Multi-base-station cooperative wireless resource allocation method based on uplink rate segmentation

Abstract

The invention discloses a multi-base-station cooperative wireless resource allocation method based on uplink rate segmentation, and relates to the technical field of electronic communication, an uplink multi-carrier RSMA (Received Signal Moving Average) segments a message of each user onto a plurality of subcarriers and adopts a proper decoding sequence, so that all available degrees of freedom are effectively utilized; in order to deal with non-convexity of a transmission rate maximization problem and reduce centralized calculation burden, a two-stage method is developed: firstly, a low-complexity base station selection method based on a matching game is proposed and is used for segmenting a user set; next, a cooperative distributed scheme is provided, and resource allocation and beam forming tasks are divided to corresponding base stations; a semi-closed form solution of power and subcarrier allocation is derived by adopting continuous convex approximation and dual decomposition technologies, meanwhile, received beam forming is optimized by utilizing fractional programming and an alternating direction multiplier method, and a simulation result verifies the effectiveness of the method in the aspects of transmission rate gain and calculation complexity.

Description

Technical Field

[0001] The present invention relates to the field of electronic communication technology, and in particular to a multi-base station collaborative wireless resource allocation method based on uplink rate division. Background Art

[0002] In recent years, the demand for high-speed data transmission and the widespread adoption of dense wireless networks have led to an increasingly urgent need for technologies that achieve superior resource efficiency. As a cutting-edge multiple access technology in modern multi-antenna communication networks, Rate-Splitting Multiple Access (RSMA) has become a highly anticipated and promising solution due to its ability to dynamically decode partial multi-user interference and treat the remaining interference as noise.

[0003] Specifically, RSMA divides a user's information into multiple sub-information and transmits them using superposition coding, enabling multiple users to access the same orthogonal resource blocks and allowing for arbitrary joint decoding combinations. It cleverly integrates and encompasses existing methods and is an advanced form of spatial division multiple access (i.e., treating interference purely as noise), non-orthogonal multiple access (i.e., fully decoding interference), and orthogonal multiple access (i.e., allocating orthogonal wireless resources to completely avoid interference). With its outstanding capabilities, RSMA occupies a favorable position in addressing many emerging challenges in the 6G field and has therefore attracted widespread attention from industry and academia.

[0004] However, implementing RSMA in wireless networks involves several key issues, particularly interference management and decoding order design during information transmission. Furthermore, current research on uplink RSMA is primarily based on the preconditions of a single cell, focusing on mitigating intra-cell interference.

[0005] On the other hand, due to the severe shortage of spectrum resources, the instantaneous capacity of information transmission faces enormous challenges. One effective strategy to address this issue is to achieve spatial frequency reuse in multi-cell networks by deploying dense base stations, rather than dedicating independent spectrum to each cell. When transmitting simultaneously on the same frequency band, the transmission power not only affects the target receiver but also causes co-channel interference to non-target receivers. Therefore, to overcome the interference control issues caused by frequency reuse, multi-cell networks must implement efficient resource management and interference coordination to ensure reliable end-to-end communication.

[0006] Although existing work has made many improvements to dense cell resource management methods, most research still focuses on single-antenna, single-carrier and traditional multiple access scenarios, which cannot fully utilize the advantages of dense spectrum reuse. Therefore, how to maximize the uplink transmission service rate in multi-cell cellular systems has become an urgent problem to be solved. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention provides a multi-base station cooperative wireless resource allocation method based on uplink rate division, comprising the following steps:

[0008] S1. Establish an uplink RSMA transmission model;

[0009] S2. Optimization issues and constraints for establishing a multi-cell uplink communication system;

[0010] S3. Globally divide the user set and determine which base station each user uploads to;

[0011] S4. Perform distributed solution on wireless resource allocation scheme;

[0012] S5. Build a multi-cell distributed collaboration framework based on uplink RSMA.

[0013] The technical solution further defined in the present invention is:

[0014] Furthermore, in step S1, the uplink RSMA transmission model includes I multi-antenna base stations and K cellular users, and is equipped with Nr receiving antennas. Each single-antenna user communicates with a specific base station of the receiving antenna, and the entire spectrum is divided into N orthogonal subcarriers. In the uplink RSMA, each user terminal splits its message into M parts and transmits them to the access base station simultaneously. Subsequently, the base station uses serial interference cancellation technology to decode the submessages of all users in a predetermined decoding order. The sets of users, carriers, submessages, base stations, and antennas are defined in sequence as follows: and

[0015] Define the mth sub-information of user k as (k, m), define the nth sub-carrier of base station i as (i, n), and let The signal to be transmitted for sub-information (k, m) on (i, n) is:

[0016]

[0017] in, Represents the (k, m)th sub-information on the (i, n)th subcarrier, and satisfies express The transmission power;

[0018] The channel information of user k on the (i, n)th subcarrier is defined as Then the received signal of base station i on carrier n is expressed as:

[0019]

[0020] in, represents the receive beamforming, z i，n Indicates power is Gaussian white noise.

