MIMO resource allocation method considering channel estimation error and related device

Abstract

The invention discloses an MIMO (Multiple Input Multiple Output) resource allocation method considering a channel estimation error and a related device, and the method comprises the steps: constructing a multi-cell MIMO system, and carrying out the estimation of a channel through employing an uplink orthogonal pilot frequency, and obtaining a least square and minimum mean square error estimation channel; estimating a channel according to the least square and the minimum mean square error, and determining an expected closed expression of a channel estimation error matrix; based on the channel hardening effect, with the average minimization of all users as the optimization target, a pilot frequency power distribution optimization problem is constructed, the pilot frequency power distribution optimization problem is solved, and the optimal pilot frequency power distribution scheme is obtained.By means of the method and the related device, pilot frequency pollution can be effectively relieved, the channel estimation precision is improved, and the channel estimation efficiency is improved. And meanwhile, balanced optimization of the overall performance of the system is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and relates to a MIMO resource allocation method considering channel estimation error and a related device. BACKGROUND

[0002] In a large-scale MIMO system, a base station is equipped with a large number of antennas to simultaneously serve multiple users, thereby significantly improving the spectral efficiency and energy efficiency. Since the large-scale MIMO system usually works in a time division duplex mode, the reciprocity of the uplink and downlink channels enables the base station to obtain channel state information through uplink pilot signals and accordingly perform downlink precoding and resource scheduling. Therefore, the accuracy of channel estimation directly affects the capacity, interference suppression capability and reliability of the system.

[0003] However, in a typical scenario where the number of users is much larger than the number of orthogonal pilot resources, multiple users have to reuse the same set of pilots, thereby causing a pilot contamination problem. This interference cannot be eliminated by traditional precoding methods and has become one of the main bottlenecks limiting the performance improvement of the system. In addition, due to the differences in large-scale channel characteristics such as distance from the base station, path loss and shadow fading, the reception quality of the pilot signals of different users is uneven. If a uniform pilot power configuration is adopted for all users, it may cause inaccurate channel estimation for distant users and energy waste for close-range users.

[0004] Therefore, research on pilot power allocation algorithms has become an important direction for the optimal design of large-scale MIMO systems. By allocating pilot power according to the channel differences between users, pilot contamination can be effectively alleviated, channel estimation accuracy can be improved, and the overall performance of the system can be balanced and optimized under energy constraints. The modeling and solving of this problem also provide a solid foundation for the research of system resource allocation, interference transmission, etc. However, the existing technology does not provide relevant disclosure. SUMMARY

[0005] The purpose of the present application is to overcome the above-mentioned shortcomings of the prior art, and to provide a MIMO resource allocation method considering channel estimation error and a related device, which can effectively alleviate pilot contamination, improve channel estimation accuracy, and achieve balanced optimization of the overall performance of the system.

[0006] To achieve the above-mentioned purpose, the present application discloses a MIMO resource allocation method considering channel estimation error, comprising: constructing a multi-cell MIMO system, estimating the channel using uplink orthogonal pilots to obtain least squares and minimum mean square error estimated channels; determining the closed-form expression of the channel estimation error matrix expectation based on the least squares and minimum mean square error estimated channels; Based on the channel hardening effect, the average of each user Minimize the pilot power allocation as the optimization objective, construct a pilot power allocation optimization problem, solve the pilot power allocation optimization problem, and obtain the optimal pilot power allocation scheme.

[0007] A further improvement of the MIMO resource allocation method considering channel estimation error described in this invention is as follows: Furthermore, the channel estimation error matrix is ​​expected to... The closed expression is:

[0008] in, Indicates the community l Chinese users k With the community j Large-scale fading coefficients between mid-range base stations For the community l Chinese users k pilot power, M The number of antennas configured for the base station.

[0009] Furthermore, the pilot power allocation optimization problem is expressed as:

[0010] in, Total pilot power for each cell, and for The lower and upper bound constraints, For the community j Chinese users k pilot power, K This refers to the number of users per antenna within a single cell.

[0011] Furthermore, the pilot power allocation optimization problem is solved using the Lagrange multiplier method.

