A method and system for allocating resources for d2d communication in a cellular network

Abstract

This invention discloses a method and system for allocating D2D communication resources in a cellular network, relating to the field of wireless communication technology. The method includes the following steps: receiving D2D user-related variables; inputting these variables into a pre-built system user throughput model to obtain system user throughput, wherein the D2D user-related variables include mode selection variables, power control variables, and channel allocation vectors; determining the optimal D2D communication mode based on the optimal throughput principle; wherein the optimal throughput principle aims to maximize system user throughput; solving for the D2D user signal-to-noise ratio (SNR) under the optimal D2D communication mode; inputting the D2D user SNR into a pre-established power optimization model to obtain the optimal transmit power for the D2D user; and inputting the optimal transmit power for the D2D user into a pre-established channel allocation model to obtain the optimal channel allocation result.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically a method and system for allocating D2D communication resources in cellular networks. Background Technology

[0002] With the increasing demand for wireless communication technology, people are placing higher and higher demands on spectrum resources, making spectrum resource shortage a research hotspot. Therefore, for cellular networks, achieving high-quality service communication with limited spectrum resources is particularly important. Device-to-device (D2D) communication has emerged as a promising solution to the problem of limited spectrum resources, and many researchers consider D2D communication technology a key technology for fifth-generation (5G) wireless communication and have conducted extensive research on it.

[0003] Research has particularly focused on resource allocation in D2D communication. In D2D communication, D2D users can reuse uplink or downlink spectrum resources from cellular users for communication. To achieve higher efficiency and reduce interference between links, an efficient and reasonable resource allocation strategy is crucial. Hu et al., based on the analysis of optimal joint power control and channel allocation strategies, proposed an energy-saving iterative algorithm to maximize the efficiency of D2D communication. To maximize the operating efficiency of D2D communication equipment, existing literature has studied a D2D communication channel allocation scheme and resource optimization scheme based on spectrum clustering and non-cooperative game theory. Considering the constraints of channel reliability, some literature proposes resource allocation schemes that maximize channel ergodicity to find the optimal power allocation and use the Hungarian method with maximum weight bipartial matching to find the optimal channel allocation. Khanolkar et al. presented a strategy using an improved single-stage artificial bee colony algorithm to solve the joint energy-saving resource allocation problem. Since D2D communication can interfere with cellular networks, to achieve a resource allocation scheme that does not affect cellular users, existing literature has proposed a channel allocation algorithm based on partial location information, aiming to maximize the total throughput of D2D users when the number of cellular users changes. Existing literature has designed an iterative scheme to jointly optimize channels and power based on the many-to-one channel reuse criterion among users, aiming to maximize the overall energy efficiency of D2D user pairs. Other literature has proposed a two-stage resource allocation scheme, employing the Hungarian algorithm for initial channel allocation and then performing secondary channel allocation based on channel quality priority and interference experienced by the D2D pair during the reuse phase, thereby maximizing the total D2D communication capacity. Existing literature has also designed a mode selection framework and power control method based on D2D user distance, selecting a dedicated mode when users are close together, and a multiplexing mode otherwise. Furthermore, existing literature has proposed mode selection rules based on signal-to-interference-plus-noise ratio (SINR) and channel capacity, and analyzed their impact on various scenarios using a cooperative precoding scheme for spectrum allocation.

[0004] While existing technologies have improved the efficiency of D2D communication, their effects are not significant, and they do not jointly consider the issues of mode selection, channel allocation, and power control. Their operating speed and convergence performance are also poor. Summary of the Invention

[0005] To address the shortcomings mentioned in the background section, the present invention aims to provide a method and system for allocating D2D communication resources in cellular networks.

[0006] The objective of this invention can be achieved through the following technical solution: a method for allocating D2D communication resources in a cellular network, the method comprising the following steps:

[0007] Receive D2D user-related variables and input them into a pre-built system user throughput model to obtain system user throughput. The D2D user-related variables include mode selection variables, power control variables, and channel allocation vectors.

[0008] The system user throughput is determined based on the principle of optimal throughput to identify the optimal D2D communication mode; wherein the principle of optimal throughput aims to maximize the system user throughput.

[0009] The signal-to-noise ratio (SNR) of D2D users is solved in the optimal D2D communication mode. The SNR of D2D users is then input into a pre-established power optimization model to obtain the optimal transmit power of D2D users.