[0021] As described above, in a multi-base station cooperative wireless resource allocation method based on uplink rate division, in step S1, base station i uses continuous interference cancellation to decode all sub-information in the received signal. The entire multi-cell system has L=K×M sub-information, where the number of sub-information on the (i, n)th subcarrier is L. i，n satisfy

[0022] definition For all possible decoding orders of all sub-information, and the decoding order optimization variable where ψ I，N represents the decoding order of the sub-information on the (i, n)th carrier, that is, And the decoding order of the mth sub-information of user k on the (i, n)th carrier is The base station decodes and removes the packets with higher priority in the order of priority. sub-messages, and the remaining sub-messages are considered as interference; therefore, the sub-messages The signal-to-interference-noise ratio is expressed as:

[0023]

[0024] Among them, h j，i， n represents the channel information of user j on the (i, n)th carrier, represents the transmission power of the (j, l)th sub-information on the (i, n)th subcarrier; Indicates that the decoding order on subcarrier (i, n) is later than The set of remaining sub-messages of represents the intra-cell interference power suffered by the sub-information (k, m) on the (i, n)th subcarrier, and

[0025]

[0026] Therefore, based on formula (3), the spectrum efficiency of user k is written as:

[0027]

[0028] in, Indicates a sub-message signal-to-interference-noise ratio.

[0029] As described above, in a multi-base station cooperative wireless resource allocation method based on uplink rate division, in step S2, it is set that each user can only select one base station to access, and the transmission power The following formula must be satisfied:

[0030]

[0031] Where sgn(·) represents the sign function; each sub-information can only occupy a single subcarrier. This constraint is expressed as:

[0032]

[0033] Assume that the maximum transmission power of each base station is P max , the non-negative constraint of power allocation is expressed as:

[0034]

[0035] The receive beamforming at the base station is expressed as a unit constant modulus constraint:

[0036]

[0037] The minimum transmission rate r for each user k Need to meet:

[0038]

[0039] in, Denotes the minimum transmission rate threshold of the kth user; define d k，m is a predefined non-negative value used to determine the split ratio of the m-th sub-message, and has the following constraints:

[0040]

[0041] The selection constraints of the decoding order are expressed as:

[0042]

[0043] By jointly optimizing base station selection, decoding order, carrier allocation, and power control to maximize the system sum rate, the objective function is expressed as

[0044] As described above, in a multi-base station cooperative wireless resource allocation method based on uplink rate division, in step S3, an auxiliary variable y=[y 1，1 ,y 1，2,...,y K，I ]∈{0,1} KI , where y k，i =1 means that the kth user accesses the i-th base station. The constraint shown in formula (6) can be rewritten as:

[0045]

[0046] A matching game is performed, taking the priority lists of base stations and user terminals as input data and outputting matching relationships. In the initial stage, the association object of all user terminals is set to the nearest base station.

[0047] make represents the average channel norm of the kth user terminal on all subcarriers at the i-th base station, In each game interaction, the central unit comprehensively considers The base station is selected for the user based on the number of users in the current cell. The base station selection for the user terminal to be connected is designed based on the following criteria:

[0048]

[0049] in, Represents the user set in base station i, and the user cooperation vector y is obtained according to the above formula.

[0050] As described above, in a multi-base station cooperative wireless resource allocation method based on uplink rate division, in step S4, a given base station selects y, and each base station is responsible for its own resource allocation design in a distributed manner; first, a continuous slack variable is introduced And rewrite the constraints shown in formula (7) as follows:

[0051]

[0052] When the receive beamforming W i When the decoding order is given, the joint optimization problem of power control and carrier allocation under the i-th base station is expressed as:

[0053]

[0054] st(8),(10),(12),(16)

[0055] The objective function of the above problem is further expressed as:

[0056]

[0057] in, Indicates that the decoding order on carrier (i, n) is not less than The sub-information set of

[0058] Assign higher data rates to sub-messages with higher priority; by utilizing priority index and introducing slack variables The problem shown in formula (17) can be rewritten as follows:

[0059]

[0060] The solution to the above problem lies in optimizing the coupling between variables and Non-concavity; for linear products The log-exponential reconstruction method is used to and Rewritten as and Then Further rewritten as:

[0061]

[0062] because and about and is a joint convex function, and a continuous convex approximation method is used to iteratively approximate Approximation is made, so that:

[0063]

[0064] and Expressed as:

[0065]

[0066] in, express about and constant gradient of ;

[0067] By combining equations (20) to (22) to replace The problem shown in Equation (19) is approximated as a convex lower bound maximization problem, whose optimal solution is unique. The problem is solved by iterative updating until the lower bound is no longer tightened, and the Lagrangian dual decomposition theory is used to solve the problem.

