Uplink capacity optimization method, device, network node and storage medium

Abstract

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

[0001] The present application relates to the field of communications, and in particular to an uplink capacity optimization method, device, network node and storage medium. Background Art

[0002] The sixth generation of mobile communication technology (6G, 6th Generation mobile networks) will build a ubiquitous (covering land, sea, air, and space) and all-connected (connecting people, machines, and objects) network. In other words, the network structure of the 6G communication system will change greatly, especially when the number of antenna arrays approaches or exceeds the number of users, the network structure of the cellular network will fail. Therefore, decellularization has become a candidate networking technology for 6G.

[0003] When optimizing the uplink capacity of a network, the related technology realizes local optimization of the uplink capacity of the network within the coverage area of ​​the base station, which is not applicable to the network structure of a de-cellular network. Summary of the invention

[0004] To solve related technical problems, the embodiments of the present application provide an uplink capacity optimization method, device, network node and storage medium.

[0005] An embodiment of the present application provides an uplink capacity optimization method, which is applied to a first network node and includes:

[0006] The first objective function is optimized by 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 takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;

[0007] A corresponding first power value is configured for the transmit power of each first terminal in the at least one first terminal; wherein,

[0008] The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution.

[0009] In one embodiment, the first objective function further includes a first penalty function and a second penalty function;

[0010] The function value of the first penalty function represents the sum of the transmission powers 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; the second terminal is a terminal in the at least one first terminal whose corresponding transmission power is greater than or equal to the rated maximum transmission power.

[0011] In one embodiment, before optimizing the first objective function using the first uplink capacity optimization model to obtain an optimal solution for the first objective function, the method further includes:

[0012] A first weight value corresponding to the first penalty function is determined, and a second weight value corresponding to the second penalty function is determined.

[0013] In one embodiment, a signal quality parameter of each first terminal of the at least one first terminal is greater than or equal to a first set threshold.

[0014] In one embodiment, the signal quality parameter includes one of the following:

[0015] reference signal received power;

[0016] Reference signal reception quality;

[0017] Signal-to-noise ratio.

[0018] In one embodiment, when the first uplink capacity optimization model is used to optimize the first objective function to obtain an optimal solution for the first objective function, the method includes:

[0019] The Pareto optimal solution algorithm is adopted to sort all solutions based on the fitness of each solution among all solutions, and the optimal solution is determined according to the sorting result.

[0020] In one embodiment, when the Pareto optimal solution algorithm is used to sort all solutions based on the fitness of each solution among all solutions, and the optimal solution is determined according to the sorting result, the method includes:

[0021] The fitness of the corresponding solution is determined 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.

[0022] The embodiment of the present application also provides an uplink capacity optimization device, including:

[0023] a calculation unit, configured to optimize a first objective function by adopting a first uplink capacity optimization model to obtain an optimal solution for the first objective function; a function value of the first objective function represents an uplink capacity of the network; the first objective function takes a transmit power of each first terminal in at least one first terminal accessing the network as a variable;

[0024] A configuration unit, configured to configure a corresponding first power value for the transmit power of each first terminal in the at least one first terminal; wherein,

[0025] The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution.

[0026] The embodiment of the present application further provides a network node, comprising: a first processor and a first communication interface; wherein,

[0027] The first processor is configured to:

[0028] The first objective function is optimized by 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 takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable;

[0029] A corresponding first power value is configured for the transmit power of each first terminal in the at least one first terminal; wherein,

[0030] The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution.

[0031] The embodiment of the present application further provides a network node, comprising: a first processor and a first memory for storing a computer program that can be run on the processor,

[0032] Wherein, the first processor is used to execute the steps of any of the above-mentioned uplink capacity optimization methods when running the computer program.

[0033] An embodiment of the present application further provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned uplink capacity optimization methods are implemented.

