thereby improving the signal Demodulation performance and demodulation quality.

Please refer to FIG. 3 , which shows a flowchart of a signal demodulation method provided by another exemplary embodiment of the present application. This embodiment is described by taking the method used in a baseband chip and performing signal demodulation based on the MMSE algorithm as an example. The method may include the following steps:

Step 301: Perform channel estimation and noise estimation on the received signal to obtain a channel matrix and an initial noise matrix.

For the implementation of this step, reference may be made to the foregoing step 201, which is not repeated in this embodiment.

Step 302: Determine a positive semi-definite matrix based on the product of the channel matrix and the conjugate transposed matrix corresponding to the channel matrix.

The baseband chip obtains a channel matrix through channel estimation according to the characteristics of the channel, wherein the number of rows of the channel matrix is the number of receiving antennas, and the number of columns is the number of transmitting antennas.

The baseband chip determines the positive semi-definite matrix based on the product of the channel matrix and the corresponding conjugate transposed matrix of the channel matrix, and the obtained positive semi-definite matrix is a square matrix, that is, the number of rows and columns of the matrix are equal.

Illustratively, H represents a channel matrix of size N _Rx *N _Tx , and the channel matrix is multiplied by its corresponding conjugate transposed matrix to obtain A=HH ^H , where the positive semi-definite matrix A is an N _Rx order matrix.

Step 303: Determine the reference value of the adjustment factor based on the diagonal elements of the positive semi-definite matrix.

Usually, when the elements of the noise column vector have no correlation, the baseband chip performs noise whitening processing on the received signal, and the obtained initial noise matrix is the product of the noise power and the identity matrix. Therefore, the baseband chip determines the adjustment factor reference value δ based on the diagonal elements of the positive semi-definite matrix, so as to adjust the initial noise matrix.

In a possible implementation, the baseband chip determines the maximum value of the diagonal elements of the positive semi-definite matrix as the adjustment factor reference value.

Illustratively, the semi-positive definite matrix is:

The baseband chip takes the maximum value of the diagonal elements A _nn of the semi-positive definite matrix A as the adjustment factor reference value δ:

δ=MAX(A ₁₁ ,...,A _nn )

In another possible implementation, the baseband chip determines the average value of the diagonal elements of the positive semi-definite matrix as the adjustment factor reference value.

Illustratively, the baseband chip takes the average value of all diagonal elements A _nn as the adjustment factor reference value δ:

Certainly, in other possible implementation manners, the baseband chip may determine the median value of the diagonal elements as the reference value of the adjustment factor, which is not limited in this embodiment.

Step 304: Determine a reference value scaling factor corresponding to the adjustment factor reference value based on the signal-to-noise ratio of the communication system.

Since the reference value of the adjustment factor is directly determined according to the diagonal elements of the positive semi-definite matrix, in the case of high signal-to-noise ratio, the noise power itself is very small, and the value corresponding to the initial noise matrix is also very small, so the baseband chip directly uses the reference value of the adjustment factor Adjusting the initial noise matrix will cause the value of the initial noise matrix to expand exponentially. Therefore, the baseband chip needs to determine the reference value scaling factor β corresponding to the adjustment factor reference value based on the signal-to-noise ratio of the communication system.

In a possible implementation, the baseband chip determines the maximum signal-to-noise ratio that can be achieved by the communication system in which it is located; determines a reference value scaling factor based on the maximum signal-to-noise ratio, and the reference value scaling factor has a negative correlation with the maximum signal-to-noise ratio.

The signal-to-noise ratio that can be achieved by the communication system is the largest when the system communication quality is in an ideal state. At this time, the noise power is the smallest. Correspondingly, the reference value of the adjustment factor should be correspondingly reduced. Therefore, the baseband chip determines the reference value based on the maximum signal-to-noise ratio. The value of the scaling factor can achieve a better scaling effect.

Illustratively, the maximum SNR that the system can achieve is SNR _max dB, then the reference value scaling factor can be expressed as:

Among them, M is the protection margin, and the typical range is [0,6]dB.