[0068] As described above, in the multi-base station cooperative wireless resource allocation method based on uplink rate division, in step S4, the Lagrangian dual decomposition theory is used to solve the problem shown in formula (19), and the corresponding Lagrangian function is expressed as:

[0069]

[0070] Among them, μ i ,λi , ρ i , v i represents the Lagrange multiplier; the objective function of the dual problem is expressed as:

[0071]

[0072] Among them, v i satisfy The dual problem is simplified to:

[0073]

[0074] in, According to the Carlo-Kuhn-Tucker conditions, the dual problem is solved by an iterative algorithm based on gradient descent.

[0075] As described above, in a multi-base station cooperative wireless resource allocation method based on uplink rate segmentation, in step S4, according to the Carlo-Kuhn-Tucker condition, the dual problem is solved by an iterative algorithm based on gradient descent. First, the Lagrangian function is calculated. The gradient of is:

[0076]

[0077] in,

[0078] information The iterative process of the transmit power is expressed as:

[0079]

[0080] Where (e) and ξ represent the iteration index and step size respectively;

[0081] Then, the carrier allocation is expressed as:

[0082]

[0083] The (k, m)th message is assigned the maximum The corresponding carrier (i, n), that is:

[0084]

[0085] The subgradient method is used to solve the minimization of the dual problem of each base station as shown in Equation (25); the Lagrangian dual variables are updated according to the following formula:

[0086]

[0087] Among them, (u) is the iteration index, Represents positive step length; projection operator In the above formula, it is defined as the feasible lower bound.

[0088] As described above, in a multi-base station cooperative wireless resource allocation method based on uplink rate division, in step S4, a receive beamforming method is designed. First, define and Will Rewritten as:

[0089]

[0090] This constitutes the following optimization problem:

[0091]

[0092]

[0093] By using the Lagrange dual transformation, the objective function of the above formula can be rewritten as:

[0094]

[0095] Introducing auxiliary variables definition

[0096] By reintroducing auxiliary variables and applying the quadratic transformation, the above formula can be rewritten as:

[0097]

[0098] in, express The conjugate of The optimal solution of is obtained by taking the first-order partial derivative of the above formula, namely:

[0099]

[0100] Substituting Equation (38) into the problem shown in Equation (35), we can get i The problem form is expressed as:

[0101]

[0102] in, The alternating direction multiplication method is used to solve the above problem.

[0103] As described above, in a multi-base station cooperative wireless resource allocation method based on uplink rate division, in step S4, the alternating direction multiplier method is used to solve the problem shown in equation (39). First, w im Introduce auxiliary variable q i，n, rewrite the problem shown in formula (39) into the form of augmented Lagrangian function, and we have:

[0104]

[0105] in, represents the possible values ​​of the beamforming variable; and denote the real and imaginary parts of the Lagrange multiplier respectively; express The indicator function of

[0106] The problem is decomposed into multiple local problems by the alternating direction multiplier method, and the update of each variable is expressed as follows:

[0107]

[0108] in, t represents the iteration index, κ is the step size; since The convexity of The updated solution is:

[0109]

[0110] Then, by removing irrelevant beam vectors, Equation (42) is derived as:

[0111]

[0112] in, Indicates (·) projection on;

[0113] Finally, based on the joint optimization of the channel gain and rate split ratio of the user terminal, the mathematical representation of the optimal decoding order is achieved through the preset decoding order optimization criterion. The specific decoding order optimization criterion is: for all sub-messages on the (i, n)th subcarrier, the approximate optimal decoding order is according to Sort in descending order.

[0114] The beneficial effects of the present invention are:

[0115] In the present invention, by dividing the message stream of each user terminal into multiple sub-messages and allocating differentiated rates, the joint optimization of user association, power allocation, subcarrier scheduling, decoding order and receive beamforming in the multi-cell multi-carrier system is achieved, thereby maximizing the system and rate. Not only is a new theoretical framework for uplink multi-dimensional resource allocation constructed, but also through multi-domain collaborative optimization such as space-frequency-power-decoding, the superiority of RSMA technology in complex interference environments is verified, thereby effectively improving the throughput performance of the cellular system, and at the same time having obvious advantages in reducing computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0117] Figure 2 It is a system model diagram of the present invention;

[0118] Figure 3 Schematic diagram of the curve showing the changes in the number of carriers and the system and rate in the simulation experiment results of the embodiment of the present invention;

