Power and subcarrier allocation method for resisting interference among multiple cells and centralized controller
By modeling and distribution robust optimization of channel gain errors in a multi-cell environment, the optimization problem is decomposed and optimization problems are power and subcarrier allocation subproblems, and iteratively solves them, and the optimization solution is not feasible due to channel errors, achieving robustness Higher allocation plan.
Patent Information
- Application Number
- CN202411362566.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-27
- Publication Date
- 2025-02-11
AI Technical Summary
In the prior art, the optimization solution caused by channel error is not the optimal situation or even unfeasible, especially in multi-cell environments, interference and noise problems caused by inaccurate channel parameters are difficult to effectively solve.
By modeling the system channel gain error based on the channel state parameters of each cell, the system error model is approximately processed by using distribution robust optimization, and the problem to be optimized is constructed, and it is divided into power distribution sub-problems and sub-carrier allocation sub-problems. The sub-problem is iteratively solved to obtain a more robust allocation scheme.
In the case of channel gain disturbance, a higher robust and better power and subcarrier allocation scheme is achieved, ensuring the improvement of system performance and the fairness of service quality among users.
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Figure CN119421166A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of mobile communication technology, and in particular to a power and subcarrier allocation method and a centralized controller for resisting multi-cell interference. Background Art
[0002] Inter-cell interference is one of the main factors affecting system performance in a multi-cell system. Optimizing power allocation and subcarrier allocation can effectively reduce or avoid inter-cell interference, allowing users in each cell to better utilize wireless resources and maximize downlink rates. In addition, in a multi-cell environment, there may be large channel differences between different users. Through joint optimization, while ensuring overall system performance, it is possible to ensure fairness in service quality among users, especially cell edge users can obtain better service quality.
[0003] It should be noted that before optimization, some system parameters need to be measured and estimated, among which the most important are channel parameters. Currently, there are mainly pilot signal measurement, receiver estimation, Kalman filter and other methods to measure and estimate them. For example, a centralized controller is set up to receive the channel state information measured by each cell, and as a centralized processing unit to optimize the performance of the entire system, and transmit the optimized results to each cell base station. However, due to many factors such as fast channel fading, estimation error, feedback delay, reciprocity error, interference and noise, the estimated channel parameters will be inaccurate, resulting in the formulated optimization plan not being optimal or even infeasible. Summary of the invention
[0004] The embodiments of the present application provide a power and subcarrier allocation method and a centralized controller that are resistant to multi-cell interference, so as to at least solve the technical problem in the related art that the optimization solution caused by channel errors is not optimal or even unfeasible.
[0005] According to one aspect of an embodiment of the present application, a method for allocating power and subcarriers to resist multi-cell interference is provided, including:
[0006] Modeling the system channel gain error based on the channel state parameters of each cell to obtain a system error model;
[0007] The system error model is approximated by using distributed robust optimization to obtain a converted system error model;
[0008] Constructing a problem to be optimized based on the converted system error model;
[0009] The problem to be optimized is divided into a power allocation subproblem and a subcarrier allocation subproblem, and the subproblems are iteratively solved to obtain a subcarrier allocation scheme and a power allocation scheme.
[0010] In an optional implementation manner, before modeling the system channel gain error based on the channel state parameters of each cell, the method further includes:
[0011] Obtain channel state parameters of each cell;
[0012] The channel state parameters include channel gain, channel bandwidth, channel noise, and channel transmit power.
[0013] In an optional implementation, constructing a problem to be optimized based on the converted system error model includes:
[0014] Bringing the converted system error model into the problem to be optimized;
[0015] The objective function of the problem to be optimized is to maximize the sum of the rates of each user in the system when the probability threshold is violated.
[0016] In an optional implementation, the constraints of the problem to be optimized include:
[0017] A first constraint, wherein the first constraint indicates that the allocation variable of each subcarrier can only be 0 or 1, and each subcarrier can be allocated to at most one user;
[0018] A second constraint, wherein the second constraint indicates that the sum of power allocations of each cell shall not exceed a maximum power limit of the cell;
[0019] A third constraint, wherein the third constraint represents a service quality constraint of a minimum rate of a user, and satisfies a minimum rate requirement of each user;
[0020] a fourth constraint, wherein the fourth constraint represents a lower bound estimate of the numerator in the SINR fraction;
[0021] A fifth constraint, wherein the fifth constraint represents an upper bound estimate of the denominator in the SINR fraction.