[0010] The optimal transmit power of the D2D user is input into the pre-established channel allocation model to obtain the optimal channel allocation result.

[0011] Preferably, the communication modes adopted by the D2D user include multiplexing mode and dedicated mode.

[0012] Preferably, in the dedicated mode, D2D communication selects a dedicated channel for communication. In the dedicated mode, there is no interference between D2D users and cellular users. However, there may be multiple D2D users occupying the same channel. In the multiplexing mode, D2D users are not only affected by interference from cellular users, but also by interference from D2D users multiplexing the same channel.

[0013] Preferably, when the i-th D2D user is in dedicated mode, it occupies the uplink channel resources of the j-th cell. Since the channel is pre-allocated to the cellular user, the dedicated mode channel should be selected from the idle channel, so j∈{N+1,...,X}, X={1,2,...j,...,X},j∈X, and the mode selection vector x i =1, i∈D, the signal-to-noise ratio of D2D users in dedicated mode is as follows:

[0014]

[0015] In the formula, and Let represent the path loss gain of the i-th pair of D2D users and the path loss gain from the transmitter of the l-th pair of D2D users to the receiver of the i-th pair of D2D users, respectively. Indicates the transmit power of D2D users. This represents the Gaussian noise at the receiver of the i-th D2D user.

[0016] Preferably, when the i-th D2D user is in multiplexing mode, the j-th cellular channel is multiplexed. Since it is multiplexing cellular user channels, the multiplexing mode channel should be selected from the channel set pre-allocated to cellular users. Therefore, at this time j∈Cell, the mode selection vector x i =2, i∈M, the signal-to-noise ratio of D2D users in the multiplexing mode is as follows:

[0017]

[0018] In the formula, This indicates the transmit power of cellular users, and For fixed values, This represents the path loss gain from the transmitter of the j-th cellular user to the receiver of the i-th D2D user pair. This represents the Gaussian noise at the receiver of the j-th cellular user;

[0019] When the SINR of the j-th cellular user, j∈Cell, is as follows:

[0020]

[0021] In the formula, This represents the path loss gain from the transmitter of the k-th D2D user to the receiver of the j-th cellular user. This represents the path loss gain from the transmitter of the j-th cellular user to the base station;

[0022] When cellular users communicate, they are subject to interference from D2D user transmitters that are sharing the same channel.

[0023] Preferably, the throughput of the D2D users and cellular users is expressed using the Shannon formula as shown below.

[0024]

[0025]

[0026] The specific formula for the path loss gain model is shown below.

[0027] U=kd -α

[0028] In the formula, k represents the path loss attenuation constant coefficient, α represents the path loss attenuation exponent, and d represents the distance between the transmitter and receiver along the transmission path. To meet the service quality constraints, the minimum signal-to-noise ratio (SNR) for both D2D and cellular users must be achieved. and To verify the system throughput performance, the objective function is defined as follows:

[0029]

[0030] In the above formula S represents the transmit power of the m-th D2D user. m Let x represent the channel allocation result for the m-th D2D user. m Represents the mode selection vector of the m-th D2D user. The D2D user pair set is D = {1, 2, ..., i, ..., M}, i ∈ D, and the cellular user set is Cell = {1, 2, ..., h, ..., N}, h ∈ Cell. This represents the m-th D2D user in the mode selection vector x. m S occupies m D2D user throughput of channel resources This represents the throughput of the h-th cellular user;

[0031] The constraints of the objective function include: power constraints, mode selection vector, channel allocation vector for each D2D user, and signal-to-noise ratio range limits that satisfy the quality of service constraints.

[0032] Preferably, the process of determining the optimal D2D communication mode by judging the system user throughput based on the principle of optimal throughput is as follows:

[0033] For any i-th D2D user, The mode selection process is as follows:

[0034] When the D2D user is in dedicated mode, the computation

[0035] When the D2D user is in reuse mode, computation

[0036] like If the following condition is met, then the D2D user adopts the reuse mode; otherwise, the dedicated mode is adopted.

[0037]

[0038] Inter max This represents the maximum interference threshold for D2D users.