However, in an actual communication system, there is a certain gap between the actual signal-to-noise ratio of the communication system and the maximum signal-to-noise ratio that can be achieved in an ideal state. Therefore, in order to further optimize the scaling effect on the reference value of the adjustment factor, the baseband chip may determine the reference value scaling factor based on the actual signal-to-noise ratio of the communication communication system.

In another possible implementation, the baseband chip determines the historical average signal-to-noise ratio of the communication system in which it is located; determines the reference value scaling factor based on the historical average signal-to-noise ratio, and the reference value scaling factor has a negative correlation with the historical average signal-to-noise ratio .

The baseband chip determines the reference value scaling factor according to the historical average signal-to-noise ratio of the communication system within a period of time, so that the reference value of the adjustment factor can be scaled according to the actual situation.

Illustratively, the maximum signal-to-noise ratio that can be achieved by the communication system where the baseband chip is located is 120 dB, and the historical average signal-to-noise ratio of the communication system within a period of time is 110 dB. When the baseband chip demodulates the signal in the actual communication system, the reference value scaling factor can be determined based on the historical average signal-to-noise ratio of 110dB, and the reference value of the adjustment factor can be scaled.

Step 305: Determine the noise matrix adjustment factor based on the adjustment factor reference value and the reference value scaling factor.

The baseband chip determines the product of the adjustment factor reference value δ and the reference value scaling factor β as the noise matrix adjustment factor βδ.

Step 306 , generate a noise adjustment matrix based on the noise matrix adjustment factor, and the noise adjustment matrix is a diagonal matrix.

Usually, the baseband chip performs noise whitening processing on the received signal, and the obtained initial noise matrix is the product of the noise power and the identity matrix, that is, a diagonal matrix.

Therefore, the baseband chip determines the noise adjustment matrix generated based on the noise matrix adjustment factor as a diagonal matrix, thereby effectively adjusting the initial noise matrix.

Illustratively, the baseband chip determines the product of the noise matrix adjustment factor and the unit matrix as the noise adjustment matrix, which can be expressed as βδ·I, where I represents the unit matrix.

Step 307: Perform matrix superposition on the noise adjustment matrix and the initial noise matrix to obtain a target noise matrix.

The baseband chip performs matrix superposition on the noise adjustment matrix and the initial noise matrix to obtain the target noise matrix.

Illustratively, the target noise matrix can be expressed as: βδ·I+R _nn , where R _nn is the initial noise matrix. After the baseband chip performs noise whitening processing on the received signal, the target noise matrix can be expressed as: βδ·I+σ ² I, where σ ² represents the noise power.

Step 308, based on the channel matrix and the target noise matrix, perform signal demodulation on the received signal through the target demodulation algorithm to obtain a signal demodulation result, the target demodulation algorithm involves the matrix inversion operation of the target matrix, and the target matrix is based on the channel matrix. and the target noise matrix are determined.

Illustratively, according to the target noise matrix, the process of signal demodulation through the target demodulation algorithm can be expressed as:

When the baseband chip performs noise whitening processing on the received signal, the process of signal demodulation through the target demodulation algorithm can be expressed as:

In the embodiment of the present application, the baseband chip determines the positive semi-definite matrix based on the product of the channel matrix and the corresponding conjugate transposed matrix of the channel matrix, so as to determine the reference value of the adjustment factor based on the diagonal elements of the positive semi-definite matrix. At the same time, the baseband chip scales the reference value of the adjustment factor correspondingly according to the signal-to-noise ratio of the communication system, thereby determining the noise adjustment matrix. In the process of determining the noise adjustment matrix, the baseband chip can determine the noise adjustment matrix more reasonably with reference to the channel matrix and the signal-to-noise ratio of the communication system, thereby ensuring the stability of the matrix inversion operation in the target demodulation algorithm.

In conjunction with the above embodiments, please refer to FIG. 4 , which shows a schematic diagram of the implementation of a signal demodulation process provided by an exemplary embodiment of the present application.