[0022] In an optional embodiment, it also includes:
[0023] Introduce auxiliary functions and use the Cauchy-Schwarz inequality to scale the problem to be optimized.
[0024] The converted constraint conditions of the problem to be optimized include the first constraint, the second constraint and the third constraint.
[0025] In an optional implementation manner, the problem to be optimized is divided into a power allocation subproblem and a subcarrier allocation subproblem, including:
[0026] Dividing the problem to be optimized into a power allocation subproblem and a subcarrier allocation subproblem;
[0027] In the power allocation subproblem, the subcarrier allocation variables are fixed.
[0028] In the subcarrier allocation subproblem, the power allocation variable is fixed.
[0029] In an optional implementation manner, the sub-problems are iteratively solved to obtain a subcarrier allocation scheme and a power allocation scheme, including:
[0030] Cyclic solving the power allocation subproblem and the subcarrier allocation subproblem in sequence;
[0031] Determine whether the difference between the target value obtained in this iteration and the target value obtained in the previous iteration is less than a preset threshold;
[0032] If the difference is less than a preset threshold, the iteration is stopped and the final subcarrier allocation scheme and power allocation scheme are output.
[0033] In an optional implementation manner, before iteratively solving the sub-problem, the method further includes:
[0034] The power in each cell is evenly distributed to the subcarriers to which it belongs and initialized.
[0035] According to another aspect of an embodiment of the present application, a centralized controller is provided, which includes a robust optimization module for modeling the system channel gain error based on the channel state parameters of each cell to obtain a system error model; using distributed robust optimization to approximate the system error model to obtain a converted system error model; constructing a problem to be optimized based on the converted system error model; dividing the problem to be optimized into a power allocation subproblem and a subcarrier allocation subproblem, iteratively solving the subproblems to obtain a subcarrier allocation scheme and a power allocation scheme.
[0036] In one embodiment, the centralized controller is embedded in a multi-cell control unit.
[0037] The technical solution provided by the embodiments of the present application may have the following beneficial effects:
[0038] The scheme of the present application takes into account the channel parameter error during the optimization process, models the uncertain channel gain based on the method of distributed robust optimization, and takes into account its perturbation during the optimization process, so that the obtained optimization scheme is robust in the case of channel gain perturbation. A more robust and better allocation scheme can be obtained. In addition, the present application adopts the method of problem decomposition and iteration, which can simplify the problem and improve the solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0040] Figure 1 It is a flow chart of a power and subcarrier allocation method for resisting multi-cell interference according to an embodiment of the present application;
[0041] Figure 2 is a multi-cell interference scenario diagram according to an embodiment of the present application;
[0042] Figure 3 is a flow chart of an allocation method according to an embodiment of the present application;
[0043] Figure 4 is a schematic diagram of a first simulation scenario according to an embodiment of the present application;
[0044] Figure 5 is a schematic diagram of algorithm convergence of a first simulation scenario according to an embodiment of the present application;
[0045] Figure 6 is a schematic diagram of a second simulation scenario according to an embodiment of the present application;
[0046] Figure 7 is a schematic diagram of algorithm convergence of a second simulation scenario according to an embodiment of the present application;
[0047] Figure 8 It is a schematic diagram of the design of a robust resource allocation device according to an embodiment of the present application. DETAILED DESCRIPTION
[0048] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.
[0049] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0050] Inter cell interference (ICI) is one of the main factors affecting system performance in a multi-cell system. Figure 2 As shown in the figure, there are M cells in the whole system, and each cell has the same number of K users. The system has N orthogonal subcarriers {f1,…,f N}, each cell can determine the orthogonal subcarriers allocated to each user, and these subcarriers are reused between different cells. Because each cell reuses the same subcarrier channel, the base station of each cell will cause interference to the devices in other cells. For simplicity, Figure 2 Only the interference link from cell 1 to the equipment in cell 2 is shown.
