Sub-channel allocation method, system and equipment for multi-cell multi-antenna system

Abstract

The invention discloses a sub-channel allocation method, system and equipment for a multi-cell multi-antenna system, which are different from the characteristic that a traditional interference hypergraph is kept static after being constructed, and can dynamically update the interference hypergraph according to the current maximum reachable sum rate of the system in the sub-channel allocation process, so that the interference hypergraph can be dynamically updated, and the sub-channel allocation efficiency is improved. Therefore, accurate modeling of the interference relation between the users can be realized, and the optimization objective of the constructed sub-channel resource optimization model is maximization of the reachability and rate of the system; on the basis, the distribution of the sub-channels can meet the reachability and the maximum rate of the system while the effectiveness of the distribution of the sub-channels is ensured; therefore, low-complexity and high-effectiveness distribution of the sub-channels of the multi-cell multi-antenna system can be realized, the spectrum utilization rate of the system is improved, the interference is reduced, and the network capacity and the user service quality are improved, so that the method has very important application value for resource distribution under the multi-cell multi-antenna system.

Description

Technical Field

[0001] The present invention relates to the technical field of resource optimization in wireless communication systems, and specifically relates to a sub-channel allocation method, system, and device for a multi-cell multi-antenna system. Background Art

[0002] With the rapid development of wireless communication technologies and the continuous growth of user demands, traditional single-cell wireless networks face problems such as insufficient capacity, wasted spectrum resources, and degraded network performance. To address these issues, multi-cell technologies have emerged. By dividing the large coverage area of a single cell into small coverage areas of multiple cells, spectrum efficiency can be improved, interference reduced, and network capacity enhanced, thereby meeting the requirements for large-scale data transmission and device access. At the same time, increasing the number of antennas can not only improve spectrum efficiency but also further enhance the system throughput through beamforming technology. In practical applications, although multi-cell multi-antenna systems have significant advantages in improving spectrum efficiency and system capacity, they still face many challenges. Among them, inter-cell interference (ICI) is a key problem in such systems. Especially at the cell-edge users, due to signal interference from adjacent cells, the channel quality deteriorates significantly, leading to a sharp decline in the performance of edge users. Therefore, to solve this problem, a reasonable interference coordination method needs to be designed for multi-cell multi-antenna systems to reduce interference and improve user rates.

[0003] Among them, inter-cell interference coordination technology suppresses ICI by coordinating sub-channel allocation among multiple cells through sub-channel allocation methods to improve the performance of cell-edge users. Currently, there are some sub-channel allocation methods for multi-cell systems, such as the sub-channel allocation algorithm based on 0-1 integer linear programming. However, it has problems such as high solution difficulty and extremely high computational complexity. The sub-channel algorithm based on matching can solve the sub-channel allocation problem in a low-complexity manner, but its preference list is fixed, which is not conducive to finding a stable matching solution, and this algorithm does not model the interference situation between cells. The sub-channel allocation algorithm based on the interference hypergraph models the inter-cell interference level by constructing an interference hypergraph, improving the effectiveness of sub-channel allocation. However, the method for constructing its interference hypergraph is relatively rough, and it only relies on the initially established interference hypergraph to implement sub-channel allocation. As a result, the accuracy of sub-channel allocation is relatively low. Based on this, how to provide an allocation method that can accurately perform sub-channel allocation has become an urgent problem to be solved. Summary of the Invention

[0004] The technical problem to be solved by the present invention is the low accuracy of sub-channel allocation. The purpose is to provide a sub-channel allocation method, system and device for a multi-cell multi-antenna system, which solves the problem of low accuracy of sub-channel allocation in the traditional technology due to relying on the initially established interference hypergraph to implement sub-channel allocation.

[0005] The present invention is realized through the following technical solutions:

[0006] In the first aspect, a sub-channel allocation method for a multi-cell multi-antenna system is provided, including:

[0007] Construct a sub-channel resource optimization model for the downlink multi-cell multi-antenna system composed of all base stations in the target area, where the sub-channel resource optimization model takes maximizing the achievable sum rate of the downlink multi-cell multi-antenna system as the optimization goal;

[0008] Establish a user interference hypergraph corresponding to the target area, where the user interference hypergraph contains multiple nodes, any node corresponds to a user in the target area, and the users with signal interference relationships are connected by edges;

[0009] According to the user interference hypergraph, determine the sub-channel corresponding to each user in the target area;

[0010] Using the sub-channel corresponding to each user and the sub-channel resource optimization model, calculate the model optimization result, where the model optimization result includes the maximum achievable sum rate of the downlink multi-cell multi-antenna system;

[0011] Judge whether the maximum achievable sum rate in the model optimization result meets the preset condition;

[0012] If not, re-establish the user interference hypergraph corresponding to the target area until the maximum achievable sum rate meets the preset condition, and use the sub-channel of each user corresponding to the maximum achievable sum rate when the preset condition is met as the optimal sub-channel corresponding to each user.

[0013] Based on the above - disclosed content, the present invention first constructs a sub - channel resource optimization model with the optimization objective of maximizing the achievable sum - rate of the downlink multi - cell multi - antenna system composed of all base stations within the target area; then, it establishes an interference hypergraph among all users within the target area; then, according to the constructed interference hypergraph, it determines the sub - channels of each user within the target area; next, based on the sub - channels of each user, it solves the sub - channel resource optimization model to obtain the current maximum achievable sum - rate of the aforementioned downlink multi - cell multi - antenna system; finally, it determines whether the current maximum achievable sum - rate meets the preset conditions; wherein, if it does not meet, it is necessary to re - establish the interference hypergraph among all users and, according to the re - established interference hypergraph, re - determine the sub - channels of each user until it is judged that the current maximum achievable sum - rate meets the preset conditions. At this time, the sub - channels of each user corresponding to the maximum achievable sum - rate that meets the preset conditions can be used as the optimal sub - channels corresponding to each user.

[0014] Through the above design, different from the traditional interference hypergraph which remains static after construction, the present invention can dynamically update the interference hypergraph according to the current maximum achievable sum - rate of the system during the sub - channel allocation process. In this way, it can accurately model the interference relationship among users, and the optimization objective of the constructed sub - channel resource optimization model is to maximize the achievable sum - rate of the system. Based on this, while ensuring the effectiveness of sub - channel allocation, it can make the sub - channel allocation satisfy the maximum achievable sum - rate of the system. Therefore, the present invention can achieve the low - complexity and high - effectiveness allocation of sub - channels in the multi - cell multi - antenna system, which improves the system spectrum utilization rate, reduces interference, and enhances the network capacity and user service quality. Therefore, it has very important application value for resource allocation in the multi - cell multi - antenna system.

[0015] In a possible design, determining the sub - channels corresponding to each user within the target area according to the user interference hypergraph includes:

[0016] Calculating the preference degrees of each user within the target area for each sub - channel in the downlink multi - cell multi - antenna system;

[0017] Based on the preference degrees of each user for each sub - channel, determining the initial matching sub - channels corresponding to each user, and generating an initial channel matching set based on the initial matching sub - channels corresponding to each user;

[0018] Judging whether there is a blocking matching pair in the initial channel matching set, where the blocking matching pair includes two users with incorrect sub - channel matching relationships;

[0019] If so, modify the sub-channel matching relationships between the two users in each blocked matching pair in the initial channel matching set until there is no blocked matching pair in the initial channel matching set, and use the initial channel matching set without blocked matching pairs as the channel matching set;

[0020] Determine the sub-channels corresponding to each user in the target area according to the channel matching set.