[0034] The uplink capacity optimization method, device, network node and storage medium provided in the embodiment of the present application, the network node uses the first uplink capacity optimization model to optimize the first objective function to obtain the optimal solution for the first objective function; and configures the corresponding first power value for the transmission power of each first terminal in the 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 uses the transmission power of each first terminal in the at least one first terminal accessing the network as a variable; the first uplink capacity optimization model represents the uplink capacity optimization model obtained based on the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution. Through the cuckoo algorithm, the uplink capacity of the network is optimized as a whole with the user as the center, and the optimization result of the transmission power of each terminal is obtained, so as to achieve the global optimization of the uplink capacity of the network, which is suitable for the network structure of large-scale de-cellular networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of the uplink capacity optimization method flow in an embodiment of the present application;

[0036] Figure 2 This is a schematic diagram of an overall structure example of a cellular network in an application embodiment of the present application;

[0037] Figure 3 This is a flow chart of an uplink capacity optimization method according to another embodiment of the present application;

[0038] Figure 4 This is a schematic diagram of the structure of an uplink capacity optimization device according to an embodiment of the present application;

[0039] Figure 5 This is a schematic diagram of the first network node structure of an embodiment of the present application. DETAILED DESCRIPTION

[0040] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0041] The uplink capacity of the network mainly depends on the distribution of terminals in the network and the transmission power of the terminals. When optimizing the uplink capacity of the network, the relevant technologies mainly achieve local optimization of the uplink capacity of the network within the coverage of the base station. In the 6G communication system, the network structure will change greatly, and the decellularized network will become one of the candidate networking technologies for 6G, making the solution of local optimization of the uplink capacity of the network no longer applicable to the network structure of the decellularized network.

[0042] Based on this, in various embodiments of the present application, global optimization of the network uplink capacity is achieved, wherein the network node optimizes the first objective function using a first uplink capacity optimization model to obtain an optimal solution to the first objective function; and configures a corresponding first power value for the transmit power of each first terminal in the at least one first terminal to achieve global optimization of the network uplink capacity; wherein the function value of the first objective function represents the uplink capacity of the network; the first objective function uses the transmit power of each first terminal in at least one first terminal accessing the network as a variable; 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 transmit power of the corresponding terminal when the first objective function takes the optimal solution.

[0043] The present application embodiment provides an uplink capacity optimization method, which is applied to a network node, such as Figure 1 As shown, the method includes:

[0044] Step 101: optimize a first objective function using a first uplink capacity optimization model to obtain an optimal solution to the first objective function.

[0045] The function value of the first objective function represents the uplink capacity of the network; the first objective function takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable.

[0046] Step 102: configure a corresponding first power value for the transmit power of each first terminal in the at least one first terminal.

[0047] The first uplink capacity optimization model represents the uplink capacity optimization model obtained based on the cuckoo algorithm; the first power value represents the transmit power of the corresponding terminal when the first objective function takes the optimal solution. Based on the transmit power of the corresponding terminal when the first objective function takes the optimal solution, the transmit power of each terminal is controlled, thereby optimizing the uplink capacity of the network.

[0048] Here, the uplink capacity of the network is optimized as a whole. In practical applications, the uplink capacity of the network is Among them, P k Characterizes the transmission power of the kth terminal, k≤K, and the constraints are: 0 <P k <P max , P max Indicates the maximum transmit power of the terminal. The P corresponding to different terminals max Can be the same or different, SINR kCharacterizes the uplink signal-to-noise ratio of the kth terminal. In the process of calculating the uplink capacity, it is assumed that the channel fading coefficient calculated by the pilot remains unchanged, and the main variable that determines the uplink capacity is the transmit power of each terminal in the access network. Here, the uplink signal-to-noise ratio of the kth terminal is:

[0049]

[0050] Among them, DS k Characterizes the more expected demodulated signal, BU k Characterizes the error signal introduced by imperfect demodulation, IUI kk’ Characterizes the interference signal between terminals, TN k Characterizes the 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:

[0051] Combination Figure 2 For an example of the overall structure of a decellularized network, refer to Figure 2 UE1~UE4 represent the terminals that access the network and are distributed in the network, AP1~AP 20 Characterizing the antenna arrays in the network, it can be seen that the number of antenna arrays far exceeds the number of terminals. For the massive multiple input multiple output (MIMO) system of the decellular network, considering the signal combining gain formed when multiple antenna arrays that are not in the same geographical location simultaneously receive terminal signals, it is assumed here that each antenna array has only one antenna, then there are M antenna arrays and K randomly distributed terminals. The channel fading coefficient between the kth terminal and the mth antenna array is defined as Among them, β represents large-scale fading, and h represents small-scale fading that follows Gaussian distribution. In order to effectively estimate the uplink channel fading coefficient, the minimum mean square error method (MMSE) is used to estimate all uplink pilot signals. Assuming that the pilot signal length is τ, in in, represents the pilot signal assigned to the kth terminal, and the normalized energy is 1. In this way, the signal received by the mth antenna array is Among them, the vector Represents white noise that obeys Gaussian distribution. The pilot signal is processed by matched filtering method. After processing, the signal received by the mth antenna array is in According to the MMSE algorithm, the channel estimation is On this basis, we analyzed

[0052]

[0053] Here, the uplink signal of the kth terminal is defined as: Where P k Characterizes the transmission power, s k Represents the uplink symbol, ‖s k ‖ 2 =1, then the signal received by the mth antenna array is: where n m is a white noise that obeys Gaussian distribution. All signals are processed by maximum ratio combining to obtain

[0054]

[0055] Among them, Z≤M, indicating that there are Z antenna arrays receiving the signal of the kth terminal, u mk Characterizes the maximum ratio combining coefficient of the mth antenna array receiving the kth terminal. For any terminal, let us assume that it is k, then u k =[u 1k … u Mk ] T , ‖u k ‖ 2 =1, then we have

[0056]

[0057] Based on the network's uplink capacity In practical applications, the uplink capacity is optimized. The first objective function corresponding to the uplink capacity optimization problem is:

[0058]

[0059] The corresponding constraint is 0 <P k <P max The function value of the first objective function represents the uplink capacity of the network, and the first objective function takes the transmission power of each terminal among the K terminals accessing the network as a variable.

[0060] After determining the first objective function, the first uplink capacity optimization model is used to optimize the first objective function, thereby obtaining the optimal solution for the first objective function. Here, the first uplink capacity optimization model is obtained based on the cuckoo algorithm. Among them, the idea of ​​the cuckoo algorithm (Cuckoo Search) is based on the nest parasitism behavior of the cuckoo and the Levy flight behavior of birds. The cuckoo algorithm has the characteristics of global optimization and fast convergence speed. The nest parasitism behavior of the cuckoo can be described as: each cuckoo randomly selects a nest and produces an egg each time; the nest with the highest quality egg is retained to the next generation; the number of selectable nests is fixed, and the probability that the cuckoo egg is discovered by the original nest owner is pa∈(0,1). In this case, the original bird throws away the cuckoo egg or discards the existing nest. For simplicity, it is assumed that part pa of the n nests is replaced by a new nest (new solution). In the cuckoo algorithm, the egg in each nest represents a solution, and the cuckoo egg represents a new solution. The goal is to replace the inferior solution in the nest with a new solution or a potential superior solution. In addition, levy flight is a typical random walk mechanism, which represents a type of non-Gaussian random process. The stable increment of levy flight obeys the levy stable distribution. Since the second-order moment of levy flight diverges, the motion process always has a large jump in a small aggregation situation. Therefore, introducing levy flight into optimization technology is an effective strategy to avoid the algorithm from falling into the local optimum.

[0061] In the cuckoo algorithm, when cuckoo i generates a new solution x t+1 When , a levy flight is performed, as shown in the following formula: Among them, α>0 is the step size, represents the multiplication operator, the random walk step length of levy flight follows the levy distribution, Where u has a mean of 0 and a variance of σ 2 The distribution of v follows a distribution with mean 0 and variance 1.