[0119] Figure 4 The figure is a schematic diagram showing the relationship between different numbers of users and the system sum rate in the simulation experiment results of an embodiment of the present invention. DETAILED DESCRIPTION

[0120] The method of this embodiment mainly designs a collaborative distributed multi-cell multicarrier RSMA (CDMM-RSMA) resource allocation method. In this method, by dividing the message stream of each user terminal into multiple sub-messages and allocating differentiated rates, the joint optimization of user association, power allocation, subcarrier scheduling, decoding order and receive beamforming in the multi-cell multicarrier system is achieved, thereby maximizing the system sum rate. The method of this embodiment not only constructs a new theoretical framework for uplink multi-dimensional resource allocation, but also verifies the superiority of RSMA technology in complex interference environments through multi-domain collaborative optimization such as space-frequency-power-decoding.

[0121] This embodiment provides a multi-base station cooperative wireless resource allocation method based on uplink rate division, such as Figure 1 As shown, the following steps are included:

[0122] S1. Establish an uplink RSMA transmission model, such as Figure 2 As shown in FIG, there are I multi-antenna base stations and K cellular users. Each single-antenna user communicates with a specific base station equipped with Nr receiving antennas, and the entire spectrum is divided into N orthogonal subcarriers. In the uplink RSMA, each user terminal splits its message into M parts and transmits them to the access base station simultaneously. Subsequently, the base station uses serial interference cancellation technology to decode the submessages of all users in a predetermined decoding order. The sets of users, carriers, submessages, base stations, and antennas are defined in sequence as follows: as well as

[0123] Define the mth sub-information of user k as (k, m), define the nth sub-carrier of base station i as (i, n), and let The signal to be transmitted for sub-information (k, m) on (i, n) is:

[0124]

[0125] in, Represents the (k, m)th sub-information on the (i, n)th subcarrier, and satisfies express The transmission power of user k is defined as Then the received signal of base station i on carrier n is expressed as:

[0126]

[0127] in, represents the receive beamforming, z i，n Indicates power is Gaussian white noise.

[0128] Next, base station i uses continuous interference cancellation to decode all sub-information in the received signal. The entire multi-cell system has L=K×M sub-information, where the number of sub-information on the (i, n)th subcarrier is L. i，n satisfy definition For all possible decoding orders of all sub-information, and the decoding order optimization variable where ψ I，N represents the decoding order of the sub-information on the (i, n)th carrier, that is, And the decoding order of the mth sub-information of user k on the (i, n)th carrier is

[0129] The base station decodes and removes the packets with higher priority in the order of priority. sub-messages, and the remaining sub-messages are considered as interference; therefore, the sub-messages The signal-to-interference-noise ratio is expressed as:

[0130]

[0131] Among them, h j，i，n represents the channel information of user j on the (i, n)th carrier, represents the transmission power of the (j, l)th sub-information on the (i, n)th subcarrier; Indicates that the decoding order on subcarrier (i, n) is later than The set of remaining sub-messages of represents the intra-cell interference power suffered by the sub-information (k, m) on the (i, n)th subcarrier, and

[0132]

[0133] Therefore, based on formula (48), the spectral efficiency of user k is written as:

[0134]

[0135] in, Indicates a sub-message signal-to-interference-noise ratio.

[0136] S2. Optimization problems and constraints for establishing a multi-cell uplink communication system; considering that each user can only choose one base station to access, The following formula must be satisfied:

[0137]

[0138] Here, sgn(·) represents the sign function.

[0139] In order to reduce the decoding complexity of the receiver, each sub-information can only occupy a single subcarrier. This constraint is expressed as:

[0140]

[0141] Assume that the maximum transmission power of each base station is P max , the non-negative constraint of power allocation is expressed as:

[0142]

[0143] The receive beamforming at the base station is expressed as a unit constant modulus constraint:

[0144]

[0145] The minimum transmission rate r for each user k Need to meet:

[0146]

[0147] in, Denotes the minimum transmission rate threshold of the kth user; define d k，m is a predefined non-negative value used to determine the split ratio of the m-th sub-message, and has the following constraints:

[0148]

[0149] The selection constraints of the decoding order are expressed as:

[0150]

[0151] By jointly optimizing base station selection, decoding order, carrier allocation, and power control to maximize the system sum rate, the objective function is expressed as

[0152] S3. Give a global partitioning method for the user set to determine which base station each user uploads to; first define an auxiliary variable y = [y 1，1 ,y 1，2 ,...,y K，I ]∈{0,1} KI , where y k，i =1 means that the kth user accesses the i-th base station. The constraint shown in formula (51) is rewritten as:

[0153]

[0154] Taking into account signaling overhead and computational complexity, this embodiment designs a distributed low-complexity user association method. Specifically, a matching game scheme called "instantaneous acceptance" is proposed. This scheme uses the priority lists of base stations and user terminals as input data and outputs matching relationships; in the initial stage, the association object of all user terminals is set to the nearest base station.

