CN114125884B - Uplink capacity optimization method, device, network node and storage medium - Google Patents
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Abstract
本申请公开了一种上行容量优化方法、装置、网络节点及存储介质。其中，方法包括：采用第一上行容量优化模型对第一目标函数进行优化，得到关于所述第一目标函数的最优解；所述第一目标函数的函数值表征所述网络的上行容量；所述第一目标函数以接入所述网络的至少一个第一终端中每个第一终端的发射功率为变量；为所述至少一个第一终端中的每个第一终端的发射功率配置对应的第一功率值；其中，所述第一上行容量优化模型表征基于布谷鸟算法得到的上行容量优化模型；所述第一功率值表征所述第一目标函数取最优解时对应终端的发射功率。
The present application discloses an uplink capacity optimization method, device, network node and storage medium. The method includes: optimizing a first objective function using a first uplink capacity optimization model to obtain an optimal solution for the first objective function; the function value of the first objective function represents the uplink capacity of the network; the first objective function uses the transmission power of each first terminal in at least one first terminal accessing the network as a variable; configuring a corresponding first power value for the transmission power of each first terminal in the at least one first terminal; wherein the first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a method and apparatus for uplink capacity optimization, a network node, and a storage medium.
Background
The sixth generation mobile communication technology (6G,6th Generation mobile networks) will construct a ubiquitous (land, sea, air, sky) and unconnected (man, machine, thing connected) network. That is, the network structure of the 6G communication system will vary greatly, and particularly when the number of antenna arrays approaches or exceeds the number of users, the network structure of the cellular network will fail, and thus, the cellular network becomes a candidate networking technology for 6G.
When the uplink capacity of the network is optimized, the related technology realizes local optimization of the uplink capacity of the network in the coverage area of the base station, and cannot be applied to the network structure of the cellular network.
Disclosure of Invention
In order to solve the related technical problems, the embodiment of the application provides an uplink capacity optimization method, an uplink capacity optimization device, a network node and a storage medium.
The embodiment of the application provides an uplink capacity optimization method which is applied to a first network node and comprises the following steps:
Optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function, wherein the function value of the first objective function characterizes the uplink capacity of the network;
Configuring a corresponding first power value for the transmission power of each of the at least one first terminal, wherein,
The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm, and the first power value represents the transmitting power of a corresponding terminal when the first objective function takes an optimal solution.
Wherein in an embodiment the first objective function further comprises a first penalty function and a second penalty function, wherein,
The function value of the first penalty function represents the sum of the transmitting power of all second terminals in the at least one first terminal, the function value of the second penalty function represents the number of all second terminals in the at least one first terminal, and the second terminal is a terminal with the transmitting power which is larger than or equal to the rated maximum transmitting power and corresponds to the at least one first terminal.
In an embodiment, before the optimizing the first objective function by using the first uplink capacity optimization model to obtain an optimal solution for the first objective function, the method further includes:
determining a first weight value corresponding to the first penalty function, and determining a second weight value corresponding to the second penalty function.
In an embodiment, the signal quality parameter of each of the at least one first terminal is greater than or equal to a first set threshold.
In an embodiment, the signal quality parameter comprises one of:
Reference signal received power;
reference signal reception quality;
Signal to noise ratio.
In an embodiment, when the first objective function is optimized by using the first uplink capacity optimization model to obtain an optimal solution for the first objective function, the method includes:
And sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to the sorting result.
In an embodiment, the pareto optimal solution algorithm is used to rank all solutions based on the fitness of each solution in all solutions, and when determining the optimal solution according to the ranking result, the method includes:
and determining the fitness of the corresponding solution based on the sequence number of the layer where the current solution is located and the Euclidean distance of the signal to noise ratio between any two first terminals.