[0012] This invention discloses a MIMO resource allocation system that considers channel estimation error, comprising: The module is used to build a multi-cell MIMO system. It uses uplink orthogonal pilots to estimate the channel and obtain the least squares and least mean square error channel estimates. The determination module is used to determine the expected channel estimation error matrix based on the least squares and least mean square error estimation of the channel. A closed expression; The solution module is used to calculate the average value for each user based on the channel hardening effect. Minimize the pilot power allocation as the optimization objective, construct a pilot power allocation optimization problem, solve the pilot power allocation optimization problem, and obtain the optimal pilot power allocation scheme.

[0013] A further improvement of the MIMO resource allocation system considering channel estimation error described in this invention is as follows: Furthermore, the channel estimation error matrix is ​​expected to... The closed expression is:

[0014] in, Indicates the community l Chinese users k With the community j Large-scale fading coefficients between mid-range base stations For the community l Chinese users k pilot power, M The number of antennas configured for the base station.

[0015] Furthermore, the pilot power allocation optimization problem is expressed as:

[0016] in, Total pilot power for each cell, and for The lower and upper bound constraints, For the community j Chinese users k pilot power, K This refers to the number of users per antenna within a single cell.

[0017] Furthermore, the pilot power allocation optimization problem is solved using the Lagrange multiplier method.

[0018] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the MIMO resource allocation method considering channel estimation error.

[0019] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the MIMO resource allocation method considering channel estimation error.

[0020] The present invention has the following beneficial effects: The MIMO resource allocation method and related apparatus that consider channel estimation error described in this invention aim to minimize the average relative channel estimation error of users during specific operation. By combining the Lagrange multiplier method to derive closed-form solutions, it significantly reduces computational overhead while ensuring channel estimation accuracy, supports distributed deployment, meets the requirements of 5G / 6G edge intelligence scenarios for low-latency and high-reliability resource allocation, achieves near-optimal channel estimation performance, has low computational complexity, supports distributed deployment, and meets the requirements of 5G / 6G networks for low-latency and high-reliability resource allocation. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a diagram illustrating the algorithm verification of the present invention; Figure 3 This is a verification diagram of the average relative channel estimation error (LS) estimation method of the present invention; Figure 4 This is a verification diagram of the Mean Relative Channel Estimation Error (MMSE) estimation method of the present invention; Figure 5 This is a diagram illustrating the Joint User and Cell Group (JUCG) scheduling strategy of the present invention. Figure 6 This is a system structure diagram of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0025] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0027] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0028] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0030] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0031] Example 1 refer to Figure 1 The MIMO resource allocation method considering channel estimation error described in this invention includes the following steps: 1) Construct a multi-cell MIMO system, use uplink orthogonal pilots to estimate the channel, and obtain the least squares and least mean square error estimated channel; In step 1), the multi-cell MIMO system includes L There are hexagonal communities, and each hexagonal community contains... K One single-antenna user and one equipped M The base station has one antenna. The base station and users in the entire system are perfectly synchronized. The entire system operates based on a time-division duplex protocol and employs orthogonal frequency division multiplexing (OFDM) technology. Furthermore, to simplify the analysis, the dependence of the user channel on the subcarrier index is ignored; therefore, the first... j The signal vectors received by the base station in each cell are:

[0032] in, Indicates the community l The transmit signal vector rate of the user in the middle, This represents the average normalized transmit power of all users in all cells. For the community j The noise vector in the data. Furthermore... Indicates the community l All users and communities j The channel matrix between base stations, i.e.:

[0033] in, For the community l Chinese users k With the community j Uplink channel transmission vector between base stations, channel The channel is a combination of small-scale and large-scale fading. for:

[0034] in, This indicates small-scale fading. This represents the large-scale fading coefficient. The block fading assumption is used here, meaning the large-scale fading coefficient remains constant over multiple coherent time intervals. It is assumed that the base station knows the large-scale fading coefficient, while the small-scale fading coefficient remains constant within one coherent time interval but changes between any two adjacent coherent time intervals. Furthermore, each user's channel is considered independent of the channels of other users.

[0035] At the start of the coherent interval, before a user sends data to the base station, the base station needs to obtain channel state information by estimating the channel between the user and the base station. Uplink pilot sequences are commonly used for channel estimation. (Cell) l Chinese users k The transmitted pilot sequence is as follows:

[0036] in, For the community l Chinese users k The length sent is τ pilot sequence, This is the pilot power for this user.