[0155] make represents the average channel norm of the kth user terminal on all subcarriers at the i-th base station, In each game interaction, the central unit comprehensively considers The base station is selected for the user based on the channel gain and the number of users in the current cell. This is because under unit transmit power conditions, high channel gain means that the user terminal can obtain a higher average signal-to-interference-and-noise ratio and low interference from other cells.

[0156] In addition, a smaller number of active user terminals in each cell helps alleviate competition for wireless spectrum resources, especially for user terminals at the cell edge. Therefore, the base station selection for user terminals to access is designed based on the following criteria:

[0157]

[0158] in, Represents the user set in base station i, and the user cooperation vector y is obtained according to the above formula.

[0159] S4. Design a distributed solution for wireless resource allocation. Given a base station selection y, each base station is responsible for its own resource allocation design in a distributed manner. First, introduce the continuous slack variable And rewrite the constraints shown in formula (52) as follows:

[0160]

[0161] When the receive beamforming W i When the decoding order is given, the joint optimization problem of power control and carrier allocation under the i-th base station is expressed as:

[0162]

[0163] st(53),(55),(57),(61)

[0164] The objective function of the above problem is further expressed as:

[0165]

[0166] in, Indicates that the decoding order on carrier (i, n) is not less than The sub-information set of

[0167] Considering that the proportional coefficients of each sub-message in the same user terminal are predetermined, a higher data rate can be allocated to the sub-message with higher priority; by utilizing the priority index and introducing the slack variable The problem shown in formula (62) can be rewritten as follows:

[0168]

[0169] The solution to the above problem mainly lies in optimizing the coupling between variables and The non-concavity of .

[0170] For linear products The log-exponential reconstruction method is used to and Rewritten as and Then Further rewritten as:

[0171]

[0172] because and about and is a joint convex function, and a continuous convex approximation method is used to iteratively approximate Approximation is made, so that:

[0173]

[0174] Head Expressed as:

[0175]

[0176] in, express about and The constant gradient of .

[0177] Therefore, by combining equations (65) to (67) to replace The problem shown in Equation (64) is approximated as a convex lower bound maximization problem, whose optimal solution is unique and is updated iteratively until the lower bound is no longer tightened. It is worth noting that although this problem can be solved by a standard convex optimization solver, the polynomial order complexity brought by the interior point method is still high. Therefore, the Lagrangian dual decomposition theory is considered to solve the problem shown in Equation (64).

[0178] The corresponding Lagrangian function is expressed as:

[0179]

[0180] Among them, μ i ,λ i , ρ i , v i represents the Lagrange multiplier; the objective function of the dual problem is expressed as:

[0181]

[0182] In order to avoid the dual problem going to infinity, v i satisfy Therefore, the dual problem can be simplified to:

[0183]

[0184] in, According to the Carlo-Kuhn-Tucker conditions, an iterative algorithm based on gradient descent is designed to solve the dual problem.

[0185] First calculate the Lagrangian function The gradient of is:

[0186]

[0187] in, Therefore, information The iterative process of the transmit power is expressed as:

[0188]

[0189] Where (e) and ξ represent the iteration index and step size, respectively.

[0190] Then, the carrier allocation is expressed as:

[0191]

[0192] Furthermore, the (k, m)th information is assigned the maximum The corresponding carrier (i, n), that is:

[0193] The subgradient method is used to solve the minimization of the dual problem shown in Equation (70) for each base station.

[0194] The Lagrange dual variables are updated according to the following formula:

[0195]

[0196] Among them, (u) is the iteration index, Represents positive step length; projection operator In the above formula, it is defined as the feasible lower bound.

[0197] Next, we design the receive beamforming method. First, we define and Will Rewritten as:

[0198]

[0199] This constitutes the following optimization problem:

[0200]

[0201] By using the Lagrange dual transformation, the objective function of the above formula can be rewritten as:

[0202]

[0203] Introducing auxiliary variables definition

[0204] By reintroducing auxiliary variables and applying the quadratic transformation, the above formula can be rewritten as:

[0205]

[0206] in, express The conjugate of The optimal solution of is obtained by taking the first-order partial derivative of the above formula, namely:

[0207]

[0208] Substituting Equation (83) into the problem shown in Equation (80), we can get the value of W iThe problem form is expressed as:

[0209]

[0210] in, The above problem belongs to quadratic programming, and the constant modulus constraint increases the difficulty of solving the problem. Consider using the alternating direction multiplier method to solve the above problem.