[0021] In a possible design, each base station in the target area corresponds to served users. Among them, calculating the preference degrees of each user in the target area for each sub-channel in the downlink multi-cell multi-antenna system includes:

[0022] For any user, obtain the precoding vector assigned by the target base station to the any user, and the channel vector of the any user on any sub-channel, and the target base station is the base station to which the any user belongs;

[0023] According to the precoding vector and the channel vector of the any user on the any sub-channel, calculate the preference degree of the any user for the any sub-channel, and after polling all sub-channels, obtain the preference degrees of the any user for each sub-channel.

[0024] In a possible design, each base station in the target area corresponds to served users. Among them, determining the initial matching sub-channels corresponding to each user based on the preference degrees of each user for each sub-channel includes:

[0025] For any user, sort each sub-channel according to the descending order of the preference degrees of the any user for each sub-channel to obtain a sub-channel preference list;

[0026] Control the any user to send a matching request to the target base station for the i-th sub-channel in the sub-channel preference list, so that after receiving the matching request, the target base station generates matching feedback information according to the user interference hypergraph, where the target base station is the base station to which the any user belongs, and the initial value of i is 1;

[0027] Based on the matching feedback information, determine whether the target base station accepts the matching request sent by the any user;

[0028] If not, increment i by 1, and re-control the any user to send a matching request to the target base station for the i-th sub-channel in the sub-channel preference list until it is determined that the target base station accepts the matching request sent by the any user, and use the i-th sub-channel corresponding to the target base station accepting the matching request sent by the any user as the initial matching sub-channel of the any user.

[0029] In a possible design, determining whether there is a blocking matching pair in the initial channel matching set includes:

[0030] Based on the initial channel matching set, determining the matching user set corresponding to each sub-channel;

[0031] For the matching user set corresponding to the k-th sub-channel and the matching user set corresponding to the j-th sub-channel, determining whether there is a connection edge in the user interference hypergraph between the q-th user in the matching user set corresponding to the k-th sub-channel and the a-th user in the matching user set corresponding to the j-th sub-channel, where k, j, q, and a are all positive integers;

[0032] If not, calculating the first preference degree of the q-th user for the k-th sub-channel and the second preference degree of the q-th user for the j-th sub-channel;

[0033] Determining whether the second preference degree is greater than the first preference degree;

[0034] If so, deleting the q-th user from the matching user set corresponding to the k-th sub-channel and adding the a-th user to obtain a first updated matching user set, and deleting the a-th user from the matching user set corresponding to the j-th sub-channel and adding the q-th user to obtain a second updated matching user set;

[0035] Using the sub-channel preference function, calculating the first channel preference degree of the first updated matching user set and the second channel preference degree of the second updated matching user set, as well as the third channel preference degree of the matching user set corresponding to the k-th sub-channel and the fourth channel preference degree of the matching user set corresponding to the j-th sub-channel;

[0036] Determining whether the sum of the first channel preference degree and the second channel preference degree is greater than the sum of the third channel preference degree and the fourth channel preference degree;

[0037] If so, determining that there is an error in the sub-channel matching relationship between the q-th user and the a-th user, forming a blocking matching pair with the q-th user and the a-th user, and determining that there is a blocking matching pair in the initial channel matching set.

[0038] In a possible design, using the sub-channel preference function, calculating the first channel preference degree of the first updated matching user set includes:

[0039] Using the following formula (1) to calculate the first channel preference degree;

[0040]

[0041] F(Ω′(N k ),N k) represents the first channel preference degree, represents the sub-channel allocation variable of the u-th user in the user set served by the m-th base station, represents the signal-to-interference-plus-noise ratio of the u-th user with respect to the k-th sub-channel N k , B represents the total bandwidth of the downlink multi-cell multi-antenna system, N represents the total number of sub-channels of the downlink multi-cell multi-antenna system, M represents the total number of base stations, and Μ m represents the user set served by the m-th base station, and Ω′(N k ) represents the first updated matching user set. Among them, when the u-th user belongs to the first updated matching user set, it is 1, and when the u-th user does not exist in the first updated matching user set, it is 0.

[0042] In a possible design, the model variables of the sub-channel resource optimization model include the sub-channel allocation variables of each user and the precoding vectors of each user; among them, using the sub-channel corresponding to each user and the sub-channel resource optimization model, the model optimization result is calculated, including:

[0043] Based on the sub-channel corresponding to each user, the sub-channel allocation variables of each user are determined;

[0044] According to the sub-channel allocation variables of each user, the sub-channel resource optimization model is solved to obtain the precoding vectors of each user when the downlink multi-cell multi-antenna system reaches the maximum achievable sum rate;

[0045] Using the maximum achievable sum rate and the precoding vectors of each user when reaching the maximum achievable sum rate, the model optimization result is formed;

[0046] Correspondingly, re-establishing the user interference hypergraph corresponding to the target area includes:

[0047] Using the precoding vectors of each user when reaching the maximum achievable sum rate, re-establish the user interference hypergraph corresponding to the target area.

[0048] In a possible design, constructing the sub-channel resource optimization model of the downlink multi-cell multi-antenna system composed of all base stations in the target area includes:

[0049] Obtain the base station information of the target area;

[0050] According to the base station information, determine the signal-to-interference-plus-noise ratio of each user in the target area;

[0051] Using the signal-to-interference-plus-noise ratio of each user, construct the sub-channel resource optimization model.

[0052] In a second aspect, a sub-channel allocation system for a multi-cell multi-antenna system is provided, including:

[0053] A model construction unit for constructing an optimization model of sub-channel resources of a downlink multi-cell multi-antenna system composed of all base stations in a target area, wherein the sub-channel resource optimization model takes maximizing the achievable sum rate of the downlink multi-cell multi-antenna system as the optimization objective;

[0054] A sub-channel allocation unit for establishing a user interference hypergraph corresponding to the target area, wherein the user interference hypergraph includes a plurality of nodes, any node corresponds to a user in the target area, and users with signal interference relationships are connected by edges;

[0055] The sub-channel allocation unit is used to determine the sub-channel corresponding to each user in the target area according to the user interference hypergraph;

[0056] A calculation unit for calculating a model optimization result by using the sub-channel corresponding to each user and the sub-channel resource optimization model, wherein the model optimization result includes the maximum achievable sum rate of the downlink multi-cell multi-antenna system;

[0057] The sub-channel allocation unit is used to determine whether the maximum achievable sum rate in the model optimization result meets a preset condition;

[0058] The sub-channel allocation unit is further used to, when it is determined that the maximum achievable sum rate in the model optimization result does not meet the preset condition, re-establish the user interference hypergraph corresponding to the target area until the maximum achievable sum rate meets the preset condition, so as to use the sub-channel of each user corresponding to the maximum achievable sum rate when the preset condition is met as the optimal sub-channel corresponding to each user.

[0059] In a third aspect, a sub-channel allocation device based on a multi-cell multi-antenna system is provided. Taking the device as an example of an electronic device, it includes a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the sub-channel allocation method for the multi-cell multi-antenna system as described in the first aspect or any possible design in the first aspect.

[0060] In a fourth aspect, a storage medium is provided, on which instructions are stored. When the instructions run on a computer, they execute the sub-channel allocation method for the multi-cell multi-antenna system as described in the first aspect or any possible design in the first aspect.

[0061] Fifth aspect, a computer program product including instructions is provided, which when running on a computer, causes the computer to execute the sub-channel allocation method for a multi-cell multi-antenna system as described in the first aspect or any one of the possible designs in the first aspect.