[0037] To ensure the orthogonality of pilot sequences for users within the same cell, the pilot sequence length is set to be greater than the number of users, i.e. τ ≥ K And satisfy and From the perspective of pilot pollution, the worst-case scenario is that all... L Users in one community k They all reuse the same pilot sequence, that is .

[0038] Therefore, during the channel estimation phase, the cell j The signal matrix received by the base station is:

[0039] in, express The additive white Gaussian noise matrix, whose elements follow an independent and identically distributed complex Gaussian distribution with zero mean and unit variance, can be estimated using the least squares method as follows:

[0040] in, Channel obtained based on LS estimation method The estimated vector.

[0041] Furthermore, based on the above least squares results, using the least mean square error estimation method, we obtain:

[0042] In the following sections, unless otherwise specified, both the LS and MMSE methods will be referred to as... express The estimate.

[0043] 2) Calculate the channel estimation error matrix based on the least squares and least mean square error estimation methods, and then calculate the expected value of the channel estimation error matrix. A closed expression; In step 2), the community j Chinese users k Relative channel estimation error for:

[0044] also, Expectations for:

[0045] Based on extensive algebraic operations, the expected value of the channel estimation error matrix is ​​obtained. The closed expression is:

[0046] 3) Based on the channel hardening effect, using the average of each user As the objective function, a pilot power allocation optimization problem is constructed, and the optimal pilot power allocation scheme is obtained by solving the pilot power allocation optimization problem. In step 3), to avoid dealing with iterative non-stationary optimization problems, this invention focuses on pilot power allocation for a single cell while fixing the pilot power of other cells. This scheme is called the single-cell PPA scheme. Clearly, the relative channel error estimate obtained from a single channel estimation cannot characterize the channel estimation performance over a period of time. Considering the characteristics of large-scale MIMO, the channel hardening effect can be utilized, and its expected value can be used instead. For performance metrics. Furthermore, to ensure fairness among all users within the target cell, the average [percentage] for each user is selected. As the objective function for evaluating the channel estimation performance of the system, the constrained PPA optimization problem P1 is thus constructed as:

[0047] in, Total pilot power for each cell, and for The lower and upper bound constraints are as follows: specifically, to ensure that each user in the cell has at least the minimum power available for pilot transmission, assume the minimum pilot power is... Subsequently, to prevent a single user from consuming excessive pilot power and significantly reducing the available power for other users, it is assumed that the maximum pilot power is... To ensure that at least one user in the target cell can be allocated the maximum pilot power, the following conditions must be met: K ≥2 and ,Right now Finally, to ensure that the channel estimation performance of the user allocated the maximum pilot power can be effectively improved, it is assumed that... Therefore, parameters The range of values ​​is When the above problem is solved, the optimal pilot power allocation is determined. Obviously, Its expression is complex and subject to constraints. This makes solving the problem quite challenging. Therefore, considering the characteristics of large-scale MIMO, we use... Alternative and relax The constraints, among which Defined as:

[0048] At this point, the optimization problem P1 can be rewritten as P2, that is:

[0049] At this point, solving the optimization problem using the Lagrange multiplier method yields the optimal solution. for:

[0050] in

[0051]

[0052]

[0053] At this point, the optimal solution in P2 can serve as an effective starting point for approximating the constrained optimization problem P1.

[0054] In step 3), the community j The users are divided into three groups, with the first group of users assigned the same minimum pilot power. for The lower bound; the second group of users are assigned the same maximum pilot power. The third group of users determined the pilot power by solving optimization problem P2 based on the remaining total power budget. For example... Figure 1 As shown, the specific algorithm flow can be summarized as follows: First, solve the unconstrained optimization problem P2 to obtain the initial optimal solution. Check whether it meets the requirements. If the condition is met, all users are assigned to group 3, and the algorithm terminates; otherwise, the pilot power is significantly lower than the specified value. or higher Users are assigned to either Group 1 or Group 2. This process is repeated iteratively for the remaining users and the remaining power budget until the pilot power of all users meets the constraints.

[0055] The comprehensive algorithm for the above iterations is shown in Table 1: Table 1

[0056] 4) Allocate MIMO resources according to the optimal pilot power allocation scheme.