[0051] Optimizing power allocation and subcarrier allocation can effectively reduce or avoid interference between cells. This application proposes a power and subcarrier allocation method that is resistant to interference between multiple cells. It is hoped that in a multi-cell scenario, the power allocation and subcarrier allocation in each cell can be jointly optimized to achieve the goal of maximizing the sum of the downlink rates of all users. It should be noted that within each cell, orthogonal frequency division multiple access is used between its subcarriers, so there is no interference within the cell. Since these subcarriers are reused between different cells, interference between cells will have a greater impact on the rate. In addition, in this scenario, the impact of channel gain error on the quality of service constraints and objective functions is also considered and expressed in the form of probability constraints.
[0052] The following is combined with Figure 1 The power and subcarrier allocation method for resisting multi-cell interference in the embodiment of the present application is described in detail. Figure 1 As shown, the method mainly includes the following steps:
[0053] S101 models the system channel gain error based on the channel state parameters of each cell to obtain a system error model.
[0054] In an optional implementation, the channel state parameters of each cell are obtained, and the channel state parameters include channel gain, channel bandwidth, channel noise, channel transmission power, etc. Currently, the commonly used channel state parameter acquisition methods include pilot signal measurement, receiver estimation, Kalman filter, etc. to measure and estimate them. The specific acquisition method is not specifically limited in this application.
[0055] Furthermore, if Figure 2 For the kth user in the mth cell, the rate of the subcarrier n is expressed as follows according to the Shannon formula:
[0056]
[0057] In this application, It represents the rate of the kth user in the mth cell on subcarrier n. or 1, indicating whether channel n under base station m is allocated to user k. If so, the value is 1, otherwise it is 0. w represents the bandwidth of each subcarrier channel. Here, all channel bandwidths are set to be the same. represents the channel gain of the kth user in channel n under base station m, represents the channel gain of base station j to the kth user under base station m in channel n, represents the transmission power allocated to channel n by the base station of cell j, represents the transmission power allocated by the mth base station to the nth channel, σ 2 represents the channel noise.
[0058] It is understandable that due to various factors, there will be inevitable errors in the measurement of channel gain. In some extreme cases, these errors may cause the optimization solution to fail or the service requirements to fail to be met. The error is It is hoped that the probability that its rate is lower than the specified service quality under channel gain disturbance does not exceed Right now:
[0059]
[0060] Where Prob represents the probability of an event occurring. N: total number of subcarriers, represents the minimum service quality requirement of the kth user under the mth base station, It indicates the threshold that the probability that the rate of the kth user under the mth base station is lower than the service quality requirement should not exceed.
[0061] In addition, the goal of this application is to maximize the sum of the rates of each user in the system. However, due to the fractional form of SINR, the objective function is not a concave function. Here, we first scale it up and introduce two auxiliary variables and The SI NR of the above formula is converted into two constraints through the lower bound of the numerator and the upper bound of the denominator:
[0062]
[0063] You can get:
[0064]
[0065] Then for the following formula:
[0066]
[0067] When (1.3) and 1.5) hold, constraint (1.2) must hold.
[0068] By modeling the channel gain error, the system error model is obtained.
[0069] S102 uses distributed robust optimization to approximate the system error model to obtain a converted system error model.
[0070] In one implementation, a problem in the form of a fraction is converted into two chance constraints, but these two chance constraints are still not easy to solve directly. In addition, generally only some moment information about the distribution of the error can be obtained, such as the range of the first-order moment and the second-order moment, that is, the range of the mean and variance, which can be approximated by distributed robust optimization. Here, for simplicity, take the normal distribution as an example,
[0071] By dividing the Bruno rod approximation, Then these two chance constraints can be transformed into:
[0072]
[0073]
[0074] in, and They respectively represent the mean and standard deviation of the channel gain disturbance of the kth user under the mth base station on the nth subcarrier.
[0075] The present application relates to converting a chance constraint problem containing fractions into a more tractable form and approximating the uncertainty by means of a distributionally robust optimization method. It includes determining the chance constraints in the original problem that contain fractional forms, which are not easy to solve directly, the error distribution assumption, and the assumption that the channel gain perturbation follows a normal distribution. Approximately handling uncertainty by means of a distributionally robust optimization method, converting the chance constraints into deterministic constraints. Convert the original chance constraints into two deterministic constraints (Formulas 1.6a and 1.6b).