[0062] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0063] Different from the traditional interference hypergraph which remains static after construction, in the process of sub-channel allocation, the present invention can dynamically update the interference hypergraph according to the current maximum achievable sum rate of the system. In this way, an accurate modeling of the interference relationship between users can be achieved, and the optimization objective of the constructed sub-channel resource optimization model is to maximize the achievable sum rate of the system. Based on this, while ensuring the effectiveness of sub-channel allocation, the sub-channel allocation can satisfy the maximum achievable sum rate of the system. Therefore, the present invention can achieve a low-complexity and high-effectiveness allocation of sub-channels in a multi-cell multi-antenna system, which improves the system spectrum utilization rate, reduces interference, and enhances the network capacity and user service quality. Therefore, it has very important application value for resource allocation in a multi-cell multi-antenna system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0065] Figure 1 It is a schematic flowchart of the steps of the sub-channel allocation method for a multi-cell multi-antenna system provided by an embodiment of the present invention;

[0066] Figure 2 It is a model diagram of a typical multi-cell multi-antenna system provided by an embodiment of the present invention;

[0067] Figure 3 It is a convergence effect diagram of the sub-channel allocation algorithm provided by an embodiment of the present invention;

[0068] Figure 4 It is an effect diagram of the achievable sum rate of the system varying with power provided by an embodiment of the present invention;

[0069] Figure 5 It is a schematic structural diagram of the sub-channel allocation system for a multi-cell multi-antenna system provided by an embodiment of the present invention;

[0070] Figure 6Schematic diagram of the electronic device provided by the embodiment of the present invention. Detailed implementation manners

[0071] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and do not limit the present invention; it should be understood that although terms such as first and second may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, the first unit may be called the second unit, and similarly, the second unit may be called the first unit, without departing from the scope of the exemplary embodiments of the present invention.

[0072] Embodiment:

[0073] Refer to Figure 1 As shown, for the sub-channel allocation method for a multi-cell multi-antenna system provided in this embodiment, first, a sub-channel resource optimization model is constructed with the optimization objective of maximizing the achievable sum rate of the multi-cell multi-antenna system corresponding to the target area. Then, an interference hypergraph among users within the target area is established. Then, through an iterative method, sub-channel allocation is performed, that is, based on the current interference hypergraph, the current sub-channels of each user are determined, and based on this, model solving is carried out to obtain the maximum achievable sum rate of the multi-cell multi-antenna system at present. Finally, it can be judged whether the current maximum achievable sum rate meets the preset conditions. Among them, if it does not meet, the interference hypergraph among users is re-established, and the foregoing iterative allocation steps are repeated until the current maximum achievable sum rate meets the preset conditions. At this time, the sub-channel allocation result that maximizes the achievable sum rate of the system can be obtained, that is, the optimal sub-channel corresponding to each user can be obtained. Thus, in the process of sub-channel allocation of the present invention, the interference hypergraph can be dynamically updated according to the current maximum achievable sum rate of the system. In this way, accurate modeling of the interference relationship among users can be realized, so as to improve the achievable sum rate of the system as much as possible while ensuring the effectiveness of sub-channel allocation, thereby guaranteeing the quality of service of users. Based on this, this method realizes the allocation of sub-channels of a multi-cell multi-antenna system with low complexity and high effectiveness, improves the system spectrum utilization rate, reduces interference, and enhances the network capacity and user service quality. Therefore, it is very suitable for large-scale application and promotion. Among them, for example, this method can but is not limited to running on the server side. It can be understood that the foregoing execution entity does not constitute a limitation to the embodiments of the present application. Correspondingly, the running steps of this method can but are not limited to the following steps S1 to S6.

[0074] S1. Construct a sub-channel resource optimization model for the downlink multi-cell multi-antenna system composed of all base stations in the target area. Among them, the sub-channel resource optimization model aims to maximize the achievable sum rate of the downlink multi-cell multi-antenna system; in this embodiment, the target area is divided into multiple communication areas, each communication area corresponds to a base station, and each base station corresponds to served users (that is, each base station corresponds to a set of served users). Thus, a downlink multi-cell multi-antenna model in which M base stations serve M communication areas is established (M is a positive integer), and its schematic diagram can be seen in Figure 2 as shown; at the same time, in this embodiment, a sub-channel resource optimization model with the goal of maximizing the achievable sum rate of the downlink multi-cell multi-antenna system is established to allocate sub-channels for each user in the target area.

[0075] Optionally, in this embodiment, a multi-cell multi-antenna orthogonal frequency division multiple access system model is selected as the sub-channel resource optimization model. Among them, one of the construction methods of the sub-channel allocation model is given below, which can be but is not limited to the following steps S11 to S13.

[0076] S11. Obtain the base station information of the target area; in specific applications, for example, the base station information may include but is not limited to the total number of base stations in the target area, the total bandwidth and the total number of sub-channels of the aforementioned downlink multi-cell multi-antenna system, the channel noise power, and the set of users served by each base station; thus, after obtaining the base station information, the signal-to-interference-plus-noise ratio (SINR) of each user can be calculated based on this, and its calculation process is as shown in the following step S12.

[0077] S12. Determine the signal-to-interference-plus-noise ratio (SINR) of each user in the target area according to the base station information; in specific applications, it is assumed that each communication area allocates at most one sub-channel to the users in its area. Therefore, the signal-to-interference-plus-noise ratio (SINR) of the u-th user served by the m-th base station can be calculated by but is not limited to the following formula (2).

[0078]

[0079] In the above formula (2), represents the signal-to-interference-plus-noise ratio (SINR) of the u-th user with respect to the n-th sub-channel, represents the sub-channel allocation variable of the u-th user in the set of users served by the m-th base station, which is 0 or 1. Among them, when it is 1, it means that the m-th base station allocates the n-th sub-channel to the u-th user, when it is 0, it means that the m-th base station does not allocate the n-th sub-channel to the u-th user, M m represents the set of users served by the m-th base station, represents the channel vector of the u-th user in the user set served by the m-th base station on the n-th subchannel (which is a known parameter preset in the server), C represents a matrix of size L×1, and L is the number of antennas of each base station, w m,u ∈C L×1 which represents the precoding vector allocated by the m-th base station for the u-th user, σ 2 represents the channel noise power; similarly, represents the subchannel allocation variable of the u'-th user in the user set served by the m-th base station, represents the subchannel allocation variable of the u''-th user in the user set served by the m'-th base station, w m,u′ represents the precoding vector allocated by the m-th base station for the u'-th user, w m′,u″ represents the precoding vector allocated by the m'-th base station for the u''-th user, M m′ represents the user set served by the m'-th base station, and H represents the conjugate transpose operation.

[0080] Thus, after calculating the signal-to-interference-plus-noise ratio (SINR) of each user based on the foregoing formula (2), the foregoing subchannel resource optimization model can be established based on this. The construction process is as shown in the following step S13.

[0081] S13. Use the SINR of each user to construct the subchannel resource optimization model; in this embodiment, based on the foregoing formula (2) and according to the Shannon formula, the achievable sum rate of the downlink multi-cell multi-antenna system can be expressed as:

[0082]

[0083] In formula (3), R sum represents the achievable sum rate of the downlink multi-cell multi-antenna system, N represents the total number of subchannels of the downlink multi-cell multi-antenna system, and B represents the total bandwidth of the downlink multi-cell multi-antenna system.

[0084] Thus, based on the achievable sum rate of the foregoing system, a resource optimization problem model of the multi-cell multi-antenna system with the optimization objective of maximizing the achievable sum rate of the system and with subchannel allocation and precoding vectors as variables can be established, that is, the foregoing subchannel resource optimization model. Among them, the subchannel resource optimization model can be expressed as:

[0085]

[0086] The above formula (4) represents the sub-channel resource optimization model. It can be seen from this formula that its purpose is to maximize the achievable sum rate of the system, and the model variables include β and w, that is, the sub-channel allocation variables of each user and the precoding vectors of each user. At the same time, formulas (a)-(d) represent the constraint conditions of the model. || || represents the norm operation. represents an arbitrary symbol, P max represents the maximum transmit power of the base station, R min represents the minimum spectral efficiency to meet the user requirements.