[0039] Preferably, the power optimization model is as follows:

[0040]

[0041] In the above formula S represents the transmit power of the m-th D2D user. m Let x represent the channel allocation result for the m-th D2D user. m Represents the mode selection vector of the m-th D2D user. The set of D2D user pairs is D = {1,2,...i,...,M}, i∈D. This represents the m-th D2D user in the mode selection vector x. m S occupies m D2D user throughput of channel resources.

[0042] Preferably, the channel allocation model is as follows:

[0043]

[0044] In the above formula, S m This represents the channel allocation result for the m-th D2D user. Since the channel allocation optimization in this paper is performed in multiplexing mode, the mode selection vector x is... m =2,S m The set of D2D user pairs in Cell is D = {1, 2, ..., i, ..., M}, i ∈ D, and the set of cellular users is Cell = {1, 2, ..., h, ..., N}, h ∈ Cell. This indicates that the m-th D2D user occupies S in reuse mode. m D2D user throughput of channel resources.

[0045] In another aspect of the present invention, in order to achieve the above-mentioned objective, a cellular network D2D communication resource allocation system is disclosed, comprising:

[0046] Throughput calculation module: used to receive D2D user-related variables, input the D2D user-related variables into a pre-built system user throughput model to obtain the system user throughput, wherein the D2D user-related variables include mode selection variables, power control variables and channel allocation vector;

[0047] Judgment module: used to determine the system user throughput based on the throughput optimization principle and determine the optimal D2D communication mode; wherein the throughput optimization principle aims to maximize the system user throughput;

[0048] Transmit power module: used to solve the signal-to-noise ratio of D2D users in the optimal mode of D2D communication, inputting the D2D user signal-to-noise ratio into a pre-established power optimization model to obtain the optimal transmit power of D2D users;

[0049] Channel allocation module: Used to input the optimal transmit power of D2D users into a pre-established channel allocation model to obtain the optimal channel allocation result.

[0050] The beneficial effects of this invention are:

[0051] This invention can not only effectively improve system user throughput and reduce cellular user interference, but also improve convergence effect and optimization accuracy while ensuring algorithm running speed. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0054] Figure 2 This is a schematic diagram of the system structure of the present invention;

[0055] Figure 3 This is a single-cell D2D communication system model of the present invention;

[0056] Figure 4 This is the cross-breeding process of the population in this invention;

[0057] Figure 5 This is the population mutation process of the present invention;

[0058] Figure 6 This is a comparison chart of user throughput in different schemes of the present invention;

[0059] Figure 7 This is a comparison chart of total interference to cellular users under different schemes of the present invention;

[0060] Figure 8 This is a graph showing the variation in system user throughput at different D2D user distances according to the present invention;

[0061] Figure 9 This is a CDF comparison chart of the cumulative user throughput distribution of different schemes in the present invention;

[0062] Figure 10 This is a comparison chart of the convergence of different schemes in this invention;

[0063] Figure 11 This is a comparison chart of the running speeds of different schemes of the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] like Figure 1 As shown, a method for allocating D2D communication resources in a cellular network includes the following steps:

[0066] Receive D2D user-related variables and input them into a pre-built system user throughput model to obtain system user throughput. The D2D user-related variables include mode selection variables, power control variables, and channel allocation vectors.

[0067] The system user throughput is determined based on the principle of optimal throughput to identify the optimal D2D communication mode; wherein the principle of optimal throughput aims to maximize the system user throughput.

[0068] The signal-to-noise ratio (SNR) of D2D users is solved in the optimal D2D communication mode. The SNR of D2D users is then input into a pre-established power optimization model to obtain the optimal transmit power of D2D users.

[0069] The optimal transmit power of the D2D user is input into the pre-established channel allocation model to obtain the optimal channel allocation result.

[0070] It needs to be further explained that, in the specific implementation process, Figure 3 A single-cell D2D communication system model is described. The cell center contains a base station, and D2D communication users and cellular users are evenly distributed around the base station. This paper only considers the resource allocation of the cellular uplink. It is assumed that D2D users in this cell can use either dedicated mode or multiplexing mode, and each D2D user can multiplex a maximum of one channel, while one channel can be multiplexed by multiple D2D users. The system parameters are shown in Table 1.