The embodiment of the application also provides an uplink capacity optimization device, which comprises:
the system comprises a calculation unit, a first objective function, a first control unit and a second objective function, wherein the calculation unit is used for optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function;
A configuration unit, configured to configure a corresponding first power value for a transmission power of each of the at least one first terminal,
The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm, and the first power value represents the transmitting power of a corresponding terminal when the first objective function takes an optimal solution.
The embodiment of the application also provides a network node which comprises a first processor and a first communication interface, wherein,
The first processor is configured to:
Optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function, wherein the function value of the first objective function characterizes the uplink capacity of the network;
Configuring a corresponding first power value for the transmission power of each of the at least one first terminal, wherein,
The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm, and the first power value represents the transmitting power of a corresponding terminal when the first objective function takes an optimal solution.
The embodiment of the application also provides a network node comprising a first processor and a first memory for storing a computer program capable of running on the processor,
And the first processor is used for executing any step of the uplink capacity optimization method when running the computer program.
The embodiment of the application also provides a storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of any of the above-mentioned uplink capacity optimization methods.
The uplink capacity optimization method, device, network node and storage medium provided by the embodiment of the application are characterized in that the network node optimizes a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function, and configures a corresponding first power value for the transmitting power of each first terminal in at least one first terminal, wherein the function value of the first objective function represents the uplink capacity of the network, the first objective function takes the transmitting power of each first terminal in at least one first terminal accessed into the network as a variable, the first uplink capacity optimization model represents the uplink capacity optimization model obtained based on a cuckoo algorithm, and the first power value represents the transmitting power of the corresponding terminal when the first objective function takes the optimal solution. The network uplink capacity is optimized integrally by using a cuckoo algorithm and taking a user as a center to obtain an optimization result about the transmitting power of each terminal, so that the global optimization of the network uplink capacity is realized, and the network structure is suitable for a large-scale network structure of a cellular network.
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FIG. 1 is a schematic flow chart of an uplink capacity optimization method according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating an overall structure of a cellular network to which an embodiment of the present application is applied;
FIG. 3 is a flow chart of an uplink capacity optimization method according to another embodiment of the present application;
FIG. 4 is a schematic diagram of an uplink capacity optimization device according to an embodiment of the present application;
Fig. 5 is a schematic diagram of a first network node structure according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings and examples.
The uplink capacity of the network mainly depends on the distribution condition of the terminals in the network and the transmitting power of the terminals, and when the uplink capacity of the network is optimized, the related technology mainly realizes the local optimization of the uplink capacity of the network in the coverage area of the base station. In a 6G communication system, the network structure will vary greatly, and the cellular network becomes one of the candidate networking technologies of 6G, so that the scheme of locally optimizing the uplink capacity of the network will not be applicable to the network structure of the cellular network.
Based on the above, in various embodiments of the present application, global optimization of network uplink capacity is achieved, where a network node optimizes a first objective function by using a first uplink capacity optimization model to obtain an optimal solution for the first objective function, and configures a corresponding first power value for a transmission power of each first terminal in the at least one first terminal to achieve global optimization of network uplink capacity, where a function value of the first objective function characterizes uplink capacity of the network, the first objective function uses a transmission power of each first terminal in at least one first terminal accessed to the network as a variable, and the first uplink capacity optimization model characterizes an uplink capacity optimization model obtained based on a cuckoo algorithm, and the first power value characterizes a transmission power of a corresponding terminal when the first objective function takes the optimal solution.
The embodiment of the application provides an uplink capacity optimization method, which is applied to a network node, as shown in fig. 1, and comprises the following steps:
and 101, optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function.
The function value of the first objective function characterizes the uplink capacity of the network, and the first objective function takes the transmitting power of each first terminal in at least one first terminal accessed to the network as a variable.
And 102, configuring a corresponding first power value for the transmitting power of each first terminal in the at least one first terminal.
The first uplink capacity optimization model characterizes an uplink capacity optimization model obtained based on a cuckoo algorithm, and the first power value characterizes the transmitting power of a corresponding terminal when the first objective function takes an optimal solution. And based on the transmitting power of the corresponding terminal when the first objective function is the optimal solution, controlling the transmitting power of each terminal, thereby optimizing the uplink capacity of the network.