The baseband chip obtains the corresponding channel matrix 42 and the initial noise matrix 43 by performing channel estimation and noise estimation on the received signal 41, and determines the noise matrix adjustment factor 44 according to the channel matrix 42, thereby using the noise matrix adjustment factor 44 to adjust the initial noise matrix 43. Adjustments are made to obtain the target noise matrix 45 . Based on the channel matrix 42 and the target noise matrix 45 , the baseband chip demodulates the received signal 41 through the MMSE demodulation algorithm 46 to obtain a demodulation result 47 .

In the case of high signal-to-noise ratio, the initial noise matrix is based on the noise power, the value is small, and according to the properties of the singular matrix, the singular matrix is an irreversible matrix, and the larger the condition number of the matrix, the closer the matrix is to singularity, so in the In the target demodulation algorithm, the baseband chip needs to adjust the initial noise matrix to ensure the stability of the matrix inversion of the target matrix.

Therefore, in a possible implementation manner, when the signal-to-noise ratio of the communication system is higher than the signal-to-noise ratio threshold, and the semi-positive definite matrix is close to singular, the baseband chip determines the noise matrix adjustment factor based on the channel matrix, so that the subsequent basis based on The noise matrix adjustment factor adjusts the initial noise matrix to ensure that a stable matrix inversion operation can be achieved in the target demodulation algorithm.

Among them, the baseband chip determines the ratio of the largest eigenvalue to the smallest eigenvalue of the positive semi-definite matrix that can be stably inverted as the condition number threshold. When the condition number of the semi-positive definite matrix is greater than the condition number threshold, the baseband chip determines that the positive semi-definite matrix is close to bizarre.

However, in the medium and low signal-to-noise ratio environment (the initial noise matrix value is relatively large), or when the semi-positive definite matrix is not close to singular (is an invertible matrix), the matrix inversion operation in the target demodulation algorithm is relatively stable, so in order to reduce the computational cost The baseband chip does not need to determine the noise matrix adjustment factor based on the channel matrix.

In a possible implementation, when the signal-to-noise ratio of the communication system in which it is located is lower than the signal-to-noise ratio threshold, or the semi-positive definite matrix is not close to singular, the baseband chip passes the target solution based on the channel matrix and the initial noise matrix. The modulation algorithm demodulates the received signal and obtains the signal demodulation result.

Illustratively, when the signal-to-noise ratio is higher than 70dB and the condition number of the semi-positive definite matrix is greater than the condition number threshold, the baseband chip determines the noise matrix adjustment factor based on the channel matrix, and adjusts the initial noise matrix to ensure the target Stability of matrix inversion in demodulation algorithms.

When the signal-to-noise ratio is lower than 70dB, or the condition number of the semi-positive definite matrix is less than the condition number threshold set by the system, the baseband chip directly performs signal demodulation on the received signal through the target demodulation algorithm based on the channel matrix and the initial noise matrix. tune.

In the above embodiment, the baseband chip preferentially determines whether to adjust the initial noise matrix according to the signal-to-noise ratio of the communication system where it is located and the singularity of the positive semi-definite matrix, and then performs signal demodulation on the received signal based on the target demodulation algorithm, which can In the case of ensuring the stability of the matrix inversion operation, the amount of calculation is reduced.

In a possible implementation, the baseband chip supports at least two demodulation algorithms (including target demodulation algorithms), and can select different demodulation algorithms for signal demodulation according to different channel environments. When the demodulation algorithm adopted is the target demodulation algorithm, the baseband chip determines the noise matrix adjustment factor based on the channel matrix. In the case where the demodulation algorithm used does not belong to the target demodulation algorithm, the baseband chip does not need to determine the noise matrix adjustment factor based on the channel matrix.

For example, the demodulation algorithms supported by the baseband chip include TS_idealMod, TS_RobustQAM and MMSE. When the MMSE algorithm is determined to be used based on the channel environment of the communication system, the baseband chip determines the noise matrix adjustment factor based on the channel matrix; when the TS_idealMod algorithm is determined to be used based on the channel environment of the communication system, the baseband chip does not need to determine the noise based on the channel matrix. Matrix adjustment factor.