[0076] This application uses fractional robust optimization to perform reliability conversion on the constructed system error model. It can simplify problem solving. By converting the original fractional chance constraints into linear constraints, the problem becomes easier to solve because linear constraints can usually be effectively handled by linear programming or other optimization algorithms. Improve robustness by considering the uncertainty and error of channel gain to ensure that the system's service quality meets the predetermined requirements even in the worst case. Flexibility and adaptability. This scheme allows the system to maintain performance in the face of uncertainty, especially in wireless communication environments where channel conditions change frequently. Probabilistic guarantee: By setting The system ensures that The service quality requirements can be met with a certain probability, which provides flexibility for system design.
[0077] Overall, this transformation approach allows for better handling of uncertainties and dynamically changing environments when designing and optimizing communication systems, thereby improving the overall performance and reliability of the system.
[0078] S103 constructs the problem to be optimized based on the converted system error model.
[0079] In an optional implementation, constructing the problem to be optimized based on the converted system error model includes: bringing the converted system error model into the problem to be optimized.
[0080] Specifically, the original problem to be optimized P1 is constructed as:
[0081]
[0082] In this application, represents the channel gain of base station j in channel n, represents the power of user k in channel n under base station j, represents the signal processing correlation coefficient of user k, represents the noise of user k in channel n under base station j.
[0083] Among them, the objective function of the problem to be optimized is to maximize the probability of each user in the system violating the probability threshold. The rate and under the circumstances. The constraints of the problem to be optimized include: the first constraint C1, the first constraint indicates that the allocation variable of each subcarrier can only be 0 or 1, and each subcarrier can be allocated to at most one user; the second constraint C2, the second constraint indicates that the sum of the power allocation of each cell must not exceed the maximum power limit of the cell; the third constraint C3, the third constraint indicates the service quality constraint of the minimum rate of the user, which meets the minimum rate requirement of each user; the fourth constraint C4, the fourth constraint indicates the lower bound estimate of the numerator in the SINR fraction; the fifth constraint C5, the fifth constraint indicates the upper bound estimate of the denominator in the SINR fraction.
[0084] Furthermore, an auxiliary function is introduced and the Cauchy-Schwarz inequality is used for scaling to obtain the transformed problem to be optimized P2.
[0085] Specifically, the transformation process includes:
[0086]
[0087] The original optimization problem can be conservatively transformed into:
[0088]
[0089] Among them, g(p) and h(p) are introduced auxiliary functions, representing the upper and lower bounds of the system rate. Indicates the maximum power limit, and denote the user-specific versions, for user k and base station m respectively. represents the minimum rate requirement of user k under base station m. The transformed constraint conditions of the problem to be optimized include the first constraint, the second constraint and the third constraint.
[0090] S104 divides the problem to be optimized into a power allocation subproblem and a subcarrier allocation subproblem, iteratively solves the subproblems, and obtains a subcarrier allocation solution and a power allocation solution.
[0091] In an optional implementation, the problem to be optimized is divided into a power allocation subproblem and a subcarrier allocation subproblem, including: dividing the problem to be optimized into a power allocation subproblem and a subcarrier allocation subproblem; in the power allocation subproblem, the subcarrier allocation variable is fixed. In the subcarrier allocation subproblem, the power allocation variable is fixed.
[0092] The objective function and constraint C3 of the optimization problem P2 are not concave functions and convex constraints, which means that it is not a convex optimization problem. In addition, since problem P2 needs to optimize both the power variable p and the subcarrier variable α at the same time, it is difficult to directly solve the original problem. Therefore, the present application can decompose the problem into two sub-problems: the subcarrier allocation problem, in which the power variable p is fixed; and the power allocation problem, in which the subcarrier allocation variable α is fixed.
[0093] In an optional implementation manner, the sub-problems are iteratively solved to obtain a subcarrier allocation scheme and a power allocation scheme, including:
[0094] The power allocation subproblem and the subcarrier allocation subproblem are solved in turn in a loop; it is determined whether the difference between the target value obtained in this iteration and the target value obtained in the previous iteration is less than a preset threshold; if the difference is less than the preset threshold, the iteration is stopped and the final subcarrier allocation scheme and power allocation scheme are output.