[0071] Table 1. Community System Parameters

[0072]

[0073] Assume that D2D users in this cell can use either dedicated mode or multiplexing mode. Each D2D user can multiplex a maximum of one channel, and one channel can be multiplexed by multiple D2D users. The cell currently has X channels, X = {1, 2, ..., c, ..., X}, c ∈ X. Each channel is first pre-assigned to cellular users, and the set of cellular users is defined as Cell = {1, 2, ..., h, ..., N}, h ∈ Cell. The set of D2D user pairs is D = {1, 2, ..., i, ..., M}, i ∈ D. Each D2D user pair is transmitted by a D2D transmitter (DUT). i and D2D receiver DUR i Composition, N > M. M D2D communication users access the cellular system in dedicated mode or multiplexing mode. The channel allocation set of the D2D users is S, where the channel allocation vector of the i-th D2D user is Si. i =c, c∈X. For convenience, the service quality constraint QoS is abstracted as signal-to-noise ratio (SINR). Therefore, the SINR (Signal to Interference plus Noise Ratio) for D2D user pairs and cellular users is defined as follows:

[0074] 1. When the i-th D2D user is in dedicated mode, it occupies the uplink channel resources of the j-th cell. Since the channel is pre-allocated to cellular users, the dedicated mode channel should be selected from the idle channel, so j∈{N+1,...,X}, and the mode selection vector x i =1, i∈M, its SINR is as follows:

[0075]

[0076] Dedicated mode refers to D2D communication using a dedicated channel. In dedicated mode, there is no interference between D2D users and cellular users, but there may be multiple D2D users occupying the same channel.

[0077] 2. When the i-th D2D user is in multiplexing mode, the j-th cellular channel is multiplexed. Since it is multiplexing cellular user channels, the multiplexing mode channel should be selected from the channel set pre-allocated to cellular users. Therefore, at this time j∈Cell, the mode selection vector x i =2, i∈M, its SINR is as follows:

[0078]

[0079] Multiplexing mode refers to D2D users reusing the channel resources of cellular users within the same cell. In multiplexing mode, D2D users are not only subject to interference from cellular users, but also to interference from other D2D users reusing the same channel.

[0080] 3. The SINR of the j-th cellular user, j∈Cell, is as follows:

[0081]

[0082] Cellular users are subject to interference from D2D user transmitters that reuse the same channel when communicating.

[0083] In the above equations (1) to (3), and Let represent the path loss gain of the i-th pair of D2D users and the path loss gain from the transmitter of the l-th pair of D2D users to the receiver of the i-th pair of D2D users, respectively (the l-th pair of D2D users and the i-th pair of D2D users occupy the same channel spectrum resources). and This represents the transmit power of D2D users and cellular users. Since scheduling issues for cellular users are not considered, therefore... It is a fixed value, while The upper and lower bounds are and This indicates the maximum and minimum transmit power of D2D. and Let represent the path loss gain from the transmitter of the j-th cellular user to the receiver of the i-th pair of D2D users and the path loss gain from the transmitter of the k-th pair of D2D users to the receiver of the j-th cellular user. This represents the path loss gain from the transmitter to the base station for the j-th cellular user. and Let represent the Gaussian noise at the receiver of the i-th D2D user and the j-th cellular user. The throughput of the D2D user and the cellular user is then expressed using Shannon's formulas as shown in equations (4) and (5), while the specific formula for the path loss gain model is shown in equation (6).

[0084]

[0085]

[0086] U=kd -α (6)

[0087] In equation (6), k represents the path loss attenuation constant coefficient, α represents the path loss attenuation exponent, and d represents the distance between the transmitter and receiver along the transmission path. To meet the quality of service constraints, the minimum signal-to-noise ratio (SNR) for both D2D and cellular users must be achieved. and To verify the performance of the proposed scheme in terms of system throughput, the objective function is defined as follows:

[0088]

[0089] The constraints are as follows:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] The above (7) to (12) describe the optimization framework of this paper. Equation (7) is the objective function formula for verifying the performance of this paper, which includes two parts: D2D users and cellular users. Equation (8) represents the power constraint condition for D2D users, and Equation (9) represents the mode selection vector. When D2D users perform dedicated mode communication, x m =1, otherwise x m =2. Equation (10) defines the channel allocation vector for each D2D user and indicates that each D2D user can only occupy one channel. Equations (11) and (12) represent the signal-to-noise ratio range limits under the service quality constraints.

[0096] Step 2 includes:

[0097] This article mainly discusses the selection of D2D user modes between dedicated mode and reuse mode.