Please refer to FIG. 5, which shows a structural block diagram of a signal demodulation apparatus provided by an exemplary embodiment of the present application. The apparatus may include the following structures:

an estimation module 501, configured to perform channel estimation and noise estimation on the received signal to obtain a channel matrix and an initial noise matrix;

an adjustment module 502, configured to determine a noise matrix adjustment factor based on the channel matrix;

The adjustment module 502 is further configured to adjust the initial noise matrix based on the noise matrix adjustment factor to obtain a target noise matrix;

The demodulation module 503 is configured to perform signal demodulation on the received signal through a target demodulation algorithm based on the channel matrix and the target noise matrix to obtain a signal demodulation result, wherein the target demodulation algorithm involves A matrix inversion operation of a matrix, where the target matrix is determined based on the channel matrix and the target noise matrix.

Optionally, the adjustment module 502 is used for:

determining a positive semi-definite matrix based on the product of the channel matrix and the corresponding conjugate transpose matrix of the channel matrix;

determining an adjustment factor reference value based on the diagonal elements of the positive semi-definite matrix;

determining a reference value scaling factor corresponding to the adjustment factor reference value based on the signal-to-noise ratio of the communication system in which it is located;

The noise matrix adjustment factor is determined based on the adjustment factor reference value and the reference value scaling factor.

Optionally, the adjustment module 502 is used for:

determining the maximum value of the diagonal elements of the positive semi-definite matrix as the adjustment factor reference value;

or,

An average value of diagonal elements of the positive semi-definite matrix is determined as the adjustment factor reference value.

Optionally, the adjustment module 502 is used for:

determining the maximum signal-to-noise ratio that can be achieved by the communication system in which it is located; determining the reference value scaling factor based on the maximum signal-to-noise ratio, where the reference value scaling factor is negatively correlated with the maximum signal-to-noise ratio;

or,

determining the historical average signal-to-noise ratio of the communication system; determining the reference value scaling factor based on the historical average signal-to-noise ratio, where the reference value scaling factor has a negative correlation with the historical average signal-to-noise ratio.

Optionally, the adjustment module 502 is used for:

generating a noise adjustment matrix based on the noise matrix adjustment factor, where the noise adjustment matrix is a diagonal matrix;

Perform matrix superposition on the noise adjustment matrix and the initial noise matrix to obtain the target noise matrix.

Optionally, the adjustment module 502 is used for:

In the case where the signal-to-noise ratio of the communication system is higher than the signal-to-noise ratio threshold, and the positive semi-definite matrix is close to singular, the noise matrix adjustment factor is determined based on the channel matrix, where the positive semi-definite matrix is the channel matrix and the The channel matrix corresponds to the product of the conjugate transpose matrix.

Optionally, the demodulation module 503 is further configured to:

When the signal-to-noise ratio of the communication system in which it is located is lower than the signal-to-noise ratio threshold, or the positive semi-definite matrix is not close to singular, the target demodulation is performed based on the channel matrix and the initial noise matrix The algorithm performs signal demodulation on the received signal to obtain a signal demodulation result.

Optionally, the baseband chip supports at least two demodulation algorithms;

The adjustment module 502 is used for:

When the demodulation algorithm used is the target demodulation algorithm, the noise matrix adjustment factor is determined based on the channel matrix.

Optionally, the target demodulation algorithm is an MMSE algorithm.

To sum up, in the embodiment of the present application, the baseband chip obtains the channel matrix and the initial noise matrix by performing channel estimation and noise estimation on the received signal, and determines the noise matrix adjustment factor according to the channel matrix, and uses the noise matrix adjustment factor to adjust the initial noise. The matrix is adjusted to obtain the target noise matrix, so as to realize the signal demodulation of the received signal through the target demodulation algorithm according to the channel matrix and the target noise matrix. In the process of determining the noise matrix adjustment factor, the baseband chip uses the channel matrix as the adjustment basis, which can dynamically change the noise matrix adjustment factor in different channel environments, thereby enhancing the stability of the matrix inversion in the target demodulation algorithm, thereby improving the signal Demodulation performance and demodulation quality.