[0062] Combined with the first objective function, since the channel fading coefficient is relatively fixed, the uplink capacity optimization problem only uses the transmission power of each of the K terminals accessing the network as a variable, thus simplifying the cuckoo's nest to have one egg.

[0063] In the above, the first objective function has 0 <P k <P max The constraints of , that is, the uplink capacity optimization problem is a constrained optimization problem. Here, we consider using a penalty function to convert the constrained optimization problem into an unconstrained optimization problem to improve the algorithm iteration efficiency in the uplink capacity optimization process.

[0064] Based on this, in one embodiment, the first objective function also includes a first penalty function and a second penalty function; wherein,

[0065] The function value of the first penalty function represents the sum of the transmission powers 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; the second terminal is a terminal in the at least one first terminal whose corresponding transmission power is greater than or equal to the rated maximum transmission power.

[0066] 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. When determining the first objective function, the penalty coefficient corresponding to each penalty function can be determined according to the actual deployment of the network.

[0067] Based on this, in one embodiment, before optimizing the first objective function using the first uplink capacity optimization model to obtain an optimal solution for the first objective function, the method further includes:

[0068] A first weight value corresponding to the first penalty function is determined, and a second weight value corresponding to the second penalty function is determined.

[0069] In practical application, the first objective function is set to

[0070] F(P)=f(P)+w1*(sum_viol) 2 +w2*num_viol

[0071] Among them, f(P) is based on The function determined to characterize the uplink capacity of the network corresponds to the constraints in the above text, sum_viol characterizes the sum of the transmit powers of all terminals that violate the constraints, that is, the sum of the transmit powers of terminals whose transmit powers are greater than or equal to the rated maximum transmit power; w1 is the first weight value corresponding to the first penalty function sum_viol, that is, the penalty coefficient corresponding to the first penalty function sum_viol; num_viol characterizes the number of terminals that violate the constraints, w2 is the second weight value corresponding to the second penalty function num_viol, that is, the penalty coefficient corresponding to the second penalty function num_viol.

[0072] In actual application, there are a large number of terminals accessing the network. Considering that the uplink capacity of the network mainly depends on the transmission power of the terminals accessing the network, signal strength-related constraints can be introduced in the uplink capacity optimization process. In the process of the cuckoo generating new solutions through levy flight, in order to further reduce the amount of calculation and improve the calculation efficiency, the terminals whose signal strength reaches a certain threshold participate in the uplink capacity optimization process, and the terminals whose signal strength does not reach a certain threshold do not participate in the uplink capacity optimization process.

[0073] Based on this, in one embodiment, the signal quality parameter of each first terminal in the at least one first terminal is greater than or equal to a first set threshold.

[0074] Here, the signal quality parameter of the first terminal is greater than the first set threshold, and the signal quality parameter includes but is not limited to the measured reference signal receiving power (RSRP, Reference Signal Receiving Power) of the terminal, the reference signal receiving quality (RSRQ, Reference Signal Receiving Quality) signal to noise ratio or signal to interference plus noise ratio (SINR, Signal to Interference plus Noise Ratio). For example, the first set threshold r is set th , RSRP is greater than or equal to r th The terminals participate in the uplink capacity optimization process. For the terminals with RSRP less than r th The terminal does not participate in the uplink capacity optimization process. For another example, the first set threshold SINR is set th , SINR is greater than or equal to SINR th The terminal participates in the uplink capacity optimization process. For SINR less than SINR th The terminal does not participate in the uplink capacity optimization process.

[0075] Combination Figure 3 , the process of optimizing the first objective function using the first uplink capacity optimization model is as follows:

[0076] Step 1: Initialize the population and generate n nests P i ;

[0077] Step 2: Determine whether t is less than the maximum number of iterations or the termination condition. If so, execute step 3; otherwise, execute step 5.

[0078] Step 3: The cuckoo generates a new solution through levy flight and evaluates the fitness value γ corresponding to the new solution.

[0079] Step 4: Generate a random number ∈, determine whether the random number ∈ is less than pa, and update the solution based on the judgment result.