Here, the uplink capacity of the network is optimized as a whole, and in practical application, the uplink capacity of the network isWherein, P k represents the transmitting power of the kth terminal, K is less than or equal to K, the constraint condition is that 0<P k<Pmax,Pmax represents the rated maximum transmitting power of the terminal, P max corresponding to different terminals can be the same or different, and SINR k represents the uplink signal to noise ratio of the kth terminal. In the process of calculating the uplink capacity, assuming that the channel fading coefficient calculated by the pilot frequency remains unchanged, the main variable determining the uplink capacity is the transmission power of each terminal in the access network. Here, the uplink signal-to-noise ratio of the kth terminal is:
Wherein DS k represents a more desirable demodulation signal, BU k represents an error signal introduced due to non-ideal demodulation, IUI kk' represents an interference signal between terminals, and TN k represents a noise signal introduced after demodulation. For ease of understanding, the following describes how to determine the uplink signal-to-noise ratio of the kth terminal:
In connection with the overall architecture example of the cellular network of fig. 2, referring to fig. 2, the ue 1～UE4 characterizes the access network and distributes terminals in the network, and the AP 1～AP20 characterizes each antenna array in the network, it can be seen that the number of antenna arrays far exceeds the number of terminals. For a large-scale multiple-input multiple-output (MIMO, multiple Input Multiple Output) system for a cellular network, considering the signal combining gain formed when a plurality of antenna arrays that are not in the same geographic location simultaneously receive the terminal signal, it is assumed here that there is only one antenna for each antenna array, and then there are M antenna arrays and K terminals with randomly distributed locations. Defining the channel fading coefficient between the kth terminal and the mth antenna array as Wherein, beta represents large-scale fading, and h represents small-scale fading which obeys Gaussian distribution. To effectively estimate the uplink channel fading coefficients, a minimum mean square error method (MMSE, minimum Mean Squared Error) is used to estimate all uplink pilot signals. Assuming that the pilot signal length is tau,Wherein the method comprises the steps of Wherein, The pilot signal assigned to the kth terminal is characterized with a normalized energy of 1. Thus, the signal received by the mth antenna array isWherein the vector isCharacterizing white noise subject to gaussian distribution. Processing pilot signals by adopting a matched filtering method, wherein the signals received by the mth antenna array after processing are Wherein the method comprises the steps ofObtaining channel estimation as according to MMSE algorithmOn the basis of which analysis results in
Here, the uplink signal of the kth terminal is defined as: Where P k represents the transmit power, s k represents the uplink symbol, and |s k‖2 =1, then the signal received by the mth antenna array is: Where n m is white noise that follows a gaussian distribution. Processing the received signals by adopting a mode of maximum ratio combining to all the signals to obtain
Wherein Z is less than or equal to M, the signal representing that the Z antenna arrays receive the kth terminal is characterized in that u mk represents the maximum ratio combining coefficient of the kth terminal received by the mth antenna array, and if any one terminal is not limited to k, u k＝[u1k … uMk]T,‖uk‖2 =1, then the signal comprises
Uplink capacity based on networkIn practical application, the uplink capacity is optimized, and a first objective function corresponding to the uplink capacity optimization problem is as follows:
The corresponding constraint condition is 0<P k<Pmax, the function value of the first objective function is the uplink capacity of the characterization network, and the first objective function takes the transmitting power of each terminal in K terminals accessed to the network as a variable.