[0057] Figure 2 The objective function values ​​for optimization problem P1 are presented in contour form, with red circles marking the solution points of the PPA algorithm and black asterisks marking the solution points of the fmincon method in MATLAB. The figure shows that the geometric centers of these two markings almost completely overlap, indicating that the solution obtained by the PPA algorithm is almost identical to the solution obtained by the numerical computation method. The computational complexity is analyzed by calculating the average complexity of the two methods. The average running time of each algorithm is based on the average of 1000 independent large-scale coefficient implementations. Table 2 shows the average running time values ​​of the PPA algorithm and the fmincon method under different numbers of users. It can be seen that when the number of users... K As the value increases, the average running time increases in all cases. Most importantly, compared to the fmincon method, the average running time of the PPA algorithm is significantly reduced. Therefore, the proposed PPA algorithm has the significant advantage of low computational complexity.

[0058] Table 2

[0059] Figure 3 and Figure 4The analytical calculations and Monte Carlo simulations of the expected user-average relative channel estimation error based on the LS and MMSE methods are presented, demonstrating the effectiveness of the theoretical analysis under the channel hardening effect. Furthermore, for LS estimation, as the frequency reuse factor increases, the performance gap between the PPA scheme and other power allocation schemes such as EPPA remains relatively stable, with PPA consistently showing a significant advantage over EPPA. For MMSE estimation, as the frequency reuse factor increases, the performance gap between PPA and EPPA gradually widens, highlighting the gain of PPA dynamic power allocation on MMSE estimation (see Table 3).

[0060] Table 3

[0061] Figure 5 The Joint User and Cell Grouping (JUCG) scheduling strategy aims to minimize the expected average relative channel estimation error per user within each cell by considering the allocation of pilot power for all users in a multi-cell system. Figure 6 The table shows that while the fmincon method is theoretically optimal, its computational complexity is extremely high, making it difficult to implement in engineering. JUCG significantly reduces the computational load through group optimization, with an average running time of 1%-10% of fmincon (e.g., K =70 times faster than 10 hours), combining high efficiency and practicality.

[0062] This invention has the following characteristics: This invention utilizes direct optimization targeting the expectation of relative channel estimation error to more accurately reflect the long-term average performance of channel hardening effects in large-scale MIMO systems, avoiding the random interference of single estimation errors. Furthermore, it uses user average... Minimize the objective function to take into account the channel estimation quality of all users, avoid the excessive bias of the traditional water-filling method on users with weak channels, and improve the overall performance balance of the system.

[0063] This invention derives closed-form solutions to unconstrained optimization problems using the Lagrange multiplier method, avoiding the high computational cost of traditional iterative algorithms, making it particularly suitable for large-scale MIMO systems with high real-time requirements. Furthermore, it employs a three-grouping strategy (fixed minimum power group, fixed maximum power group, and dynamically adjusted group) to quickly assign users to boundary groups or retain them in adjustable groups using heuristic rules, significantly reducing the number of iterations.

[0064] This invention supports multiple estimation methods, distinguishing between LS and MMSE estimation through a flag bit, and can flexibly adapt to base stations with different computing capabilities. Furthermore, the algorithm focuses on single-cell optimization while fixing the power of other cells, and can be extended to multi-cell collaborative optimization through distributed deployment, mitigating pilot pollution problems.

[0065] Example 2 refer toFigure 6 The MIMO resource allocation system considering channel estimation error described in this invention includes: The module is used to build a multi-cell MIMO system. It uses uplink orthogonal pilots to estimate the channel and obtain the least squares and least mean square error channel estimates. The determination module is used to determine the expected channel estimation error matrix based on the least squares and least mean square error estimation of the channel. A closed expression; The solution module is used to calculate the average value for each user based on the channel hardening effect. Minimize the pilot power allocation as the optimization objective, construct a pilot power allocation optimization problem, solve the pilot power allocation optimization problem, and obtain the optimal pilot power allocation scheme.

[0066] In this embodiment, the channel estimation error matrix expectation The closed expression is:

[0067] in, Indicates the community l Chinese users k With the community j Large-scale fading coefficients between mid-range base stations For the community l Chinese users k pilot power, M The number of antennas configured for the base station.

[0068] In this embodiment, the pilot power allocation optimization problem is expressed as:

[0069] in, Total pilot power for each cell, and for The lower and upper bound constraints, For the community j Chinese users k pilot power, K This refers to the number of users per antenna within a single cell.