[0080] Among them, Update the solution, H() represents the step function, that is, when pa is greater than ∈, H() is 1, and Update the solution; when pa is less than ∈, H() is 0, use Update solution.

[0081] Step 5: Sort the fitness values ​​γ corresponding to all solutions and find the current optimal solution.

[0082] Step 6: Output the uplink optimization results.

[0083] In actual application, in order to most accurately determine the optimal solution for uplink capacity optimization, the Pareto optimal solution algorithm is used. According to the definition of Pareto optimal solution: Assume that for any two solutions S1 and S2, S1 is better than S2 for all objectives, and S1 is said to dominate S2. If S1 is not dominated by other solutions, then S1 is called a non-dominated solution (also called an undominated solution or Pareto solution). The non-dominated set of all solutions is the first layer, and each layer of solutions is defined as the dominating set of the next layer of solutions. The serial numbers of each layer start from 1.

[0084] Based on this, when the first uplink capacity optimization model is used to optimize the first objective function and an optimal solution for the first objective function is obtained, the method includes:

[0085] The Pareto optimal solution algorithm is adopted to sort all solutions based on the fitness of each solution among all solutions, and the optimal solution is determined according to the sorting result.

[0086] In actual applications, even if the transmit power of the terminal changes, the interference between different terminals may change or remain unchanged. Therefore, in order to further distinguish the optimal solution from the non-optimal solution, 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 impact of the optimal solution on the uplink capacity optimization result.

[0087] Based on this, in one embodiment, the Pareto optimal solution algorithm is used to sort all solutions based on the fitness of each solution in all solutions, and when the optimal solution is determined according to the sorting result, the method includes:

[0088] Based on the Euclidean distance of the signal-to-noise ratio between any two first terminals, the fitness of the corresponding solution is determined.

[0089] Here, let fitness where q i Indicates the serial number of the layer where the solution is located. For the first layer solution, define N i is the distance factor, N i =∑ j S(d ij), d ij In this way, by adopting the Pareto optimal solution algorithm, calculating the fitness of each solution based on the Euclidean distance of SINR between the terminals, and sorting all solutions based on the fitness, and selecting the optimal solution, the accuracy of the cuckoo algorithm can be effectively improved.

[0090] The present application is further described in detail below in conjunction with application examples.

[0091] Reference Figure 2 , CPU1~CPU3 represent the centralized controllers deployed in the network, which can be understood as base stations. In related technologies, the uplink capacity of the network is locally optimized through base stations. Figure 2 As shown, CPU1 controls antenna arrays AP2, AP4-AP8, AP 10 The network covered by the CPU2 optimizes the uplink capacity of the antenna array AP9 and AP 11 ~AP 14 The network covered by the CPU3 optimizes the uplink capacity of the antenna array AP1, AP3, and AP 15 ~AP 20 In the process of implementing the embodiment of the present application, by Figure 2 The network nodes shown in the figure implement a network management system (NMS). The cuckoo algorithm is used to optimize the uplink capacity of the network as a whole with users as the center, and the optimization result of the transmission power of each terminal is obtained, so as to achieve global optimization of the uplink capacity of the network, which is suitable for the network structure of large-scale de-cellular networks.

[0092] In order to implement the method of the embodiment of the present application, the embodiment of the present application also provides an uplink capacity optimization device, which is arranged on the first network node, such as Figure 4 As shown, the device comprises:

[0093] A calculation unit 401 is configured to optimize a first objective function using a first uplink capacity optimization model to obtain an optimal solution for the first objective function; a function value of the first objective function represents an uplink capacity of the network; the first objective function uses a transmit power of each first terminal in at least one first terminal accessing the network as a variable;

[0094] The configuration unit 402 is configured to configure a corresponding first power value for the transmit power of each first terminal in the at least one first terminal; wherein,

[0095] The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution.