[0087] Based on this, the aforementioned formula (a) means that each user can be allocated at most one sub-channel; formula (b) means that the sub-channel allocation variable is a variable of 0 or 1; formula (c) represents the base station transmit power constraint; and formula (d) represents the minimum spectral efficiency constraint of the user.

[0088] It can be known from the aforementioned sub-channel resource optimization model that the optimization of formula (4) is a mixed integer non-convex problem of 0-1 variables, which is an NP-hard problem and difficult to solve within polynomial time. Therefore, to solve this problem, this embodiment decomposes the optimization problem into a sub-channel allocation problem and a precoding vector optimization problem, that is, first given a variable, find another variable, and then, based on the obtained another variable, perform model solving.

[0089] Among them, when the sub-channel allocation variable is given, the aforementioned sub-channel resource optimization model can be changed into a precoding vector design optimization problem, which can be expressed as:

[0090]

[0091] In the above formula, * means that the variable is a known value, that is, the variable is given.

[0092] Similarly, when the precoding vector is given, the solution of the aforementioned sub-channel resource optimization model can be changed into a sub-channel allocation optimization problem, which can be expressed as:

[0093]

[0094] Based on the foregoing description, to maximize the achievable sum rate of the downlink multi-cell multi-antenna system in the target area, it is necessary to solve the sub-channel allocation variables and the precoding vectors allocated by each base station to the users it serves. Therefore, in this embodiment, the precoding vectors of each user are first given, and then, based on this, the complex interference situation among users in multiple communication areas in the target area is modeled to obtain a user interference hypergraph. Then, based on the user interference hypergraph, sub-channel allocation is performed to obtain the sub-channels of the users at present, thereby obtaining the sub-channel allocation variables. Based on this, one variable in the sub-channel allocation model is determined. Then, it can be substituted back into the sub-channel resource optimization model to solve the model, that is, to find the precoding vector that maximizes the achievable sum rate of the system at present. Finally, it is judged whether the maximum achievable sum rate currently reached by the system meets the preset conditions. Among them, if the preset conditions are not met, the obtained precoding vector is used to update the user interference hypergraph in turn, and the foregoing steps are repeated until the maximum achievable sum rate currently reached by the system meets the preset conditions, and the iteration is stopped, so as to obtain the optimal sub-channel allocation result.

[0095] Among them, the construction process of the user interference hypergraph is as shown in the following step S2.

[0096] S2. Establish the user interference hypergraph corresponding to the target area, where the user interference hypergraph includes a plurality of nodes, any node corresponds to a user in the target area, and users with signal interference relationships are connected by edges.

[0097] In specific applications, the user interference hypergraph mainly includes nodes (i.e., vertices), ordinary edges, and hyperedges. Among them, vertices represent users, and ordinary edges and hyperedges represent interference among users. Therefore, in this embodiment, the interference hypergraph is constructed according to the interference among users, and the process is as follows:

[0098] First, the precoding vectors assigned by each base station to the corresponding users are given (i.e., the initial values are given); then, for user u in the user set corresponding to any base station (assuming user u belongs to the m-th base station), user r is selected from the user set corresponding to one of the remaining base stations in the target area (i.e., user u and user r belong to different base stations); then, the independent interference value between user u and user r is calculated; among them, if the independent interference value between user u and user r is less than or equal to the independent interference threshold, user u and user j are connected with an ordinary edge; at the same time, if the independent interference value is greater than the independent interference threshold, then another user b is selected from the user sets corresponding to the remaining base stations (i.e., user u, user r, and user b belong to different base stations); then, the cumulative interference value among user u, user r, and user b is calculated; if the cumulative interference value is less than or equal to the cumulative interference threshold, user u, user r, and user b are connected with a hyperedge; thus, in the above manner, after traversing the user set corresponding to any of the above base stations, the interfering users corresponding to each user served by the any base station can be obtained. Finally, after traversing the user sets corresponding to all base stations, the above user interference hypergraph can be constructed.

[0099] Further, the following discloses the calculation method of the independent interference value. For example, but not limited to, the following formula (7) can be used to calculate the independent interference value between user u and user r.

[0100]

[0101] In the above formula (7), represents the channel vector of the u-th user in the user set served by the m-th base station on the n-th subchannel (which is a known value), and w m,u represents the precoding vector assigned by the m-th base station to the u-th user (at the beginning of the iteration, it is the given initial value); similarly, represents the channel vector of the r-th user in the user set served by the m'-th base station on the n-th subchannel (i.e., user r belongs to the m'-th base station), and w m′,r represents the precoding vector assigned by the m'-th base station to the r-th user, and ε represents the independent interference value between user u and user r.

[0102] Furthermore, for example, but not limited to, the following formula (8) can be used to calculate the cumulative interference value among user u, user r, and user b.

[0103]

[0104] In the above formula (8), ε' represents the cumulative interference value among user u, user r, and user b, represents the channel vector of the b-th user in the user set served by the m″-th base station on the n-th subchannel (i.e., user b belongs to the m″-th base station), and w m″,b represents the precoding vector allocated by the m″-th base station to the b-th user.

[0105] Thus, based on the aforementioned formulas (7) and (8), the independent interference values and cumulative interference values between each user can be calculated. Based on this, an interference hypergraph with users as vertices and interference between users as edges or hyperedges can be constructed.

[0106] After constructing the user interference hypergraph, subchannel allocation can be performed based on this, and the process is as shown in the following step S3.

[0107] S3. According to the user interference hypergraph, determine the subchannels corresponding to each user in the target area; in specific applications, in this embodiment, based on the traditional deferred acceptance algorithm, an improved matching algorithm based on the interference hypergraph is proposed, and the process can be but is not limited to the following steps S31 to S35.

[0108] S31. Calculate the preference degrees of each user in the target area for each subchannel in the downlink multi-cell multi-antenna system; in specific applications, in this embodiment, taking any user as an example for detailed elaboration, the calculation process of the preference degree of each user for each subchannel can be but is not limited to the following steps S31a and S31b.

[0109] S31a. For any user, obtain the precoding vector allocated by the target base station to the any user, and the channel vector of the any user on any subchannel, and the target base station is the base station to which the any user belongs; in this embodiment, as previously described, the precoding vectors allocated by each base station to the corresponding users are given in advance. Therefore, at the beginning of the iteration, the given precoding vector (i.e., the initial value) is used to calculate the preference degree, and the calculation process is as shown in the following step S31b.

[0110] S31b. According to the precoding vector and the channel vector of the any user on the any subchannel, calculate the preference degree of the any user for the any subchannel, and after polling all subchannels, obtain the preference degrees of the any user for each subchannel; in specific applications, for example, the following formula (9) can be used but is not limited to calculate the preference degree of the any user for the aforementioned any subchannel.

[0111]

[0112] In the above formula (9), G(n,z) represents the preference degree of any user z for the any subchannel n, Denote the channel vector of the z-th user in the user set served by the m-th base station on any of the sub-channels. Denote the precoding vector allocated by the m-th base station for the z-th user, M m Denote the user set corresponding to the m-th base station.

[0113] Thus, based on step S31a and step S31b, the preference degrees of any user for each sub-channel can be calculated. Similarly, based on this principle, the preference degrees of each user for each sub-channel can be calculated. Then, based on the preference degrees of each user for each sub-channel, the initial matching sub-channels of each user can be determined, and the process is as shown in the following step S32.

[0114] S32. Based on the preference degrees of each user for each sub-channel, determine the initial matching sub-channels corresponding to each user, and generate an initial channel matching set based on the initial matching sub-channels corresponding to each user. In specific applications, still taking any user as an example for detailed elaboration, the determination process of its corresponding initial matching sub-channel can be but is not limited to the following steps S32a - S32d.

[0115] S32a. For any user, sort each sub-channel in descending order according to the preference degree of the user for each sub-channel to obtain a sub-channel preference list. In this embodiment, it is equivalent to sorting each sub-channel in descending order according to the preference degree, that is, the higher the sorting position, the greater the preference degree.