[0098] Based on the principle of optimal throughput, the optimal D2D communication mode is determined. For any i-th D2D user, The mode selection process is as follows:

[0099] 1. Assuming the D2D user is in dedicated mode, calculate according to equation (1).

[0100] 2. Assuming the D2D user is in reuse mode, calculate according to equation (2).

[0101] 3. If If equation (13) is satisfied, then the D2D user adopts the reuse mode; otherwise, the dedicated mode is adopted.

[0102]

[0103] Inter max Represents the maximum interference threshold for D2D users

[0104] The power control section determines the optimal transmit power for D2D users. It's important to note that the channel allocation scheme obtained by the channel allocation section at this stage is known, i.e., the channel allocation matrix S = [S1, ..., S2] is known.i ,...S M ], i∈M. The power optimization problem in this paper can be transformed into the power control problem of each D2D user under the current channel allocation scheme, so the power optimization model can be simplified to Equation (14).

[0105]

[0106] The constraints of equation (13) above are (7), (9), and (10). For this power optimization model, this paper proposes a hybrid gray wolf optimization algorithm to solve it. The specific steps of the algorithm are as follows:

[0107] 1. Population initialization based on Tent chaotic mapping

[0108] The Tent map is a piecewise linear map and also a two-dimensional chaotic map. Because chaotic maps possess randomness and ergodicity, this paper introduces the Tent chaotic map to initialize the population, enabling the algorithm to converge faster. The chaotic sequence generated based on the Tent chaotic map is shown in equation (15):

[0109]

[0110] This paper sets the population size to nPop and the population dimension to M, hence Z n =rand(nPop,M), where a is typically 0.5, and thus, based on the generated chaotic sequence Z... n+1 The resulting initial population is shown in equation (16):

[0111]

[0112] 2. Population search

[0113] The population search process is consistent with the GWO algorithm. The fitness value of each individual in the population is calculated based on the initial population position and the objective function (14) to obtain the optimal solution αwolfX. α And optimal solution β wolf X β Then, calculate D according to equations (17) and (18). α and D β .

[0114] D α =|C1*X α -pop(i,j)| (17)

[0115] D β =|C2*X β -pop(i,j)| (18)

[0116] In the above formula, C1 and C2 are random numbers between [0,1], while pop(i,j) refers to the position of an individual in the population, which corresponds to the transmission power of the j-th D2D user in the i-th population.

[0117] 3. Introduce a nonlinear decreasing convergence factor

[0118] Since the convergence factor of the original GWO algorithm cannot adapt to the population situation during the algorithm iteration process, it is easy to get trapped in local optima and reduce the optimization accuracy. Therefore, a segmentation strategy is introduced, using two different nonlinear convergence factors to control the transformation between the search and utilization stages of the algorithm, as shown in Equation (19).

[0119]

[0120] Where l is the current iteration number, l max P represents the maximum number of iterations. a The convergence factor is set to an exponentially decreasing probability, typically 0.6, while l max Setting it to 100 allows the population to perform a global search over a longer period when 'a' decreases exponentially, enhancing the algorithm's global optimization capability. When 'a' decreases polynomially, it can randomly escape local optima, further expanding the search range, improving the algorithm's local convergence speed, and preventing premature convergence.

[0121] 4. Introduce Levi's flight strategy for position updates

[0122] Levy flight refers to a random walk with a heavy-tailed probability distribution based on the step size. It is essentially a random walk mechanism and is a type of non-Gaussian random process. It can escape local optima, expand the search, and avoid premature convergence. Its position update process is shown in Equation (20).

[0123]

[0124] Where A1 = 2*a*r1 - a, A2 = 2*a*r2 - a, and r1, r2, and A are all arbitrary random numbers between [0,1]. As shown in equation (21).

[0125]

[0126] In the above formula, ρ and ω follow a normal distribution, as shown in formulas (22) and (23).

[0127]

[0128]

[0129] Where β is any random number between [0, 2].

[0130] 5. Greedy selection strategy

[0131] To enrich the population and retain better individuals, a greedy selection strategy is used to eliminate weaker individuals during iterations. If X new If the fitness value of a new solution is better than that of the current solution pop(i,j), then the new solution will replace the current solution; otherwise, the current solution will be retained.

