A method of joint precoding and stream number allocation
By employing joint precoding and stream allocation methods in cellless networks, the problem of high complexity in precoding technology is solved, thereby maximizing system capacity and achieving efficient utilization of communication resources.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TONGJI UNIV
- Filing Date
- 2023-10-30
- Publication Date
- 2026-05-12
AI Technical Summary
In cellless networks, precoding techniques are highly complex and stream allocation methods are inflexible, leading to a surge in computational load and limiting the improvement of system capacity.
A joint precoding and stream allocation method is adopted. By establishing a system capacity objective function and using the weighted minimum mean square error method to transform the problem into a convex function, and combining it with the block coordinate descent optimization method, the computational complexity is reduced and the precoding and stream allocation are optimized.
While reducing computational load, the system capacity is maximized and the efficiency of communication resource utilization is improved.
Smart Images
Figure CN117479282B_ABST
Abstract
Description
Technical Field
[0001] This invention pertains to mobile communication technology, and particularly relates to a joint precoding and stream allocation method for cellless networks. Background Technology
[0002] In cell-free networks, the number of data streams received by a user is actually limited by the number of their receiving antennas. However, the number of base station antennas is usually much greater than the number of user-received streams. Therefore, system capacity can be improved by allocating an optimal number of data streams to each base station. However, there are currently no documents or patents for stream allocation in cell-free networks. Furthermore, existing precoding techniques in cell-free networks suffer from high complexity and computational load, which increases dramatically with the number of base stations, base station antennas, and users. Summary of the Invention
[0003] In view of the high complexity of existing precoding techniques and the inflexible flow number allocation methods, the purpose of this invention is to provide a joint precoding and linear flow number allocation method for cellless networks, which reduces computational complexity and improves system capacity.
[0004] To achieve the above objectives, the technical solution of the present invention is as follows.
[0005] The objective of this invention is achieved through the following technical solution:
[0006] A joint precoding and stream allocation method for cell-free MIMO systems is characterized by:
[0007] The method establishes an objective function regarding system capacity, including the maximum transmit power of each base station. and the maximum number of streams received per user constraint As a constraint, the optimal joint precoding and stream number are obtained by maximizing the system capacity;
[0008] in:
[0009] For base stations i The set of user terminals (UEs) providing the service, where k is the user identifier. From base station i to user k The precoding matrix, For base stations i Maximum limited transmit power Let k be the set of all base stations serving user k. For base stations i To users k The actual number of data streams sent. Let k be the number of receiving antennas for user k.
[0010] In the above technical solution, the method uses the weighted least mean square error method to transform the system capacity maximization problem into the following convex function to reduce the problem-solving complexity, and the optimization variable is... :
[0011]
[0012] The constraints are satisfied:
[0013]
[0014]
[0015]
[0016]
[0017]
[0018]
[0019] in: For base stations i To users k The stream number allocation matrix, Here is the auxiliary weight matrix for user k. This indicates the base station i To users k The channel matrix, Let blkdiag be the minimum mean square error reception matrix for user k, where blkdiag represents the matrix arranged diagonally in blocks. j and p represents the row and column indices of the matrix. K This represents the total number of users.
[0020] In the above technical solution, an optimization method based on block coordinate descent is used to solve the transformed problem. The solution steps include:
[0021] For each user k, calculate the minimum mean square error reception matrix. :
[0022] ;
[0023] For each user k, calculate the auxiliary weight matrix. :
[0024] ;
[0025] Parallel computation of the precoding matrix from each base station to each user :
[0026] ;
[0027] Calculate the flow weight matrix from each base station to each user. :
[0028] ;
[0029] For each user k, arrange all its stream weight matrices in a block diagonal manner to obtain the matrix. Collect the indices of the diagonal elements that are less than zero in sequence to form a set. ;
[0030] Using sets Construct a stream allocation matrix for each user :
[0031]
[0032] Repeat the above steps until the system capacity converges, obtaining the optimal joint precoding and stream allocation matrix. Used for data transmission;
[0033] In the above steps: Let k be the precoding matrix. blkdiag indicates that the matrix is arranged in a block diagonal manner; Let K be the channel matrix for user k. For base stations i To users k The stream number allocation matrix; ; For Lagrange multipliers; The To the The columns are identity matrices, and all other elements are zero; express The l One diagonal element.
[0034] Secondly, this application proposes a cell-free MIMO system, which includes a serving base station and cooperating base stations. The serving base station obtains the channel matrix of each cooperating base station, uses any of the methods described above to obtain the optimal joint precoding and stream allocation matrix, and sends it to the cooperating base station. The cooperating base station uses the received joint precoding and stream allocation matrix to transmit data.