[0116] After obtaining the sub-channel preference list, channel matching can be started from the first sub-channel in the sub-channel preference list until the base station to which the user belongs agrees to the channel matching request. Among them, the channel matching process is as shown in the following steps S32b - S32d.

[0117] S32b. Control the user to send a matching request for the i-th sub-channel in the sub-channel preference list to the target base station, so that after receiving the matching request, the target base station generates matching feedback information according to the user interference hypergraph, where the target base station is the base station to which the user belongs, and the initial value of i is 1.

[0118] In specific implementation, the server sends a matching instruction (referring to the user terminal) to any one of the users, and the matching instruction contains the object to be matched (i.e., the i-th sub-channel in the preference list). Then, after receiving the matching instruction, any one of the users can generate a matching request for the i-th sub-channel and send it to the target base station. Among them, after receiving the matching request sent by any one of the users, the target base station will obtain the users already matched on the i-th sub-channel as the matching users. Then, it is determined whether there is a connection edge between any one of the users and each of the matching users in the user interference hypergraph. If there is, it means that there is user interference. At this time, a matching feedback message rejecting the matching is generated and sent to the server. If not, a matching feedback message accepting the matching is generated and sent to the server. In this way, the server can determine whether the target base station accepts the matching request sent by any one of the users based on the matching feedback message sent by the target base station, and its judgment process is shown in the following step S32c.

[0119] S32c. Based on the matching feedback message, determine whether the target base station accepts the matching request sent by any one of the users. In this embodiment, if the server determines that the target base station does not accept the matching request sent by any one of the users, at this time, it is necessary to select the second sub-channel from the sub-channel preference list for channel matching, that is, continuously repeat the matching and sending process until the sub-channel corresponding to the matching request accepted by the target base station is found. The loop matching process of the sub-channel is shown in the following step S32d.

[0120] S32d. If not, increment i by 1 and re-control any one of the users to send a matching request for the i-th sub-channel in the sub-channel preference list to the target base station until it is determined that the target base station accepts the matching request sent by any one of the users, so as to use the i-th sub-channel corresponding to the target base station accepting the matching request sent by any one of the users as the initial matching sub-channel corresponding to any one of the users.

[0121] Thus, through the foregoing steps S32a to S32d, the initial matching sub-channel of any one of the users can be determined. In this way, based on this principle, after all users are traversed, the initial matching sub-channels corresponding to each user can be obtained, thereby generating an initial channel matching set.

[0122] After obtaining the initial channel matching set, it is also necessary to determine whether there are blocking matching pairs in the matching set to ensure the accuracy of the matching. The judgment process of the blocking matching pair is shown in the following step S33.

[0123] S33. Determine whether there is a blocking matching pair in the initial channel matching set, where the blocking matching pair includes two users with incorrect sub-channel matching relationships; in this embodiment, assume that the two users included in the blocking matching pair are the first user and the second user respectively. Then, if the preference degree of the first user for the second target sub-channel is greater than the preference degree of the first user for the first target sub-channel, and the preference degree of the second target sub-channel for the first user is greater than the preference degree of the second target sub-channel for the second user, it is determined that the matching relationship between the first user and the second user is incorrect; of course, the first target sub-channel is the initial matching sub-channel corresponding to the first user, and the second target sub-channel is the initial matching sub-channel corresponding to the second user.

[0124] Further, the screening process of the foregoing blocking matching pair is disclosed as shown in the following steps S33a to S33h.

[0125] S33a. Based on the initial channel matching set, determine the matching user set corresponding to each sub-channel; in this embodiment, in the foregoing step S32, the initial matching sub-channel corresponding to each user has been determined. Then, conversely, the user set corresponding to each initial matching sub-channel can be obtained. For example, if the nth sub-channel corresponds to users u, z, t, etc., then users u, z, t form the matching user set of the nth sub-channel; of course, the foregoing example is only illustrative and does not constitute a limitation on the sub-channel matching relationship.

[0126] After obtaining the matching user set corresponding to each sub-channel, take two users in the matching user sets corresponding to any two sub-channels as an example to elaborate on the determination of the blocking matching pair. The specific judgment process is as shown in the following steps S33b to S33h.

[0127] S33b. For the matching user set corresponding to the kth sub-channel and the matching user set corresponding to the jth sub-channel, determine whether there is a connection edge in the user interference hypergraph between the qth user in the matching user set corresponding to the kth sub-channel and the ath user in the matching user set corresponding to the jth sub-channel, where k, j, q, and a are all positive integers; in this embodiment, based on the user interference hypergraph, it is determined whether there is an ordinary edge or a hyper-edge connecting the ath user and the qth user. If so, it means that there is interference between the two users. At this time, a user needs to be reselected from the matching user set corresponding to the jth sub-channel for judgment; of course, if there is no connection edge, the following step S33c needs to be executed.

[0128] S33c. If not, calculate the first preference degree of the q-th user for the k-th sub-channel and the second preference degree of the q-th user for the j-th sub-channel; in this embodiment, the calculation of the preference degree can refer to the foregoing formula (9), and the process will not be elaborated here; after calculating the preference degrees of the q-th user for the k-th sub-channel and the j-th sub-channel respectively, it is necessary to judge the preference degrees, and the process is as shown in the following step S33d.

[0129] S33d. Judge whether the second preference degree is greater than the first preference degree; in specific applications, if the second preference degree is greater than the first preference degree, it means that the q-th user prefers the j-th sub-channel more than the k-th sub-channel (that is, the preference degree of the foregoing first user for the second target sub-channel is greater than the preference degree of the first user for the first target sub-channel); at this time, it is also necessary to calculate the preference degree of the sub-channel for the user, and the process is as shown in the following steps S33e to S33g; of course, if the second preference degree is less than or equal to the first preference degree, it is necessary to reselect a user from the matching user set corresponding to the j-th sub-channel and re-execute the foregoing step S33b.

[0130] S33e. If so, delete the q-th user from the matching user set corresponding to the k-th sub-channel and add the a-th user to obtain the first updated matching user set, and delete the a-th user from the matching user set corresponding to the j-th sub-channel and add the q-th user to obtain the second updated matching user set; in this embodiment, assume that the matching user set corresponding to the k-th sub-channel is represented as Ω(N k ), then, the first updated matching user set can be represented as: Ω′(N k ) = (Ω(N k ) \ K q ∪ K a ); specifically, Ω(N k ) \ K q ∪ K a means deleting the q-th user K k from Ω(N q ), and then adding the a-th user K a ; of course, the representation of the second updated matching user set is also the same, and will not be elaborated here.

[0131] After completing the update of the two matching user sets, it is necessary to use the sub-channel preference function to calculate the channel preference degrees of each matching user set before and after the update, so as to subsequently determine whether the q-th user and the a-th user form a blocking matching pair based on this; among them, the calculation process of the channel preference degree is as shown in the following step S33f.

[0132] S33f. Calculate the first channel preference degree of the first updated matching user set, the second channel preference degree of the second updated matching user set, the third channel preference degree of the matching user set corresponding to the k-th subchannel, and the fourth channel preference degree of the matching user set corresponding to the j-th subchannel by using the subchannel preference function.

[0133] In this embodiment, taking the first updated matching user set as an example, the calculation process of the channel preference degree is described. For example, the following formula (1) can be used to calculate the first channel preference degree.

[0134]

[0135] F(Ω′(N k ),N k ) represents the first channel preference degree. represents the subchannel allocation variable of the u-th user in the user set served by the m-th base station. represents the signal-to-interference-plus-noise ratio of the u-th user with respect to the k-th subchannel N k . B represents the total bandwidth of the downlink multi-cell multi-antenna system, N represents the total number of subchannels of the downlink multi-cell multi-antenna system, M represents the total number of base stations, and Μ m represents the user set served by the m-th base station. Ω′(N k ) represents the first updated matching user set. When the u-th user belongs to the first updated matching user set, is 1. When the u-th user does not exist in the first updated matching user set, is 0.