[0211] First, w i，n Introduce auxiliary variable q i，n , rewrite the problem shown in formula (84) into the form of augmented Lagrangian function, and we have:

[0212]

[0213] in, represents the possible values ​​of the beamforming variable; and denote the real and imaginary parts of the Lagrange multiplier respectively; express indicator function.

[0214] The idea of ​​the alternating direction multiplier method is to decompose a complex problem into multiple smaller local problems. The update of each variable is expressed as follows:

[0215]

[0216] in, t represents the iteration index and κ is the step size.

[0217] because The convexity of The updated solution is:

[0218]

[0219] Then, by removing irrelevant beam vectors, Equation (87) is derived as:

[0220]

[0221] in, Indicates (·) Projection on .

[0222] Finally, a decoding order optimization criterion for multiple sub-messages on the same sub-carrier is proposed. Based on the joint optimization of the channel gain and rate split ratio of the user terminal, the mathematical representation of the optimal decoding order is achieved through the following criterion: For all sub-messages on the (i, n)th sub-carrier, the approximate optimal decoding order is Sort in descending order.

[0223] S5. Construct a multi-cell distributed collaboration framework based on uplink RSMA and propose a multi-base station distributed collaboration scheme based on uplink rate splitting. Its core computational process can be performed locally on each base station. Specifically, the scheme consists of two key parts: a. Parallel user association based on the "instantaneous acceptance" matching game; b. Joint optimization of resource allocation and beamforming implemented alternately in distributed base stations (using the decoding order criterion described above); the update strategies of all base stations will achieve distributed collaboration through continuous interaction until convergence conditions or the maximum number of iterations are reached.

[0224] In this example, simulation experiments are used to verify the algorithm's performance. Assume that there are K users served by M base stations. The path loss varies with distance and is 120.9 + 37.6 lgd, where d is the distance from the user to the base station in kilometers. Shadow fading is represented by a log-normal random variable with zero mean and 8 dB variance. The small-scale fading coefficient follows a Rayleigh distribution with unit variance. Each base station is located at the center of a cell with a radius of 300 meters, and users are evenly distributed within the cell. The experimental parameters are shown in Table 1 below.

[0225] Table 1

[0226]

[0227] Experiment (A): As the transmission power increases, the changes in the system and rate between the method of this embodiment and the baseline method are compared.

[0228] Experiment (B): Comparison of the computational complexity of the baseline method under different parameters, increasing the total number of users from 7 to 25.

[0229] The simulation results of this example are as follows:

[0230] 1. Comparison of the sum rate between the method of this embodiment and the baseline method at different transmit powers:

[0231] From the attached Figure 3 It can be seen that the throughput of all schemes is strictly monotonically increasing with the maximum transmit power; at the same time, the distributed optimization framework proposed in this embodiment combined with the multi-carrier multi-layer sub-information access mechanism has a throughput performance significantly better than the existing benchmark scheme; the distributed optimization framework proposed in this embodiment combined with the multi-carrier multi-layer sub-information access mechanism has a throughput performance significantly better than the existing benchmark scheme.

[0232] Analysis shows that the traditional dual-message RSMA scheme can essentially be viewed as a special case of the CDMM-RSMA architecture in this embodiment. This embodiment implements dynamic power allocation between different sub-messages through a low-complexity power allocation algorithm, thereby expanding the maximum achievable rate domain. Experimental results show that the collaborative wireless resource configuration mechanism proposed in this embodiment has two core advantages: first, it can achieve efficient utilization of system resources; second, compared with the traditional independent single-cell multiple access scheme, it shows a significant performance improvement in system flexibility.

[0233] 2. Comparison of computational complexity between the method in this embodiment and the baseline method under different numbers of users:

[0234] From the attached Figure 4 It can be seen that the computational complexity of the CDMM-RSMA algorithm designed in this embodiment is significantly lower than that of the Riemannian Gradient-Based RSMA (RG-RSMA) and meta-heuristic non-orthogonal multiple access (MH-NOMA) schemes, especially in large-scale access scenarios. For example, when the number of UTs K = 25, the computational complexity can be reduced by a maximum of approximately 100 times. This is due to the fact that the algorithm proposed in this embodiment fully utilizes the (semi-)closed update mechanism, avoiding the large number of iterative operations required by the interior point method or genetic algorithm.