[0132] The channel allocation section primarily employs a hybrid genetic algorithm to obtain the optimal channel allocation scheme based on feedback from the power control section. However, this paper mainly considers channel allocation under multiplexing mode; therefore, the mode selection vector x... m =2, channel allocation vector S m ∈Cell. Thus, the channel allocation model is shown in (24).

[0133]

[0134] Its constraints are consistent with (14). This paper designs a novel hybrid genetic algorithm to solve this problem. The hybrid genetic algorithm is a stochastic global search optimization algorithm. It starts from the initial population and generates a group of individuals that are more suitable for the environment through crossover and mutation operations. The specific process is as follows:

[0135] 1. Population initialization based on Black Widow

[0136] a) Generate the first generation population

[0137] First, a randomly arranged channel allocation matrix Cpop is generated, with elements j∈cell and a size of M×N.

[0138] b) Reproductive stage

[0139] The fitness of each individual in the population, i.e., each channel allocation scheme, is calculated according to equation (14). Then, the individuals in the population are ranked according to their fitness. Based on the reproductive rate PP, the first-generation parents Cpop(:,x1) and Cpop(:,x2) participating in reproduction are selected. The offspring Cpop1(:,y1) and Cpop1(:,y2) are produced by crossbreeding the parents and mothers through the λ value. Specifically, as follows: Figure 4 As shown.

[0140] In the diagram, Cpoint = round(λ*M), where λ is any random number between [0,1]. With the reproductive rate PP set to 0.8, the size of the first generation population Cpop is M*PP*N, which is the same as the size of the offspring population.

[0141] c) Cannibalism

[0142] Cannibalism in black widow spiders is divided into sexual cannibalism and sibling cannibalism. Sexual cannibalism refers to the female black widow eating the male spider during mating. Sex can be determined by fitness; the more fit spider will be female. If Cpop(:,x1) has a better fitness than Cpop(:,x2), then Cpop(:,x1) will remain as female. After sexual cannibalism, the size of the first generation population becomes M*0.5*PP*N. Sibling cannibalism occurs when a portion of the offspring are eliminated based on the cannibalism rate CB. Again, the offspring's strength is determined by fitness. In this case, the size of the offspring population Cpop1 becomes M*(PP*N*CB*0.5+PP*N*(1-CB)). In this paper, CB is set to 0.5.

[0143] d) Mutation

[0144] The mutation phase refers to randomly selecting PT*N black widows from the first-generation population Cpop according to the mutation rate PT, with the mutation rate set to 0.4. During the mutation process, each black widow randomly swaps two values ​​in an M-dimensional array to achieve the mutation effect. Then, the mutated individuals are combined into a new population Cpop2. The mutation process is as follows: Figure 5 As shown:

[0145] Figure 5 In this case, j1 and j2 are two positions swapped in an M-dimensional array, which are any distinct integers between [1, M]. The new population size is M*PT*N.

[0146] e) Update the population

[0147] The surviving population Cpop1 after cannibalism and the mutated new population Cpop2 are combined to form a new population pop, whose size remains M×N.

[0148] 2. Crossover and Mutation Based on Tabu Search

[0149] In traditional genetic algorithms, the crossover operator can generate new individuals in the population, improving global search capability. The core idea of ​​tabu search is to avoid repeating solutions already searched, thus improving search efficiency. Therefore, this paper proposes a crossover operator based on tabu search, combining the advantages of both. The specific steps are as follows:

[0150] a) Establish a taboo search list

[0151] The fitness of each individual in the population pop is calculated according to Equation (14), and then the fitness is sorted. The individual with the best fitness is added to the tabu search table Tabu. After that, the best solution in each generation is added to the tabu table to improve the search efficiency.

[0152] b) Set the crossover probability and mutation probability

[0153] This article sets an upper bound on the crossover probability. The lower bound of the crossover probability is 0.85. The value is 0.6, and the upper and lower limits of the mutation probability are respectively... and To improve the quality of the population, the crossover and mutation probabilities after introducing an adaptive strategy are shown in equations (25) and (26).

[0154]

[0155]

[0156] Where l is the current iteration number, l max This represents the maximum number of iterations.