[0070] In this embodiment, the pilot power allocation optimization problem is solved using the Lagrange multiplier method.

[0071] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0072] Example 3 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the MIMO resource allocation method considering channel estimation error, for example including: constructing a multi-cell MIMO system; estimating the channel using uplink orthogonal pilots to obtain least squares and least mean square error channel estimates; and determining the expected channel estimation error matrix based on the least squares and least mean square error channel estimates. The closed-form expression; based on the channel hardening effect, using the average of each user Minimization is the optimization objective. A pilot power allocation optimization problem is constructed, and solving this problem yields the optimal pilot power allocation scheme. The memory may include main memory, such as high-speed random access memory, or it may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which can be an industry-standard architecture bus, a peripheral component interconnection standard bus, or an extended industry-standard architecture bus. The bus can be categorized as an address bus, data bus, and control bus. The memory stores programs; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and it provides instructions and data to the processor.

[0073] Example 4 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the MIMO resource allocation method considering channel estimation error, for example including: constructing a multi-cell MIMO system; estimating the channel using uplink orthogonal pilots to obtain least squares and least mean square error channel estimates; and determining the expected channel estimation error matrix based on the least squares and least mean square error channel estimates. The closed-form expression; based on the channel hardening effect, using the average of each user Minimization is the optimization objective. A pilot power allocation optimization problem is constructed, and solving this problem yields the optimal pilot power allocation scheme. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0074] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0076] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0077] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0078] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0079] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0080] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A MIMO resource allocation method considering channel estimation error, characterized in that, include: A multi-cell MIMO system is constructed, and the channel is estimated using uplink orthogonal pilots to obtain the least squares and least mean square error channel estimates. Based on the least squares and least mean square error estimation of the channel, determine the expected channel estimation error matrix. A closed expression; Based on the channel hardening effect, the average of each user Minimize the pilot power allocation as the optimization objective, construct a pilot power allocation optimization problem, solve the pilot power allocation optimization problem, and obtain the optimal pilot power allocation scheme.

2. The MIMO resource allocation method considering channel estimation error according to claim 1, characterized in that, The channel estimation error matrix is ​​expected The closed expression is: in, Indicates the community l Chinese users k With the community j Large-scale fading coefficients between mid-range base stations For the community l Chinese users k pilot power, M The number of antennas configured for the base station.

3. The MIMO resource allocation method considering channel estimation error according to claim 1, characterized in that, The pilot power allocation optimization problem is expressed as: in, Total pilot power for each cell, and for The lower and upper bound constraints, For the community j Chinese users k pilot power, K This refers to the number of users per antenna within a single cell.

4. The MIMO resource allocation method considering channel estimation error according to claim 1, characterized in that, The pilot power allocation optimization problem is solved using the Lagrange multiplier method.

5. A MIMO resource allocation system considering channel estimation error, characterized in that, include: The module is used to build a multi-cell MIMO system. It uses uplink orthogonal pilots to estimate the channel and obtain the least squares and least mean square error channel estimates. The determination module is used to determine the expected channel estimation error matrix based on the least squares and least mean square error estimation of the channel. A closed expression; The solution module is used to calculate the average value for each user based on the channel hardening effect. Minimize the pilot power allocation as the optimization objective, construct a pilot power allocation optimization problem, solve the pilot power allocation optimization problem, and obtain the optimal pilot power allocation scheme.

6. The MIMO resource allocation system considering channel estimation error according to claim 5, characterized in that, The channel estimation error matrix is ​​expected The closed expression is: in, Indicates the community l Chinese users k With the community j Large-scale fading coefficients between mid-range base stations For the community l Chinese users k pilot power, M The number of antennas configured for the base station.

7. The MIMO resource allocation system considering channel estimation error according to claim 5, characterized in that, The pilot power allocation optimization problem is expressed as: in, Total pilot power for each cell, and for The lower and upper bound constraints, For the community j Chinese users k pilot power, K This refers to the number of users per antenna within a single cell.

8. The MIMO resource allocation system considering channel estimation error according to claim 5, characterized in that, The pilot power allocation optimization problem is solved using the Lagrange multiplier method.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the MIMO resource allocation method considering channel estimation error as described in any one of claims 1-4.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the MIMO resource allocation method considering channel estimation error as described in any one of claims 1-4.

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