[0095] First of all, this application is considered Fixed situation. Observe that the objective function and constraint C3 are both in the form of two concave functions subtracted from each other. Through the conversion of DC optimization, the present application obtains a solvable convex optimization problem. Therefore, the present application can solve this convex problem in each iteration of DC optimization, and then update the tangent plane function of the second concave function until the optimal point is found.
[0096] The form of the power allocation subproblem in each iteration t is:
[0097]
[0098] When the power is fixed, the present application can directly calculate the violation probability of the kth user in the mth cell on subcarrier n as The rate of , remember:
[0099]
[0100] The expression of the subcarrier allocation subproblem is:
[0101]
[0102] By iteratively solving the two sub-problems, the power allocation scheme and the subcarrier allocation scheme can be obtained.
[0103] In an optional implementation manner, before iteratively solving the sub-problem, the method further includes: evenly distributing the power in each cell to the corresponding sub-carriers for initialization.
[0104] In order to facilitate understanding of the optimization allocation method provided in the embodiment of the present application, Figure 3 Further description.
[0105] First, the relevant system parameters are obtained, and each cell sends the channel status information to the centralized controller. Then, the error modeling and conversion correspond to formulas (1.3) and (1.6). In order to initialize before iterative solution, the power in each cell is evenly distributed to the subcarriers to which it belongs. In the iterative solution process, the subcarrier subproblem P4 and the power subproblem P3 are solved in turn until the difference between the target values obtained from the previous and next iterations is less than the preset threshold, that is, the iteration is terminated when the error is allowed, and the joint optimization allocation solution is obtained.
[0106] Figure 4 This is a simulation scenario diagram, which shows the distribution of base stations and users. The X-axis represents the position coordinate in the X-axis direction, and the Y-axis represents the position coordinate in the Y-axis direction. Five cells, five users in each cell, and five orthogonal subcarrier resources were tested. Figure 5 is the comparison between the summed robustness rate and the number of iterations. Figure 5 The odd-numbered iterations represent solving the subcarrier allocation subproblem, and the even-numbered iterations represent solving the power allocation subproblem. It can be seen that in this scenario, the target value gradually increases and converges to the set allowable error of 10 -6 .
[0107] Figure 6 This is a simulation scenario diagram, which shows the distribution of base stations and users. The X-axis represents the position coordinate in the X-axis direction, and the Y-axis represents the position coordinate in the Y-axis direction. The scenario with 3 cells, 5 users in each cell and 5 orthogonal subcarrier resources was tested. Figure 7 It is the comparison between the summed robustness rate and the number of iterations, indicating the convergence of the algorithm in the second scenario.
[0108] The actual robust reliability under different violation probabilities in scenario 1 was also tested, where the error was set to a normal distribution with a mean of 0 and a standard deviation of 1 / 10 of the corresponding channel gain. The robust reliability is shown in the following table:
[0109]
[0110] It can be seen that in the solution of the present application, the actual reliability is always greater than the set violation probability, so the minimum service quality rate requirement of each user in the system must be met with the corresponding probability condition.
[0111] According to another aspect of an embodiment of the present application, a centralized controller is also provided, which includes a robust optimization module for modeling the system channel gain error based on the channel state parameters of each cell to obtain a system error model; using distributed robust optimization to approximate the system error model to obtain a converted system error model; constructing a problem to be optimized based on the converted system error model; dividing the problem to be optimized into a power allocation sub-problem and a subcarrier allocation sub-problem, iteratively solving the sub-problems to obtain a subcarrier allocation scheme and a power allocation scheme.
[0112] The centralized controller is embedded in the multi-cell control unit.
[0113] Figure 8 This is a design scheme of a robust resource allocation device of the present application. First, at the interaction level, each cell sends the measured channel state information to a centralized controller, in which the present application integrates the designed robust optimization module. The module models the system error and performs the corresponding reliability conversion, and then brings it into the optimization problem for optimization solution. Finally, the centralized controller sends the result to the corresponding cell base station for joint allocation of power and subcarriers. In the scheme of the present application, the centralized controller and the robust optimization module are embedded in a "multi-cell control unit", which is responsible for resource allocation of a surrounding cell. For example, in a cell-free architecture, the unit can be placed in a cloud device for processing base stations, and in a centralized radio access network (CRAN) architecture, the unit can be placed in a baseband processing unit (BBU) for centralized processing in a central computer room.