[0136] In this embodiment, the users in the first updated matching user set are known. Therefore, the subchannel allocation variables of each user in the first updated matching user set are known. That is, for the k-th subchannel, the subchannel allocation variable of the first updated matching user set is 1 (that is, the base stations corresponding to each user allocate each user to the k-th subchannel), and for the remaining subchannels, the subchannel allocation variables of each user in the first updated matching user set are 0. At the same time, the signal-to-interference-plus-noise ratio can be calculated by the aforementioned formula (2). Therefore, based on the aforementioned formula (1), the first channel preference degree of the first updated matching user set can be obtained. Of course, the calculation processes of the second updated matching user set and the channel preference degrees of the two matching user sets before updating are also the same, which will not be elaborated here.

[0137] After calculating the channel preference degrees of the matching user sets before and after the update, based on this, the preference of the subchannel for the user can be judged, and the process is as shown in the following step S33g.

[0138] S33g. Determine whether the sum of the first channel preference degree and the second channel preference degree is greater than the sum of the third channel preference degree and the fourth channel preference degree; in this embodiment, if the sum of the channel preference degrees of the two updated matching user sets is greater than the sum of the channel preference degrees of the two original matching user sets, it indicates that the j-th sub-channel prefers the q-th user rather than the a-th user (that is, the preference degree of the aforementioned second target sub-channel for the first user is greater than the preference degree of the second target sub-channel for the second user); at this time, it can be determined that the sub-channel matching relationship between the q-th user and the a-th user is incorrect, and the two can form a blocking matching pair, and the process is as shown in the following step S33h.

[0139] S33h. If so, determine that there is an error in the sub-channel matching relationship between the q-th user and the a-th user, form a blocking matching pair with the q-th user and the a-th user, and determine that there is a blocking matching pair in the initial channel matching set; in this embodiment, if the condition in step S33g is not satisfied, a user needs to be reselected from the matching user set corresponding to the j-th sub-channel, and the aforementioned step S33b needs to be executed again; of course, if after traversing all the users in the matching user set corresponding to the j-th sub-channel, no user with an incorrect matching relationship with the q-th user is found, at this time, the matching user set corresponding to the next sub-channel is selected, and then the aforementioned steps S33b to S33h are repeated.

[0140] Thus, through the above description of steps S33a to S33h, assuming that the object of parameter judgment is user K b and user K d ,

[0141] then, the determination of the blocking matching pair can be expressed as:

[0142] If (1) K b ∈Ω(N d ), (2) there exists K d ∈Ω(N c ),

[0143] then it is considered that there is a blocking matching pair in the matching Ω; where means that for user K b , it satisfies G(N c ,K b )>G(N d ,K b ), that is, user K b prefers the sub-channel N c rather than the sub-channel N d ,

[0144] represents sub-channel N c has a greater preference for user K b rather than user K d , where, if the following is satisfied

[0145] F((Ω(N c )\K d ∪K b ,N c )) + F(Ω(N d )\K b ∪K d ,N d ) > F(Ω(N d ),N d ) +

[0146] F((Ω(N c ),N c )); then it is determined that Specifically, G(·) and F(·) respectively represent the preference function of the user for the sub-channel and the preference function of the sub-channel for the user (i.e., the aforementioned sub-channel preference function, and the calculation formulas of the two can be seen in the aforementioned formulas (9) and (1)).

[0147] Thus, through the aforementioned steps S33a to S33h, blocking matching pairs can be screened out from the initial channel matching set. Then, the sub-channel matching relationships within the screened blocking matching pairs need to be adjusted to ensure that there are no blocking matching pairs in the obtained channel matching set; the process of adjusting the sub-channel matching relationship is as shown in the following step S34.

[0148] S34. If so, modify the sub-channel matching relationships of the two users within each blocking matching pair in the initial channel matching set until there are no blocking matching pairs in the initial channel matching set, so as to use the initial channel matching set without blocking matching pairs as the channel matching set; in this embodiment, taking the aforementioned a-th user and q-th user as an example, the two are a blocking matching pair. Then, change the sub-channel matching relationship of the q-th user to the j-th sub-channel (i.e., change from matching the k-th sub-channel to matching the j-th sub-channel), and change the sub-channel matching relationship of the a-th user to the k-th sub-channel; in this way, after modifying the sub-channel matching relationships of the two users within each blocking matching pair, it is again determined whether there are blocking matching pairs in the initial channel matching set. If there are, the matching relationship needs to be modified again until there are no blocking matching pairs in the obtained initial channel matching set. At this time, the final channel matching set can be obtained.

[0149] After obtaining the final channel matching set, based on this, the sub-channel corresponding to each user can be obtained, as shown in the following step S35.

[0150] S35. Determine the sub-channels corresponding to each user in the target area according to the channel matching set.

[0151] Thus, through the foregoing steps S31 to S35, the allocation of sub-channels can be completed once, and the sub-channels of each user at the current iteration can be obtained. Then, based on the sub-channels of each current user and in combination with the foregoing sub-channel resource optimization model, the precoding vector and the maximum achievable sum rate of the system can be determined; the calculation process is as shown in the following step S4.

[0152] S4. Use the sub-channels corresponding to each user and the sub-channel resource optimization model to calculate the model optimization result, where the model optimization result includes the maximum achievable sum rate of the downlink multi-cell multi-antenna system; in this embodiment, after the sub-channels of each user at the current iteration are determined, the corresponding sub-channel allocation vector can be determined. Therefore, the only variable in the entire model is the precoding vector allocated by the base station for the corresponding user; therefore, the precoding vector with the maximum achievable sum rate can be obtained with the goal of maximizing the achievable sum rate of the system (i.e., solving the foregoing formula (5)); finally, based on the obtained maximum achievable sum rate at the current iteration, it can be determined whether it is necessary to continue the allocation optimization of sub-channels, that is, whether it is necessary to update the user interference hypergraph, and based on this, the sub-channels of each user can be re-determined.

[0153] Among them, the calculation process of the foregoing model optimization result is as follows: First, based on the sub-channels corresponding to each user, determine the sub-channel allocation variables of each user; then, according to the sub-channel allocation variables of each user, perform a solution operation on the sub-channel resource optimization model to obtain the precoding vectors of each user when the downlink multi-cell multi-antenna system reaches the maximum achievable sum rate; finally, the maximum achievable sum rate and the precoding vectors of each user when the maximum achievable sum rate is reached can be used to form the model optimization result.

[0154] In this embodiment, when the sub-channel allocation variable is given, the problem of solving the precoding vector is still a non-convex and non-linear problem, and it is difficult to directly obtain an analytical solution. At present, there are many mature solution methods for this problem. For example, the continuous convex approximation method can be used to approximately transform the non-convex objective function into a convex function, and at the same time, the constraint conditions are transformed into convex constraints, so that the optimization problem becomes a convex problem, and then optimization methods such as the Lagrangian dual method or the interior point method are used to solve the transformed convex problem. At the same time, deep reinforcement learning can also be used as a method for solving the precoding vector design problem. Among them, the input state of deep reinforcement learning can be composed of the channel vectors of all users, the received power, and the action vector of the previous step. The action vector is set as the precoding vector, and the reward function is the achievable sum rate of the system. Therefore, the deep reinforcement learning algorithm can be used to solve the sub-channel resource optimization model (here it refers to the solution of the aforementioned formula (5), that is, the solution of the precoding vector). In addition, for example, the deep reinforcement learning algorithm can be algorithms such as Deep Q-Network (DQN) or Deep Deterministic Policy Gradient (DDPG). Of course, the aforementioned algorithms are common techniques for solving convex problems, and their processes will not be elaborated here.