Please refer to FIG. 6 , which shows a structural block diagram of a terminal device provided by an exemplary embodiment of the present application. The terminal device in this application may include one or more of the following components: a processor 1210 and a memory 1220 .

Optionally, the processor 1210 uses various interfaces and lines to connect various parts of the entire terminal device, and by running or executing the instructions, programs, code sets or instruction sets stored in the memory 1220, and calling the Data, perform various functions of terminal equipment and process data. Optionally, the processor 1210 may employ at least one of a digital signal processing (Digital Signal Processing, DSP), a field-programmable gate array (Field-Programmable Gate Array, FPGA), and a programmable logic array (Programmable Logic Array, PLA). implemented in hardware. The processor 1210 may integrate one or more of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), a neural-network processing unit (Neural-network Processing Unit, NPU), a baseband chip, and the like. combination of species. Among them, the CPU mainly handles the operating system, user interface and application programs; the GPU is used to render and draw the content that needs to be displayed on the touch screen; the NPU is used to implement artificial intelligence (AI) functions; the baseband chip is used to Handle wireless communications. It can be understood that, the above-mentioned baseband chip may not be integrated into the processor 1210, and is implemented by a single chip.

The memory 1220 may include random access memory (Random Access Memory, RAM), or may include read-only memory (Read-Only Memory, ROM). Optionally, the memory 1220 includes a non-transitory computer-readable storage medium. Memory 1220 may be used to store instructions, programs, codes, sets of codes, or sets of instructions. The memory 1220 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), Instructions and the like used to implement the following method embodiments; the storage data area may store data (such as audio data, phone book) and the like created according to the use of the terminal device.

In addition, those skilled in the art can understand that the structure of the terminal device shown in the above drawings does not constitute a limitation on the terminal device, and the terminal device may include more or less components than those shown in the drawings, or a combination of certain components may be included. some components, or a different arrangement of components. For example, the terminal device further includes components such as a display component, an input unit, a sensor, an audio circuit, a speaker, a microphone, and a power supply, which will not be repeated in this embodiment.

Embodiments of the present application further provide a baseband chip, where the baseband chip includes a programmable logic circuit and/or program instructions, which are used to implement the signal demodulation method provided by the above embodiments when the baseband chip is running.

Embodiments of the present application further provide a computer-readable storage medium, where the storage medium stores at least one program, and at least one program is used to be executed by a processor to implement the signal demodulation method described in the foregoing embodiments.

Embodiments of the present application provide a computer program product, where the computer program product includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the signal demodulation method provided by the above embodiments.

Those skilled in the art should realize that, in one or more of the above examples, the functions described in the embodiments of the present application may be implemented by hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.

The above descriptions are only optional embodiments of the present application, and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection of the present application. within the range.

Claims (14)