[0096] In one embodiment, the first objective function further includes a first penalty function and a second penalty function;

[0097] The function value of the first penalty function represents the sum of the transmission powers 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; the second terminal is a terminal in the at least one first terminal whose corresponding transmission power is greater than or equal to the rated maximum transmission power.

[0098] In one embodiment, the device further comprises:

[0099] A determination unit is used to determine a first weight value corresponding to the first penalty function and to determine a second weight value corresponding to the second penalty function.

[0100] In one embodiment, a signal quality parameter of each first terminal of the at least one first terminal is greater than or equal to a first set threshold.

[0101] In one embodiment, the signal quality parameter includes one of the following:

[0102] reference signal received power;

[0103] Reference signal reception quality;

[0104] Signal-to-noise ratio.

[0105] In one embodiment, the calculation unit 401 optimizes the first objective function using the first uplink capacity optimization model to obtain an optimal solution for the first objective function, and is used to:

[0106] The Pareto optimal solution algorithm is adopted to sort all solutions based on the fitness of each solution among all solutions, and the optimal solution is determined according to the sorting result.

[0107] In one embodiment, the computing unit 401 is used to:

[0108] Based on the Euclidean distance of the signal-to-noise ratio between any two first terminals, the fitness of the corresponding solution is determined.

[0109] In actual application, the calculation unit 401 and the determination unit may be implemented by a processor in the uplink capacity optimization device, and the configuration unit 402 may be implemented by a processor in the uplink capacity optimization device in combination with a communication interface.

[0110] It should be noted that: the uplink capacity optimization device provided in the above embodiment only uses the division of the above program modules as an example when performing uplink capacity optimization. In actual applications, the above processing can be assigned to different program modules as needed, 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 provided in the above embodiment and the uplink capacity optimization method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0111] Based on the hardware implementation of the above program module, and in order to implement the method on the first network node side of the embodiment of the present application, the embodiment of the present application also provides a first network node, such as Figure 5 As shown, the first network node 500 includes:

[0112] The first communication interface 501 is capable of exchanging information with other network nodes;

[0113] The first processor 502 is connected to the first communication interface 501 to implement information exchange with other network nodes, and is used to execute the method provided by one or more technical solutions of the first network node side when running a computer program. The computer program is stored in the first memory 503.

[0114] Specifically, the first processor 502 is configured to:

[0115] The first objective function is optimized 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 takes the transmit power of each first terminal in at least one first terminal accessing the network as a variable; a corresponding first power value is configured for the transmit power of each first terminal in the at least one first terminal; wherein,

[0116] The first uplink capacity optimization model represents an uplink capacity optimization model obtained based on the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution.

[0117] In one embodiment, the first objective function further includes a first penalty function and a second penalty function;

[0118] The function value of the first penalty function represents the sum of the transmission powers 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; the second terminal is a terminal in the at least one first terminal whose corresponding transmission power is greater than or equal to the rated maximum transmission power.

[0119] In one embodiment, the first processor 502 is further configured to:

[0120] A first weight value corresponding to the first penalty function is determined, and a second weight value corresponding to the second penalty function is determined.

[0121] In one embodiment, a signal quality parameter of each first terminal of the at least one first terminal is greater than or equal to a first set threshold.

[0122] In one embodiment, the signal quality parameter includes one of the following:

[0123] reference signal received power;

[0124] Reference signal reception quality;

[0125] Signal-to-noise ratio.

[0126] In one embodiment, the first processor 502 is further configured to:

[0127] The Pareto optimal solution algorithm is adopted to sort all solutions based on the fitness of each solution among all solutions, and the optimal solution is determined according to the sorting result.

[0128] In one embodiment, the first processor 502 is further configured to:

[0129] Based on the Euclidean distance of the signal-to-noise ratio between any two first terminals, the fitness of the corresponding solution is determined.

[0130] It should be noted that the specific processing process of the first processor 502 and the first communication interface 501 can be understood by referring to the above method.

[0131] Of course, in actual application, the various components in the first network node 500 are coupled together through the bus system 504. It can be understood that the bus system 504 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 504 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 5 Various buses are labeled as bus system 504 .