[0155] After obtaining the precoding vector that maximizes the achievable sum rate of the system, it is possible to determine whether the maximum achievable sum rate at the current iteration meets the preset conditions. If it does not meet the conditions, the user interference hypergraph needs to be updated to re-allocate the sub-channels. The judgment process and the user interference hypergraph update process are shown in the following steps S5 and S6.

[0156] S5. Determine whether the maximum achievable sum rate in the model optimization result meets the preset conditions; in this embodiment, the preset condition is that the difference between the currently obtained maximum achievable sum rate and the maximum achievable sum rate obtained in the previous iteration is less than a given threshold. Among them, if it is less than the given threshold, the iteration ends. Otherwise, the user interference hypergraph needs to be updated and the iteration continues. The process is shown in the following step S6.

[0157] S6. If not, rebuild the user interference hypergraph corresponding to the target area until the maximum achievable sum rate meets the preset condition. Then, use the subchannels corresponding to each user with the maximum achievable sum rate when the preset condition is met as the optimal subchannels for each user. In this embodiment, the user interference hypergraph corresponding to the target area is rebuilt using the precoding vectors of each user when the maximum achievable sum rate is reached during the current iteration. This is equivalent to updating the precoding vectors of each user. Then, the updated precoding vectors are used to rebuild the user interference hypergraph. Next, the updated user interference hypergraph is used to continue the subchannel allocation until the maximum achievable sum rate of the system reaches the preset condition. At this time, the optimal subchannel allocation result is obtained.

[0158] In addition, the convergence effect of the subchannel allocation algorithm provided in this embodiment is as Figure 3 shown. As can be seen from Figure 3 , the algorithm disclosed in this embodiment can converge within fewer iterations and has good convergence performance. At the same time, the relationship between the achievable sum rate of the system in this embodiment and the power is as Figure 4 shown. As can be seen from Figure 4 , the achievable sum rate of the system increases with the increase of power. Under the same transmit power, the achievable sum rate of the system obtained in this embodiment is always greater than that of other compared schemes. Therefore, it shows that the allocation scheme provided in this embodiment can improve the achievable sum rate of the system as much as possible while ensuring the effectiveness of subchannel allocation.

[0159] Thus, through the subchannel allocation method for a multi-cell multi-antenna system described in detail in the foregoing steps S1 to S6, the present invention can dynamically update the interference hypergraph according to the current maximum achievable sum rate of the system during the subchannel allocation process. In this way, an accurate modeling of the interference relationship between users can be achieved, thereby improving the achievable sum rate of the system as much as possible while ensuring the effectiveness of subchannel allocation, and further guaranteeing the quality of service of users. Based on this, the present invention realizes the allocation of subchannels in a multi-cell multi-antenna system with low complexity and high effectiveness, improves the system spectrum utilization rate, reduces interference, and enhances the network capacity and user service quality. Therefore, it is very suitable for large-scale application and promotion.

[0160] As Figure 5 shown, the second aspect of this embodiment provides a hardware system for implementing the subchannel allocation method for a multi-cell multi-antenna system described in the first aspect of the embodiment, including:

[0161] A model construction unit, configured to construct a sub-channel resource optimization model for a downlink multi-cell multi-antenna system composed of all base stations in a target area, wherein the sub-channel resource optimization model aims to maximize the achievable sum rate of the downlink multi-cell multi-antenna system.

[0162] A sub-channel allocation unit, configured to establish a user interference hypergraph corresponding to the target area, wherein the user interference hypergraph includes a plurality of nodes, any node corresponds to a user in the target area, and users with signal interference relationships are connected by edges.

[0163] The sub-channel allocation unit is configured to determine the sub-channels corresponding to each user in the target area according to the user interference hypergraph.

[0164] A calculation unit, configured to calculate a model optimization result by using the sub-channels corresponding to each user and the sub-channel resource optimization model, wherein the model optimization result includes the maximum achievable sum rate of the downlink multi-cell multi-antenna system.

[0165] The sub-channel allocation unit is configured to determine whether the maximum achievable sum rate in the model optimization result meets a preset condition.

[0166] The sub-channel allocation unit is further configured to, when it is determined that the maximum achievable sum rate in the model optimization result does not meet the preset condition, re-establish the user interference hypergraph corresponding to the target area until the maximum achievable sum rate meets the preset condition, so as to use the sub-channels of each user corresponding to the maximum achievable sum rate that meets the preset condition as the optimal sub-channels corresponding to each user.

[0167] For the working process, working details and technical effects of the system provided in this embodiment, reference may be made to the first aspect of the embodiment, which will not be elaborated herein.

[0168] As Figure 6 shown, a third aspect of this embodiment provides a sub-channel allocation device for a multi-cell multi-antenna system. Taking the device as an electronic device as an example, it includes: a memory, a processor, and a transceiver that are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the sub-channel allocation method for a multi-cell multi-antenna system as described in the first aspect of the embodiment.

[0169] Specifically, the memory may include, but is not limited to, random access memory (RAM), read only memory (ROM), flash memory, first input first output (FIFO), and / or first in last out (FILO), etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented in at least one of the following hardware forms: digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). At the same time, the processor may also include a main processor and a co-processor. The main processor is a processor used to process data in the wake state, also known as the central processing unit (CPU); the co-processor is a low-power processor used to process data in the standby state.

[0170] In some embodiments, the processor may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. For example, the processor may be, but is not limited to, a microprocessor of the STM32F105 series, a reduced instruction set computer (RISC) microprocessor, a processor with an X86 architecture, or a processor integrated with a neural-network processing unit (NPU); the transceiver may be, but is not limited to, a Wi-Fi wireless transceiver, a Bluetooth wireless transceiver, a general packet radio service (GPRS) wireless transceiver, a ZigBee wireless transceiver (a low-power local area network protocol based on the IEEE 802.15.4 standard), a 3G transceiver, a 4G transceiver, and / or a 5G transceiver, etc. In addition, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0171] For the working process, working details, and technical effects of the electronic device provided in this embodiment, reference may be made to the first aspect of the embodiment, which will not be elaborated here.

[0172] The fourth aspect of this embodiment provides a storage medium storing instructions for the sub-channel allocation method for a multi-cell multi-antenna system described in the first aspect of the embodiment, that is, instructions are stored on the storage medium, and when the instructions run on a computer, the sub-channel allocation method for a multi-cell multi-antenna system described in the first aspect of the embodiment is executed.

[0173] Among them, the storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical discs, hard disks, flash memories, USB flash drives, and / or Memory Sticks, etc. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0174] For the working process, working details, and technical effects of the storage medium provided in this embodiment, reference may be made to the first aspect of the embodiment, which will not be elaborated here.

[0175] The fifth aspect of this embodiment provides a computer program product containing instructions, which, when running on a computer, cause the computer to execute the sub-channel allocation method for a multi-cell multi-antenna system described in the first aspect of the embodiment, where the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices

[0176] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A sub-channel allocation method for a multi-cell multi-antenna system, characterized in that Including: Construct a sub-channel resource optimization model for the downlink multi-cell multi-antenna system composed of all base stations in the target area, where the sub-channel resource optimization model aims to maximize the achievable sum rate of the downlink multi-cell multi-antenna system; Establish a user interference hypergraph corresponding to the target area, where the user interference hypergraph contains multiple nodes, any node corresponds to a user in the target area, and users with signal interference relationships are connected by edges; According to the user interference hypergraph, determine the sub-channels corresponding to each user in the target area; Using the sub-channels corresponding to each user and the sub-channel resource optimization model, calculate the model optimization result, where the model optimization result includes the maximum achievable sum rate of the downlink multi-cell multi-antenna system; Judge whether the maximum achievable sum rate in the model optimization result meets the preset conditions; If not, re-establish the user interference hypergraph corresponding to the target area until the maximum achievable sum rate meets the preset conditions, and use the sub-channels of each user corresponding to the maximum achievable sum rate that meets the preset conditions as the optimal sub-channels corresponding to each user.