1. A signal demodulation method, characterized in that the method comprises: Perform channel estimation and noise estimation on the received signal to obtain a channel matrix and an initial noise matrix; determining a noise matrix adjustment factor based on the channel matrix; Adjusting the initial noise matrix based on the noise matrix adjustment factor to obtain a target noise matrix; Based on the channel matrix and the target noise matrix, the received signal is demodulated by a target demodulation algorithm to obtain a signal demodulation result. The target demodulation algorithm involves a matrix inversion operation of the target matrix, The target matrix is determined based on the channel matrix and the target noise matrix. 2. The method according to claim 1, wherein the determining a noise matrix adjustment factor based on the channel matrix comprises: determining a positive semi-definite matrix based on the product of the channel matrix and the corresponding conjugate transpose matrix of the channel matrix; determining an adjustment factor reference value based on the diagonal elements of the positive semi-definite matrix; determining a reference value scaling factor corresponding to the adjustment factor reference value based on the signal-to-noise ratio of the communication system in which it is located; The noise matrix adjustment factor is determined based on the adjustment factor reference value and the reference value scaling factor. 3. The method according to claim 2, wherein, determining the reference value of the adjustment factor based on the diagonal elements of the positive semi-definite matrix, comprising: determining the maximum value of the diagonal elements of the positive semi-definite matrix as the adjustment factor reference value; or, An average value of diagonal elements of the positive semi-definite matrix is determined as the adjustment factor reference value. 4 . The method according to claim 2 , wherein the determining the reference value scaling factor corresponding to the adjustment factor reference value based on the signal-to-noise ratio of the communication system in which it is located comprises: 4 . determining the maximum signal-to-noise ratio that can be achieved by the communication system in which it is located; determining the reference value scaling factor based on the maximum signal-to-noise ratio, where the reference value scaling factor has a negative correlation with the maximum signal-to-noise ratio; or, determining the historical average signal-to-noise ratio of the communication system; determining the reference value scaling factor based on the historical average signal-to-noise ratio, where the reference value scaling factor has a negative correlation with the historical average signal-to-noise ratio. 5. The method according to any one of claims 1 to 4, wherein the adjusting the initial noise matrix based on the noise matrix adjustment factor to obtain a target noise matrix comprises: generating a noise adjustment matrix based on the noise matrix adjustment factor, where the noise adjustment matrix is a diagonal matrix; Perform matrix superposition on the noise adjustment matrix and the initial noise matrix to obtain the target noise matrix. 6. The method according to any one of claims 1 to 4, wherein the determining a noise matrix adjustment factor based on the channel matrix comprises: In the case where the signal-to-noise ratio of the communication system is higher than the signal-to-noise ratio threshold, and the positive semi-definite matrix is close to singular, the noise matrix adjustment factor is determined based on the channel matrix, where the positive semi-definite matrix is the channel matrix and the The channel matrix corresponds to the product of the conjugate transpose matrix. 7. The method according to claim 6, wherein the method further comprises: When the signal-to-noise ratio of the communication system in which it is located is lower than the signal-to-noise ratio threshold, or the positive semi-definite matrix is not close to singular, the target demodulation is performed based on the channel matrix and the initial noise matrix The algorithm performs signal demodulation on the received signal to obtain a signal demodulation result. 8. The method according to any one of claims 1 to 4, wherein the baseband chip supports at least two demodulation algorithms; The determining the noise matrix adjustment factor based on the channel matrix includes: When the demodulation algorithm used is the target demodulation algorithm, the noise matrix adjustment factor is determined based on the channel matrix. 9. The method according to any one of claims 1 to 4, wherein the target demodulation algorithm is a minimum mean square error MMSE algorithm. 10. A signal demodulation device, characterized in that the device comprises: The estimation module is used for channel estimation and noise estimation on the received signal to obtain a channel matrix and an initial noise matrix; an adjustment module, configured to determine a noise matrix adjustment factor based on the channel matrix; The adjustment module is further configured to adjust the initial noise matrix based on the noise matrix adjustment factor to obtain a target noise matrix; The demodulation module is configured to perform signal demodulation on the received signal through a target demodulation algorithm based on the channel matrix and the target noise matrix to obtain a signal demodulation result, wherein the target demodulation algorithm involves the target matrix The target matrix is determined based on the channel matrix and the target noise matrix. 11. A baseband chip, characterized in that the baseband chip comprises a programmable logic circuit and/or program instructions, when the baseband chip is running, for implementing the signal solution according to any one of claims 1 to 9. tuning method. 12 . A terminal device, characterized in that, the terminal device is provided with the baseband chip according to claim 11 . 13. A computer-readable storage medium, characterized in that, the storage medium stores at least one section of program, and the at least one section of program is used to be executed by a processor to realize the signal solution according to any one of claims 1 to 9. tuning method. 14. A computer program product, characterized in that the computer program product comprises computer instructions, and the computer instructions are stored in a computer-readable storage medium; and a processor of a computer device reads the data from the computer-readable storage medium. The computer instructions are executed by the processor, so that the computer device implements the signal demodulation method according to any one of claims 1 to 9.

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