[0132] 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 used to operate on the first network node 500.

[0133] The method disclosed in the above embodiment of the present application can 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 the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 502. The above-mentioned first processor 502 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The first processor 502 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in the first memory 503, and the first processor 502 reads the information in the first memory 503 and completes the steps of the above method in combination with its hardware.

[0134] 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), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0135] It can be understood that the first memory 503 of the embodiment of the present application can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0136] In an exemplary embodiment, the embodiment of the present application further provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, for example, including a first memory 503 storing a computer program, and the computer program can be executed by the first processor 502 of the first network node 500 to complete the steps of the first network node side method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface storage, optical disk, or CD-ROM.

[0137] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0138] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0139] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A method for optimizing uplink capacity, characterized in that: Applied to a first network node, comprising: The first objective function is optimized by 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 takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable; A corresponding first power value is configured for the transmit 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 the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution; the optimization of the first objective function using the first uplink capacity optimization model includes: sorting all solutions based on the fitness of each solution in all solutions of the first objective function, and determining the optimal solution according to the sorting result; the fitness is determined based on the Euclidean distance of the signal-to-noise ratio between any two first terminals.

2. The method according to claim 1, characterized in that The first objective function also includes a first penalty function and a second penalty function; wherein, The function value of the first penalty function represents the sum of the transmission powers 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; the second terminal is a terminal in the at least one first terminal whose corresponding transmission power is greater than or equal to the rated maximum transmission power.

3. The method according to claim 2, characterized in that Before 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: A first weight value corresponding to the first penalty function is determined, and a second weight value corresponding to the second penalty function is determined.

4. The method according to claim 1, characterized in that: A signal quality parameter of each first terminal of the at least one first terminal is greater than or equal to a first set threshold.

5. The method according to claim 4, characterized in that The signal quality parameter includes one of the following: reference signal received power; Reference signal reception quality; Signal-to-noise ratio.

6. The method according to claim 1, characterized in that When the first uplink capacity optimization model is used to optimize the first objective function to obtain an optimal solution for the first objective function, the method includes: The Pareto optimal solution algorithm is adopted to sort all solutions based on the fitness of each solution among all solutions, and the optimal solution is determined according to the sorting result.

7. The method according to claim 6, characterized in that When all solutions are sorted based on the fitness of each solution among all solutions using the Pareto optimal solution algorithm and the optimal solution is determined according to the sorting result, the method includes: Based on the Euclidean distance of the signal-to-noise ratio between any two first terminals, the fitness of the corresponding solution is determined.

8. An uplink capacity optimization device, characterized in that: include: A calculation unit, configured to optimize a first objective function by 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 takes the transmit power of each first terminal of at least one first terminal accessing the network as a variable; A configuration unit, configured to configure a corresponding first power value for the transmit 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 the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution; the optimization of the first objective function using the first uplink capacity optimization model includes: sorting all solutions based on the fitness of each solution in all solutions of the first objective function, and determining the optimal solution according to the sorting result; the fitness is determined based on the Euclidean distance of the signal-to-noise ratio between any two first terminals.

9. A network node, characterized in that: include: A first processor and a first communication interface; wherein, The first processor is configured to: The first objective function is optimized by 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 takes the transmission power of each first terminal in at least one first terminal accessing the network as a variable; A corresponding first power value is configured for the transmit 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 the cuckoo algorithm; the first power value represents the transmission power of the corresponding terminal when the first objective function takes the optimal solution; the optimization of the first objective function using the first uplink capacity optimization model includes: sorting all solutions based on the fitness of each solution in all solutions of the first objective function, and determining the optimal solution according to the sorting result; the fitness is determined based on the Euclidean distance of the signal-to-noise ratio between any two first terminals.

10. A network node, characterized in that: include: a first processor and a first memory for storing a computer program executable on the processor, Wherein, when the first processor is used to run the computer program, the steps of the method described in any one of claims 1 to 7 are executed.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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