[0157] c) Cross-tabulation based on tabu search

[0158] Population involves individuals interacting with each other based on a certain crossover probability P. c Crossover is performed, which involves swapping a specific position in the M-dimensional array of each individual. A value V1 is assigned to the population, which is any value between [0,1]. If V1 < P... c Then, crossover is performed on every two individuals in the population, and the crossover process is similar to... Figure 2 Similarly, if the new individuals generated after crossover belong to the taboo list Tabu, then crossover is performed again; otherwise, the new individuals are retained and added to the new population pop1. If crossover is not performed, the original individuals are retained and added to the new population.

[0159] d) Taboo-based mutation

[0160] According to the mutation probability P m Mutate each individual in the pop1 population by randomly swapping two positions in the M-dimensional array of each individual. Similarly, assign a V2 value to each individual in the population, which is any value between [0,1]. If V2 < P... m Then, the mutation operation is initiated on individuals within the population. The mutation operation is related to... Figure 3 Similarly, if the mutated individual is the same as the individual in Tabu, the mutation operation is restarted; otherwise, the newly generated individual is retained as an individual in the new population pop2.

[0161] 3. Update the population and taboo lists.

[0162] Calculate the fitness of each individual in the original population pop and each individual in the new population pop2. Then compare the fitness of individuals in the original population and individuals in the new population. Keep the individuals with better fitness and add them to newpop. Finally, sort them according to fitness to obtain the optimal channel allocation scheme and add the optimal channel allocation scheme to the tabu list. Start iterating until the maximum number of iterations is reached.

[0163] In another aspect, embodiments of the present invention also disclose a cellular network D2D communication resource allocation system, comprising:

[0164] Throughput calculation module: used to receive D2D user-related variables, input the D2D user-related variables into a pre-built system user throughput model to obtain the system user throughput, wherein the D2D user-related variables include mode selection variables, power control variables and channel allocation vector;

[0165] Judgment module: used to determine the system user throughput based on the throughput optimization principle and determine the optimal D2D communication mode; wherein the throughput optimization principle aims to maximize the system user throughput;

[0166] Transmit power module: used to solve the signal-to-noise ratio of D2D users in the optimal mode of D2D communication, inputting the D2D user signal-to-noise ratio into a pre-established power optimization model to obtain the optimal transmit power of D2D users;

[0167] Channel allocation module: Used to input the optimal transmit power of D2D users into a pre-established channel allocation model to obtain the optimal channel allocation result.

[0168] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0169] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0170] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0171] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A method for allocating D2D communication resources in a cellular network, characterized in that, The method includes the following steps: Receive D2D user-related variables and input them into a pre-built system user throughput model to obtain system user throughput. The D2D user-related variables include mode selection variables, power control variables, and channel allocation vectors. The system user throughput is determined based on the principle of optimal throughput to identify the optimal D2D communication mode; wherein the principle of optimal throughput aims to maximize the system user throughput. The signal-to-noise ratio (SNR) of D2D users is solved in the optimal D2D communication mode. The SNR of D2D users is then input into a pre-established power optimization model to obtain the optimal transmit power of D2D users. The optimal transmit power of the D2D user is input into the pre-established channel allocation model to obtain the optimal channel allocation result.

2. The cellular network D2D communication resource allocation method according to claim 1, characterized in that, The communication modes adopted by the D2D users include multiplexing mode and dedicated mode.

3. The method for allocating D2D communication resources in a cellular network according to claim 2, characterized in that, In the dedicated mode, D2D communication selects a dedicated channel for communication. In the dedicated mode, there is no interference between D2D users and cellular users. However, there may be multiple D2D users occupying the same channel. In the multiplexing mode, D2D users are not only affected by interference from cellular users, but also by interference from D2D users multiplexing the same channel.

4. The cellular network D2D communication resource allocation method according to claim 3, characterized in that, When the When a D2D user is in dedicated mode, the first D2D user occupies the space. For each cellular uplink channel resource, since channels are pre-allocated to cellular users, dedicated mode channels should be selected from idle channels. , Mode selection vector The signal-to-noise ratio of D2D users in dedicated mode is as follows: In the formula, and They represent the first For D2D users, the path loss gain and the first For D2D user transmitter to the first For the path loss gain at the D2D user receiver, Indicates the transmit power of D2D users. Indicates the first Gaussian noise at the D2D user receiver.