After the first objective function is determined, the first objective function is optimized by using the first uplink capacity optimization model, so that an optimal solution about the first objective function is obtained. Here, the first uplink capacity optimization model is obtained based on a cuckoo algorithm. Among these, the concept of Cuckoo Search is based on the bird's nest parasitic behavior and the bird's Levy flying behavior. The cuckoo algorithm has the characteristics of global optimization and high convergence rate. The nest parasitic behavior of cuckoo can be described as randomly selecting one nest at a time and producing one egg per cuckoo, the nest with the highest quality egg remains until the next generation, the number of selectable nests is fixed, and the probability of cuckoo eggs being found by the original nest master bird is pa e (0, 1). In this case, the original bird throws away or discards the egg of the cuckoo, assuming that the pa part of the n nests is replaced by a new nest (new solution) for simplicity. In the cuckoo algorithm, eggs in each nest represent one solution, cuckoo eggs represent a new solution, and the goal is to replace the inferior solution in the nest with the new solution or a potentially superior solution. In addition, levy flight is a typical random walk mechanism that represents a class of non-gaussian random processes, with smooth increments of levy flight following levy stable distributions. Since levy flight second moment diverges, the motion process always generates a large jump under the condition of small aggregation, so that the levy flight is introduced into an optimization technology and is a strategy for effectively avoiding the algorithm from being trapped into local optimum.
In the cuckoo algorithm, when cuckoo i generates a new solution x t+1, a levy flight is performed as shown in the following formula: Wherein alpha >0 is the step size, Representing the multiplication operator, the random walk step size of the levy flight obeys the levy distribution,Where u obeys a distribution with mean 0 and variance σ 2,V obeys a distribution with a mean of 0 and a variance of 1.
In combination with the first objective function, since the channel fading coefficient is relatively fixed, the problem of uplink capacity optimization only uses the transmitting power of each of the K terminals accessing the network as a variable, so that one egg in the nest of the cuckoo is simplified.
In the above, the first objective function has a constraint of 0<P k<Pmax, that is, the uplink capacity optimization problem is a constrained optimization problem. Here, we consider the use of a penalty function to convert the constrained optimization problem to an unconstrained one to improve the algorithm iteration efficiency in the uplink capacity optimization process.
Based on this, in an embodiment, the first objective function further comprises a first penalty function and a second penalty function, wherein,
The function value of the first penalty function represents the sum of the transmitting power of all second terminals in the at least one first terminal, the function value of the second penalty function represents the number of all second terminals in the at least one first terminal, and the second terminal is a terminal with the transmitting power which is larger than or equal to the rated maximum transmitting power and corresponds to the at least one first terminal.
Here, the first objective function is composed of a function characterizing the uplink capacity of the network, a first penalty function and a second penalty function, and when determining the first objective function, a penalty coefficient corresponding to each penalty function may be determined according to the actual deployment situation of the network.
Based on this, in an embodiment, before the optimizing the first objective function by using the first uplink capacity optimization model, the method further includes:
determining a first weight value corresponding to the first penalty function, and determining a second weight value corresponding to the second penalty function.
In practical application, the first objective function is set as
F(P)=f(P)+w1*(sum_viol)2+w2*num_viol
Wherein f (P) is based onThe determined function for representing the uplink capacity of the network corresponds to the constraint condition, sum_ viol represents the sum of the transmitting powers of all terminals violating the constraint condition, namely the sum of the transmitting powers of the terminals with transmitting powers greater than or equal to the rated maximum transmitting power, w 1 is a first weight value corresponding to a first punishment function sum_ viol, namely a punishment coefficient corresponding to the first punishment function sum_ viol, num_ viol represents the number of terminals violating the constraint condition, and w 2 is a second weight value corresponding to a second punishment function num_ viol, namely a punishment coefficient corresponding to the second punishment function num_ viol.
In practical application, the number of terminals accessing to the network is numerous, and the uplink capacity of the network is considered to be mainly dependent on the transmitting power of the terminals accessing to the network, so that the constraint condition related to the signal strength can be introduced in the uplink capacity optimization process, in the process that the cuckoo generates a new solution through levy flight, in order to further reduce the operation amount, improve the calculation efficiency, the terminals with the signal strength reaching a certain threshold participate in the uplink capacity optimization process, and the terminals with the signal strength not reaching the certain threshold do not participate in the uplink capacity optimization process.