[0235] The method of this embodiment achieves joint optimization of user association, power allocation, subcarrier scheduling, decoding order, and receive beamforming in a multi-cell, multi-carrier system by dividing the message stream of each user terminal into multiple sub-messages and assigning differentiated rates, thereby maximizing the system sum rate. This not only constructs a new theoretical framework for uplink multi-dimensional resource allocation, but also verifies the superiority of RSMA technology in complex interference environments through multi-domain collaborative optimization such as space-frequency-power-decoding, thereby effectively improving the throughput performance of the cellular system and having a significant advantage in reducing computational complexity.

[0236] In addition to the above embodiments, the present invention may also have other implementations. Any technical solution formed by equivalent replacement or equivalent transformation falls within the scope of protection required by the present invention.

Claims

1. A multi-base station cooperative wireless resource allocation method based on uplink rate segmentation, characterized by: The following steps are involved: S1. Establish an uplink RSMA transmission model; S2. Optimization issues and constraints for establishing a multi-cell uplink communication system; S3. Globally divide the user set and determine which base station each user uploads to; S4. Perform distributed solution on wireless resource allocation scheme; S5. Build a multi-cell distributed collaboration framework based on uplink RSMA.

2. The method for allocating wireless resources in multi-base station cooperation based on uplink rate division according to claim 1, characterized in that: In step S1, the uplink RSMA transmission model includes I multi-antenna base stations and K cellular users, and is equipped with N r Each single-antenna user communicates with a specific base station for the receiving antenna, and the entire spectrum is divided into N orthogonal subcarriers; In uplink RSMA, each user terminal splits its message into M parts and transmits them simultaneously to the access base station; Subsequently, the base station uses the serial interference cancellation technology to decode the sub-messages of all users in a predetermined decoding order; the sets of users, carriers, sub-messages, base stations, and antennas are defined as and Define the mth sub-information of user k as (k, m), define the nth sub-carrier of base station i as (i, n), and let The signal to be transmitted for sub-information (k, m) on (i, n) is: in, Represents the (k, m)th sub-information on the (i, n)th subcarrier, and satisfies express The transmission power; The channel information of user k on the (i, n)th subcarrier is defined as Then the received signal of base station i on carrier n is expressed as: in, represents the receive beamforming, z i，n Indicates power is Gaussian white noise.

3. The method for allocating wireless resources for multi-base station cooperation based on uplink rate division according to claim 1, characterized in that: In step S1, base station i uses continuous interference cancellation to decode all sub-information in the received signal. The entire multi-cell system has L=K×M sub-information, where the number of sub-information on the (i, n)th subcarrier is L. i，n satisfy definition For all possible decoding orders of all sub-information, and the decoding order optimization variable where ψ I，N Represents the decoding order of the sub-information on the (i, n)th carrier, that is, And the decoding order of the mth sub-information of user k on the (i, n)th carrier is The base station decodes and removes the packets with higher priority in the order of priority. sub-messages, and the remaining sub-messages are considered as interference; therefore, the sub-messages The signal-to-interference-noise ratio is expressed as: Among them, h j，i，n represents the channel information of user j on the (i, n)th carrier, represents the transmission power of the (j, l)th sub-information on the (i, n)th subcarrier; Indicates that the decoding order on subcarrier (i, n) is later than The set of remaining sub-messages of represents the intra-cell interference power suffered by the sub-information (k, m) on the (i, n)th subcarrier, and Therefore, based on formula (3), the spectrum efficiency of user k is written as: in, Indicates a sub-message signal-to-interference-noise ratio.

4. The method for allocating wireless resources for multi-base station cooperation based on uplink rate division according to claim 2, characterized in that: In step S2, it is set that each user can only select one base station to access, and the transmission power The following formula must be satisfied: Where sgn(·) represents the sign function; each sub-information can only occupy a single subcarrier. This constraint is expressed as: Assume that the maximum transmission power of each base station is P max , the non-negative constraint of power allocation is expressed as: The receive beamforming at the base station is expressed as a unit constant modulus constraint: The minimum transmission rate r for each user k Need to meet: in, Denotes the minimum transmission rate threshold of the kth user; define d k，m is a predefined non-negative value used to determine the split ratio of the m-th sub-message, and has the following constraints: The selection constraints of the decoding order are expressed as: By jointly optimizing base station selection, decoding order, carrier allocation, and power control to maximize the system sum rate, the objective function is expressed as 5. The method for allocating wireless resources in multi-base station cooperation based on uplink rate division according to claim 4, characterized in that: In step S3, an auxiliary variable y=[y 1，1 ,y 1，2 ,...,y K，I ]∈{0,1) KI , where y k，i =1 means that the kth user accesses the i-th base station. The constraint shown in formula (6) can be rewritten as: Perform a matching game, taking the priority lists of base stations and user terminals as input data and outputting matching relationships; In the initial stage, the association object of all user terminals is set to the nearest base station; make represents the average channel norm of the kth user terminal on all subcarriers at the i-th base station, In each game interaction, the central unit comprehensively considers The base station is selected for the user based on the number of users in the current cell. The base station selection for the user terminal to be connected is designed based on the following criteria: in, Represents the user set in base station i, and the user cooperation vector y is obtained according to the above formula.