[0114] An embodiment of the present application provides a hardware device that can perform joint optimization of subcarrier and power resources.
[0115] It should be noted that the centralized controller provided in the above embodiment only uses the division of the above functional modules as an example when executing the power and subcarrier allocation method for resisting multi-cell interference. In actual applications, the above functional allocation can be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the centralized controller provided in the above embodiment and the power and subcarrier allocation method for resisting multi-cell interference belong to the same concept. The implementation process is detailed in the method embodiment, which will not be repeated here.
[0116] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0117] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.
Claims
1. A method for allocating power and subcarriers to resist multi-cell interference, characterized in that: include: Modeling the system channel gain error based on the channel state parameters of each cell to obtain a system error model; The system error model is approximated by using distributed robust optimization to obtain a converted system error model; Constructing a problem to be optimized based on the converted system error model; The problem to be optimized is divided into a power allocation subproblem and a subcarrier allocation subproblem, and the subproblems are iteratively solved to obtain a subcarrier allocation scheme and a power allocation scheme.
2. The method according to claim 1, characterized in that Before modeling the system channel gain error based on the channel state parameters of each cell, it also includes: Obtain channel state parameters of each cell; The channel state parameters include channel gain, channel bandwidth, channel noise, and channel transmit power.
3. The method according to claim 1, characterized in that The problem to be optimized is constructed based on the converted system error model, including: Bringing the converted system error model into the problem to be optimized; The objective function of the problem to be optimized is to maximize the sum of the rates of each user in the system when the probability threshold is violated.
4. The method according to claim 1 or 3, characterized in that: The constraints of the problem to be optimized include: A first constraint, wherein the first constraint indicates that the allocation variable of each subcarrier can only be 0 or 1, and each subcarrier can be allocated to at most one user; A second constraint, wherein the second constraint indicates that the sum of power allocations of each cell shall not exceed a maximum power limit of the cell; A third constraint, wherein the third constraint represents a service quality constraint of a minimum rate of a user, and satisfies a minimum rate requirement of each user; a fourth constraint, wherein the fourth constraint represents a lower bound estimate of the numerator in the SINR fraction; A fifth constraint, wherein the fifth constraint represents an upper bound estimate of the denominator in the SINR fraction.
5. The method according to claim 4, characterized in that Also includes: Introduce auxiliary functions and use the Cauchy-Schwarz inequality to scale the problem to be optimized. The converted constraint conditions of the problem to be optimized include the first constraint, the second constraint and the third constraint.
6. The method according to claim 1, characterized in that The problem to be optimized is divided into a power allocation subproblem and a subcarrier allocation subproblem, including: Dividing the problem to be optimized into a power allocation subproblem and a subcarrier allocation subproblem; In the power allocation subproblem, the subcarrier allocation variables are fixed. In the subcarrier allocation subproblem, the power allocation variable is fixed.
7. The method according to claim 1, characterized in that The sub-problems are solved iteratively to obtain the sub-carrier allocation scheme and the power allocation scheme, including: Cyclic solving the power allocation subproblem and the subcarrier allocation subproblem in sequence; Determine whether the difference between the target value obtained in this iteration and the target value obtained in the previous iteration is less than a preset threshold; If the difference is less than a preset threshold, the iteration is stopped and the final subcarrier allocation scheme and power allocation scheme are output.
8. The method according to claim 1, characterized in that Before iteratively solving the subproblem, it also includes: The power in each cell is evenly distributed to the subcarriers to which it belongs and initialized.
9. A centralized controller, characterized in that: The centralized controller includes a robust optimization module, which is used to model the system channel gain error based on the channel state parameters of each cell to obtain a system error model; use distributed robust optimization to approximate the system error model to obtain a converted system error model; and construct a problem to be optimized based on the converted system error model; The problem to be optimized is divided into a power allocation subproblem and a subcarrier allocation subproblem, and the subproblems are iteratively solved to obtain a subcarrier allocation scheme and a power allocation scheme.
10. The centralized controller according to claim 9, characterized in that: The centralized controller is embedded in the multi-cell control unit.