Based on this, in an embodiment, the signal quality parameter of each of the at least one first terminal is greater than or equal to a first set threshold.
Here, the signal Quality parameter of the first terminal is greater than a first set threshold, and the signal Quality parameter includes, but is not limited to, a measured reference signal received Power (RSRP, reference Signal Receiving Power) of the terminal, a reference signal received Quality (RSRQ, reference Signal Receiving Quality) signal to noise ratio (snr) or a signal to interference plus noise ratio (SINR, signal to Interference plus Noise Ratio). For example, a first set threshold r th is set, a terminal with RSRP greater than or equal to r th participates in the uplink capacity optimization process, and a terminal with RSRP less than r th does not participate in the uplink capacity optimization process. For another example, a first set threshold SINR th is set, and terminals with SINR greater than or equal to SINR th participate in the uplink capacity optimization process, and terminals with SINR less than SINR th do not participate in the uplink capacity optimization process.
Referring to fig. 3, the process of optimizing the first objective function by using the first uplink capacity optimization model is as follows:
Initializing a population to generate n nests P i;
and 2, judging whether t is smaller than the maximum iteration number or the termination condition, if so, executing the step 3, otherwise, executing the step 5.
And 3, generating a new solution by the cuckoo through levy flight and evaluating the fitness value gamma corresponding to the new solution.
And 4, generating a random number E, judging whether the random number E is smaller than pa, and updating the solution according to a judging result.
Wherein use is made ofUpdating solutions, H () represents a step function, i.e. when pa is greater than ε, H () is 1, usingUpdate solutions, when pa is less than ε, H () is 0, useUpdating the solution.
And 5, sequencing the fitness values gamma corresponding to all the solutions and obtaining the current optimal solution.
And 6, outputting an uplink optimization result.
In practical application, in order to most accurately determine the optimal solution of uplink capacity optimization, a pareto optimal solution algorithm is adopted. According to the Pareto optimal solution definition, assuming that any two solutions S1 and S2 are superior to S2 for all targets, S1 is called as S1 to dominate S2, if S1 is not dominated by other solutions, S1 is called as non-dominated solution (also called as non-dominated solution or Pareto solution), a non-dominated set in all solutions is used as a first layer, each layer of solution is sequentially defined as a dominated set of the next layer of solution, and the serial numbers of all layers are numbered from 1.
Based on the above, when the first objective function is optimized by adopting the first uplink capacity optimization model to obtain an optimal solution about the first objective function, the method includes:
And sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to the sorting result.
In practical application, even though the transmitting power of the terminal changes, the interference between different terminals may change or may not change, so in order to further distinguish the optimal solution and the non-optimal solution, the SINR is used to measure the distance between the optimal solutions in the process of determining the optimal solution, so as to more accurately reflect the influence of the optimal solution on the uplink capacity optimization result.
Based on this, in an embodiment, the pareto optimal solution algorithm is used to rank all solutions based on the fitness of each solution in all solutions, and when determining the optimal solution according to the ranking result, the method includes:
and determining the fitness of the corresponding solution based on the Euclidean distance of the signal to noise ratio between any two first terminals.
Here, let the fitnessWhere q i denotes the sequence number of the layer where the solution resides. For the first layer solution, N i is defined as a distance factor, and N i＝∑j S(dij),dij represents the euclidean distance of SINR between any two first terminals. Therefore, by adopting the pareto optimal solution algorithm, the suitability of each solution is calculated based on the Euclidean distance of SINR between terminals, all solutions are ordered based on the suitability, and the optimal solution is selected, so that the accuracy of the cuckoo algorithm can be effectively improved.
The present application will be described in further detail with reference to examples of application.