5. A method for allocating D2D communication resources in a cellular network according to claim 4, characterized in that, When the When a D2D user is in reuse mode, the reuse of the first D2D user... For each cellular channel, since it is a multiplexed cellular user channel, the multiplexing mode channel should be selected from the set of channels pre-allocated to cellular users. Therefore, at this time... Mode selection vector In the reuse mode, the signal-to-noise ratio of D2D users is as follows: In the formula, This indicates the transmit power of cellular users, and For fixed values, Indicates the first The transmitter of the cellular user to the first For the path loss gain at the D2D user receiver, Indicates the first Gaussian noise at the receiver of a cellular user; When the SINR of each cellular user As shown in the following formula: In the formula, Indicates the first For D2D users, the transmitter to the first The path loss gain of the receiver for each cellular user Indicates the first Path loss gain from the transmitter to the base station for each cellular user; When cellular users communicate, they are subject to interference from D2D user transmitters that are sharing the same channel.

6. The method for allocating D2D communication resources in a cellular network according to claim 5, characterized in that, The throughput of D2D users and cellular users is expressed using the Shannon formula as follows: The specific formula for the path loss gain model is shown below. In the formula This represents the constant coefficient of path loss attenuation. Indicates the path loss attenuation index. This represents the distance between the transmitter and receiver along the transmission path. To meet quality of service constraints, the minimum signal-to-noise ratio (SNR) must be achieved for both D2D and cellular users. and To verify the system throughput performance, the objective function is defined as follows: In the above formula Indicates the first D2D user transmit power, Indicates the first Channel allocation results for each D2D user Indicates the first A pattern selection vector for a D2D user The D2D user pair set is Cellular user set is , Indicates the first A D2D user in the mode selection vector Lower occupation D2D user throughput of channel resources Indicates the first Throughput per cellular user; The constraints of the objective function include: power constraints, mode selection vector, channel allocation vector for each D2D user, and signal-to-noise ratio range limits that satisfy the quality of service constraints.

7. A method for allocating D2D communication resources in a cellular network according to claim 1, characterized in that, The process of determining the optimal D2D communication mode by judging the system user throughput based on the principle of optimal throughput is as follows: For any i One D2D user, The mode selection process is as follows: When the D2D user is in dedicated mode, the computation , ; When the D2D user is in reuse mode, computation ; like If the following conditions are met, then the D2D user adopts the reuse mode; otherwise, the dedicated mode is adopted. in This represents the maximum interference threshold for D2D users. Indicates the first The transmitter of the cellular user to the first For the path loss gain at the D2D user receiver, Indicates the transmit power of cellular users. Indicates the first The transmitter of the D2D user to the first For the path loss gain at the D2D user receiver, Indicates the transmit power of D2D users. , Indicates the first Channel allocation results for each D2D user Indicates the first Channel allocation results for each D2D user This represents a D2D user pair set.

8. A method for allocating D2D communication resources in a cellular network according to claim 1, characterized in that, The power optimization model is shown below: In the above formula Indicates the first D2D user transmit power, Indicates the first Channel allocation results for each D2D user Indicates the first A pattern selection vector for a D2D user The D2D user pair set is , Indicates the first A D2D user in the mode selection vector Lower occupation D2D user throughput of channel resources.

9. A method for allocating D2D communication resources in a cellular network according to claim 1, characterized in that, The channel allocation model is as follows: In the above formula Indicates the first The channel allocation results for each D2D user are shown. Since the channel allocation optimization in this paper is performed in multiplexing mode, the mode selection vector is... , D2D user pairs set is Cellular user set is , Indicates the first A D2D user occupies space in reuse mode D2D user throughput of channel resources.

10. A cellular network D2D communication resource allocation system, characterized in that, include: Throughput calculation module: used to receive D2D user-related variables, input the D2D user-related variables into a pre-built system user throughput model to obtain the system user throughput, wherein the D2D user-related variables include mode selection variables, power control variables and channel allocation vector; Judgment module: used to determine the system user throughput based on the throughput optimization principle and determine the optimal D2D communication mode; wherein the throughput optimization principle aims to maximize the system user throughput; Transmit power module: used to solve the signal-to-noise ratio of D2D users in the optimal mode of D2D communication, inputting the D2D user signal-to-noise ratio into a pre-established power optimization model to obtain the optimal transmit power of D2D users; Channel allocation module: Used to input the optimal transmit power of D2D users into a pre-established channel allocation model to obtain the optimal channel allocation result.

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