6. The method for allocating wireless resources in multi-base station cooperation based on uplink rate division according to claim 5, characterized in that: In step S4, a given base station selects y, and each base station is responsible for its own resource allocation design in a distributed manner; first, a continuous slack variable is introduced And rewrite the constraints shown in formula (7) as follows: When the receive beamforming W i When the decoding order is given, the joint optimization problem of power control and carrier allocation under the i-th base station is expressed as: st(8),(10),(12),(16) The objective function of the above problem is further expressed as: in, Indicates that the decoding order on carrier (i, n) is not less than The sub-information set of Assign higher data rates to sub-messages with higher priority; by utilizing priority index and introducing slack variables The problem shown in formula (17) can be rewritten as follows: (8)，(16) The solution to the above problem lies in optimizing the coupling between variables and Non-concavity; for linear products The log-exponential reconstruction method is used to and Rewritten as and Then Further rewritten as: because and about and is a joint convex function, and a continuous convex approximation method is used to iteratively approximate Approximation is made, so that: and Expressed as: in, express about and constant gradient of ; By combining equations (20) to (22) to replace The problem shown in Equation (19) is approximated as a convex lower bound maximization problem, whose optimal solution is unique. The problem is solved by iterative updating until the lower bound is no longer tightened, and the Lagrangian dual decomposition theory is used to solve the problem.

7. The method for allocating wireless resources in multi-base station cooperation based on uplink rate division according to claim 6, characterized in that: In step S4, the Lagrangian dual decomposition theory is used to solve the problem shown in formula (19), and the corresponding Lagrangian function is expressed as: Among them, μ i ,λ i ,ρ i , v i represents the Lagrange multiplier; the objective function of the dual problem is expressed as: Among them, v i satisfy The dual problem is simplified to: in, According to the Carlo-Kuhn-Tucker conditions, the dual problem is solved by an iterative algorithm based on gradient descent.

8. The method for allocating wireless resources for multi-base station cooperation based on uplink rate division according to claim 7, characterized in that: In step S4, according to the Carlo-Kuhn-Tucker condition, the dual problem is solved by an iterative algorithm based on gradient descent. First, the Lagrangian function is calculated. The gradient of is: in, information The iterative process of the transmit power is expressed as: Where (e) and ξ represent the iteration index and step size respectively; Then, the carrier allocation is expressed as: The (k, m)th message is assigned the maximum The corresponding carrier (i, n), that is: The subgradient method is used to solve the minimization of the dual problem of each base station as shown in Equation (25); the Lagrangian dual variables are updated according to the following formula: Among them, (u) is the iteration index, Represents positive step length; projection operator In the above formula, it is defined as the feasible lower bound.

9. The method for allocating wireless resources in multi-base station cooperation based on uplink rate division according to claim 8, characterized in that: In step S4, the receive beamforming method is designed. First, define and Will Rewritten as: This constitutes the following optimization problem: By using the Lagrange dual transformation, the objective function of the above formula can be rewritten as: Introducing auxiliary variables definition By reintroducing auxiliary variables and applying the quadratic transformation, the above formula can be rewritten as: in, express The conjugate of The optimal solution of is obtained by taking the first-order partial derivative of the above formula, namely: Substituting Equation (38) into the problem shown in Equation (35), we can get i The problem form is expressed as: in, The alternating direction multiplication method is used to solve the above problem.

10. The method for allocating wireless resources in multi-base station cooperation based on uplink rate division according to claim 9, characterized in that: In step S4, the alternating direction multiplier method is used to solve the problem shown in equation (39). First, w i，n Introduce auxiliary variable q i，n , rewrite the problem shown in formula (39) into the form of augmented Lagrangian function, and we have: in, represents the possible values ​​of the beamforming variable; and denote the real and imaginary parts of the Lagrange multiplier respectively; express The indicator function of The problem is decomposed into multiple local problems by the alternating direction multiplier method, and the update of each variable is expressed as follows: in, t represents the iteration index, K is the step size; since The convexity of The updated solution is: Then, by removing irrelevant beam vectors, Equation (42) is derived as: in, Indicates (·) projection on; Finally, based on the joint optimization of the channel gain and rate split ratio of the user terminal, the mathematical representation of the optimal decoding order is achieved through the preset decoding order optimization criterion. The specific decoding order optimization criterion is: for all sub-messages on the (i, n)th subcarrier, the approximate optimal decoding order is according to Sort in descending order.

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