Referring to fig. 2, a cpu 1～CPU3 characterizes a centralized controller that is deployed in a decentralized manner in the network, which can be understood as a base station. In the related art, the uplink capacity of the network is locally optimized by the base station. As shown in fig. 2, the CPU 1 optimizes the uplink capacity of the network covered by the antenna array AP 2、AP4～AP8、AP10, the CPU 2 optimizes the uplink capacity of the network covered by the antenna array AP 9、AP11～AP14, and the CPU 3 optimizes the uplink capacity of the network covered by the antenna array AP 1、AP3、AP15～AP20. In the implementation of the embodiment of the present application, a Network management system (NMS, network MANAGEMENT SYSTEM) is implemented through the Network node shown in fig. 2, and the Network uplink capacity is integrally optimized by using a cuckoo algorithm and taking the user as the center, so as to obtain an optimization result about the transmission power of each terminal, thereby implementing global optimization of the Network uplink capacity, and being suitable for a large-scale Network structure of the cellular Network.
In order to implement the method of the embodiment of the present application, the embodiment of the present application further provides an uplink capacity optimization device, which is disposed on the first network node, as shown in fig. 4, and the device includes:
A calculation unit 401, configured to optimize a first objective function by using a first uplink capacity optimization model to obtain an optimal solution about the first objective function, where a function value of the first objective function characterizes an uplink capacity of the network;
A configuration unit 402, configured to configure a corresponding first power value for the transmission power of each of the at least one first terminal, where,
The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm, and the first power value represents the transmitting power of a corresponding terminal when the first objective function takes an optimal solution.
Wherein in an embodiment the first objective function further comprises a first penalty function and a second penalty function, wherein,
The function value of the first penalty function represents the sum of the transmitting power of all second terminals in the at least one first terminal, the function value of the second penalty function represents the number of all second terminals in the at least one first terminal, and the second terminal is a terminal with the transmitting power which is larger than or equal to the rated maximum transmitting power and corresponds to the at least one first terminal.
In an embodiment, the device further comprises:
And the determining unit is used for determining a first weight value corresponding to the first penalty function and determining a second weight value corresponding to the second penalty function.
In an embodiment, the signal quality parameter of each of the at least one first terminal is greater than or equal to a first set threshold.
In an embodiment, the signal quality parameter comprises one of:
Reference signal received power;
reference signal reception quality;
Signal to noise ratio.
In an embodiment, when the computing unit 401 optimizes the first objective function by using the first uplink capacity optimization model to obtain an optimal solution for the first objective function, the computing unit is configured to:
And sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to the sorting result.
In an embodiment, the computing unit 401 is configured to:
and determining the fitness of the corresponding solution based on the Euclidean distance of the signal to noise ratio between any two first terminals.
In practical applications, the calculating unit 401 and the determining unit may be implemented by a processor in the uplink capacity optimizing device, and the configuring unit 402 may be implemented by a processor in the uplink capacity optimizing device in combination with a communication interface.
It should be noted that, when the uplink capacity optimization device provided in the foregoing embodiment performs uplink capacity optimization, only the division of each program module is used for illustration, and in practical application, the processing allocation may be completed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the uplink capacity optimization device and the uplink capacity optimization method provided in the foregoing embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments, which are not repeated herein.
Based on the hardware implementation of the program module, and in order to implement the method at the first network node side in the embodiment of the present application, the embodiment of the present application further provides a first network node, as shown in fig. 5, a first network node 500 includes:
The first communication interface 501 is capable of performing information interaction with other network nodes;
The first processor 502 is connected to the first communication interface 501 to implement information interaction with other network nodes, and is configured to execute the method provided by one or more technical solutions on the first network node side when running a computer program. And the computer program is stored on the first memory 503.
Specifically, the first processor 502 is configured to:
Optimizing a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution about the first objective function, wherein the function value of the first objective function represents the uplink capacity of the network, the first objective function takes the transmitting power of each first terminal in at least one first terminal accessed to the network as a variable, configures a corresponding first power value for the transmitting power of each first terminal in the at least one first terminal,
The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on a cuckoo algorithm, and the first power value represents the transmitting power of a corresponding terminal when the first objective function takes an optimal solution.