2. The method according to claim 1, wherein According to the user interference hypergraph, determining the sub-channels corresponding to each user in the target area includes: Calculate the preference degrees of each user in the target area for each sub-channel in the downlink multi-cell multi-antenna system; Based on the preference degrees of each user for each sub-channel, determine the initial matching sub-channels corresponding to each user, and generate an initial channel matching set based on the initial matching sub-channels corresponding to each user; Judge whether there are blocking matching pairs in the initial channel matching set, where the blocking matching pair includes two users with incorrect sub-channel matching relationships; If so, modify the sub-channel matching relationships of the two users in each blocking matching pair in the initial channel matching set until there are no blocking matching pairs in the initial channel matching set, and use the initial channel matching set without blocking matching pairs as the channel matching set; According to the channel matching set, determine the sub-channels corresponding to each user in the target area.

3. The method according to claim 2, wherein Each base station in the target area corresponds to served users respectively, where calculating the preference degrees of each user in the target area for each sub-channel in the downlink multi-cell multi-antenna system includes: For any user, obtain the precoding vector assigned by the target base station to the any user, and the channel vector of the any user on any sub-channel, and the target base station is the base station to which the any user belongs; According to the precoding vector and the channel vector of the any user on the any sub-channel, calculate the preference degree of the any user for the any sub-channel, and after polling all sub-channels, obtain the preference degrees of the any user for each sub-channel.

4. The method according to claim 2, wherein Each base station in the target area corresponds to served users respectively, where determining the initial matching sub-channels corresponding to each user based on the preference degrees of each user for each sub-channel includes: For any user, sort all sub-channels in descending order according to the preference degree of the user for each sub-channel to obtain a sub-channel preference list; Control the user to send a matching request for the i-th sub-channel in the sub-channel preference list to the target base station, so that after receiving the matching request, the target base station generates matching feedback information according to the user interference hypergraph, where the target base station is the base station to which the user belongs, and the initial value of i is 1; Based on the matching feedback information, determine whether the target base station accepts the matching request sent by the user; If not, increment i by 1 and re-control the user to send a matching request for the i-th sub-channel in the sub-channel preference list to the target base station until it is determined that the target base station accepts the matching request sent by the user, so as to use the i-th sub-channel corresponding to the target base station accepting the matching request sent by the user as the initial matching sub-channel corresponding to the user.

5. The method according to claim 2, wherein Judge whether there is a blocking matching pair in the initial channel matching set, including: Based on the initial channel matching set, determine the matching user set corresponding to each sub-channel; For the matching user set corresponding to the k-th sub-channel and the matching user set corresponding to the j-th sub-channel, judge whether there is a connection edge in the user interference hypergraph between the q-th user in the matching user set corresponding to the k-th sub-channel and the a-th user in the matching user set corresponding to the j-th sub-channel, where k, j, q, and a are all positive integers; If not, calculate the first preference degree of the q-th user for the k-th sub-channel and the second preference degree of the q-th user for the j-th sub-channel; Judge whether the second preference degree is greater than the first preference degree; If so, delete the q-th user from the matching user set corresponding to the k-th sub-channel and add the a-th user to obtain the first updated matching user set, and delete the a-th user from the matching user set corresponding to the j-th sub-channel and add the q-th user to obtain the second updated matching user set; Using the sub-channel preference function, calculate the first channel preference degree of the first updated matching user set, the second channel preference degree of the second updated matching user set, the third channel preference degree of the matching user set corresponding to the k-th sub-channel, and the fourth channel preference degree of the matching user set corresponding to the j-th sub-channel; Judge whether the sum of the first channel preference degree and the second channel preference degree is greater than the sum of the third channel preference degree and the fourth channel preference degree; If so, determine that there is an error in the sub-channel matching relationship between the q-th user and the a-th user, form a blocking matching pair with the q-th user and the a-th user, and determine that there is a blocking matching pair in the initial channel matching set.

6. The method according to claim 5, characterized in that Using the sub-channel preference function, calculate the first channel preference degree of the first updated matching user set, including: Calculate the first channel preference degree using the following formula (1); F(Ω′(N k ),N k ) represents the first channel preference degree, represents the sub-channel allocation variable of the u-th user in the user set served by the m-th base station, represents the signal-to-interference-plus-noise ratio of the u-th user with respect to the k-th sub-channel N k , B represents the total bandwidth of the downlink multi-cell multi-antenna system, N represents the total number of sub-channels of the downlink multi-cell multi-antenna system, M represents the total number of base stations, Μ m represents the user set served by the m-th base station, Ω′(N k ) represents the first updated matching user set, where when the u-th user belongs to the first updated matching user set, is 1, and when the u-th user does not exist in the first updated matching user set, is 0.

7. The method according to claim 1, characterized in that The model variables of the sub-channel resource optimization model include the sub-channel allocation variables of each user and the precoding vectors of each user; wherein, using the sub-channel corresponding to each user and the sub-channel resource optimization model, the model optimization result is calculated, including: Based on the sub-channel corresponding to each user, determine the sub-channel allocation variables of each user; According to the sub-channel allocation variables of each user, perform a solution operation on the sub-channel resource optimization model to obtain the precoding vectors of each user when the downlink multi-cell multi-antenna system reaches the maximum achievable sum rate; Use the maximum achievable sum rate and the precoding vectors of each user when reaching the maximum achievable sum rate to form the model optimization result; Correspondingly, re-establish the user interference hypergraph corresponding to the target area, including: Use the precoding vectors of each user when reaching the maximum achievable sum rate to re-establish the user interference hypergraph corresponding to the target area.

8. The method according to claim 1, characterized in that, Construct a sub-channel resource optimization model for the downlink multi-cell multi-antenna system composed of all base stations in the target area, including: Obtain the base station information of the target area; According to the base station information, determine the signal-to-interference-plus-noise ratio of each user in the target area; Use the signal-to-interference-plus-noise ratio of each user to construct the sub-channel resource optimization model.

9. A sub-channel allocation system for a multi-cell multi-antenna system, characterized in that Including: A model construction unit for constructing a sub-channel resource optimization model for the downlink multi-cell multi-antenna system composed of all base stations in the target area, wherein the sub-channel resource optimization model takes maximizing the achievable sum rate of the downlink multi-cell multi-antenna system as the optimization goal; A sub-channel allocation unit for establishing the user interference hypergraph corresponding to the target area, wherein the user interference hypergraph includes multiple nodes, any node corresponds to a user in the target area, and the users with signal interference relationships are connected by edges; A sub-channel allocation unit for determining the sub-channel corresponding to each user in the target area according to the user interference hypergraph; A calculation unit for calculating the model optimization result using the sub-channel corresponding to each user and the sub-channel resource optimization model, wherein the model optimization result includes the maximum achievable sum rate of the downlink multi-cell multi-antenna system; A sub-channel allocation unit for determining whether the maximum achievable sum rate in the model optimization result meets a preset condition; The sub-channel allocation unit is further configured to, when it is determined that the maximum achievable sum rate in the model optimization result does not meet the preset condition, re-establish the user interference hypergraph corresponding to the target area until the maximum achievable sum rate meets the preset condition, so as to use the sub-channel of each user corresponding to the maximum achievable sum rate that meets the preset condition as the optimal sub-channel corresponding to each user.

10. An electronic device, characterized in that, Including: A memory, a processor, and a transceiver that are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the sub-channel allocation method for a multi-cell multi-antenna system according to any one of claims 1 to 8.

Images