Wherein in an embodiment the first objective function further comprises a first penalty function and a second penalty function, wherein,
The function value of the first penalty function represents the sum of the transmitting power of all second terminals in the at least one first terminal, the function value of the second penalty function represents the number of all second terminals in the at least one first terminal, and the second terminal is a terminal with the transmitting power which is larger than or equal to the rated maximum transmitting power and corresponds to the at least one first terminal.
In an embodiment, the first processor 502 is further configured to:
determining a first weight value corresponding to the first penalty function, and determining a second weight value corresponding to the second penalty function.
In an embodiment, the signal quality parameter of each of the at least one first terminal is greater than or equal to a first set threshold.
In an embodiment, the signal quality parameter comprises one of:
Reference signal received power;
reference signal reception quality;
Signal to noise ratio.
In an embodiment, the first processor 502 is further configured to:
And sorting all solutions based on the fitness of each solution in all solutions by adopting a pareto optimal solution algorithm, and determining the optimal solution according to the sorting result.
In an embodiment, the first processor 502 is further configured to:
and determining the fitness of the corresponding solution based on the Euclidean distance of the signal to noise ratio between any two first terminals.
It should be noted that the specific processing procedure of the first processor 502 and the first communication interface 501 may be understood by referring to the above method.
Of course, in actual practice, the various components in the first network node 500 are coupled together by a bus system 504. It is to be appreciated that bus system 504 is employed to enable connected communications between these components. The bus system 504 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration, the various buses are labeled as bus system 504 in fig. 5.
The first memory 503 in the embodiment of the present application is used to store various types of data to support the operation of the first network node 500. Examples of such data include any computer program for operating on the first network node 500.
The method disclosed in the above embodiment of the present application may be applied to the first processor 502 or implemented by the first processor 502. The first processor 502 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the method may be implemented by integrated logic of hardware in the first processor 502 or instructions in software form. The first Processor 502 described above may be a general purpose Processor, a digital signal Processor (DSP, digital Signal Processor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The first processor 502 may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present application. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the application can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the first memory 503, said first processor 502 reading the information in the first memory 503, in combination with its hardware performing the steps of the method described above.
In an exemplary embodiment, the first network node 500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, programmable logic devices (PLDs, programmable Logic Device), complex Programmable logic devices (CPLDs, complex Programmable Logic Device), field-Programmable gate arrays (FPGAs), general purpose processors, controllers, micro-controllers (MCUs, micro Controller Unit), microprocessors (micro processors), or other electronic elements for performing the aforementioned methods.
It will be appreciated that the first memory 503 of embodiments of the present application may be either volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. The non-volatile Memory may be, among other things, a Read Only Memory (ROM), a programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read-Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read-Only Memory (EEPROM, ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory), Magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk-Only Memory (CD-ROM, compact Disc Read-Only Memory), which may be disk Memory or tape Memory. The volatile memory may be random access memory (RAM, random Access Memory) which acts as external cache memory. by way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronous Dynamic Random Access Memory), and, Double data rate synchronous dynamic random access memory (DDRSDRAM, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, syncLink Dynamic Random Access Memory), Direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). the memory described by embodiments of the present application is intended to comprise, without being limited to, these and any other suitable types of memory.
In an exemplary embodiment, the present application further provides a storage medium, i.e. a computer storage medium, in particular a computer readable storage medium, for example comprising a first memory 503 storing a computer program, which is executable by the first processor 502 of the first network node 500 to perform the steps of the aforementioned first network node side method. The computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash Memory, magnetic surface Memory, optical disk, or CD-ROM.
It should be noted that "first," "second," etc. are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
In addition, the embodiments of the present application may be arbitrarily combined without any collision.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the present application.
Claims (11)
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