[0035] Thirdly, this application proposes a serving base station that utilizes the channel matrix of each cooperating base station to obtain the optimal joint precoding and stream allocation matrix using any of the methods described above, and sends it to the cooperating base station for further data transmission.
[0036] The beneficial effects of this invention are:
[0037] By using the method provided in this invention, each base station can obtain precoding and stream allocation with less computational effort than existing technologies, thereby maximizing system capacity. Attached Figure Description
[0038] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.
[0039] Figure 1 , one The joint precoding and stream allocation process in this implementation method is illustrated by taking three base stations as an example.
[0040] Figure 2 , one In the four-base station collaborative transmission scenario of this implementation method, the proposed precoding technology is compared with the existing precoding technology based on continuous convex approximation—system capacity comparison;
[0041] Figure 3 , one In the four-base station collaborative transmission scenario of this implementation method, the proposed precoding technology is compared with the existing precoding technology based on continuous convex approximation in terms of running time.
[0042] Figure 4 , one In the four-base station collaborative transmission scenario of this implementation, the proposed joint precoding and stream allocation scheme outperforms the system capacity performance of individual precoding methods. Detailed Implementation
[0043] In the following content, the superscript " "" represents the transpose of the matrix, with the superscript " "" represents the conjugate transpose of the matrix.
[0044] This solution is applied in a cellless MIMO (Multi-input Multi-output) system, where the system has... Each TRP (Transmit Receive Point) or BS (Base Station) has [number of base stations]. Root antenna, service One user, and each user is equipped with Root antenna. and These represent the sets of base stations and users, respectively. Pre-assigned service users The TRP set, For base stations The set of UEs served. If each base station operates in non-coherent cooperative transmission mode, then the user... Received signal at the location It can be modeled as if the user's received signal can be modeled as:
[0045]
[0046] in: , and Representing base stations and users The channel matrix, precoding matrix, and data stream signals between them. Indicates base station i To users k The stream number allocation matrix, and A diagonal matrix whose diagonal elements are either zero or one. Indicates base station i To users k The actual number of data streams sent. For users The received noise vector at point follows a complex Gaussian distribution, i.e. .
[0047] user precoding matrix It can be represented as:
[0048]
[0049] Service users base station to user Channel matrix It can be represented as:
[0050]
[0051] Users can be obtained The rate is:
[0052]
[0053] in, .
[0054] To address the issues of insufficient utilization of communication resources in user stream allocation in existing cellless networks and the high complexity and computational burden of precoding techniques, this paper proposes a joint precoding and stream allocation method for cellless MIMO systems. This method establishes an objective function regarding system capacity, including the maximum transmit power of each base station. and the maximum number of streams received per user constraint As a constraint, the optimal joint precoding and stream number are obtained by maximizing the system capacity.
[0055] The objective function is:
[0056]
[0057] The following constraints must be met:
[0058]
[0059]
[0060] By maximizing system capacity to obtain optimal precoding and achieving stream number adaptation, communication resources can be fully utilized.
[0061] When solving the above system capacity maximization problem, the weighted minimum mean square error method is used to make the problem equivalent to the following problem, thereby reducing the problem-solving complexity:
[0062]
[0063] The constraints are satisfied:
[0064]
[0065]
[0066]
[0067]
[0068]
[0069]
[0070] in: For base stations i To users k The stream number allocation matrix, Here is the auxiliary weight matrix for user k. This indicates the base station i To users k The channel matrix, Let k be the set of all base stations serving user k. Let blkdiag be the minimum mean square error reception matrix for user k, where blkdiag represents the matrix arranged diagonally in blocks. j and p represents the row and column indices of the matrix. K This represents the total number of users.
[0071] By improving the problem-solving method, the computational complexity of the precoding matrix can be reduced, and the stream number calculation can be transformed into a linear calculation, thereby further reducing the computational load. Specifically, an optimization method based on block coordinate descent is used to solve the transformed problem, and the steps include:
[0072] S10. For each user k, calculate the minimum mean square error receiver matrix. :
[0073] ;
[0074] S20. For each user k, calculate the auxiliary weight matrix. :
[0075] ;
[0076] S30. Parallel computation of the precoding matrix from each base station to each user. :
[0077] ;
[0078] In step S30, an optimization method based on block coordinate descent can be used to calculate the precoding matrix from each base station to each user, thereby reducing the computational complexity of the precoding matrix. , which are intermediate variables for convenient calculation. Since it is a Lagrange multiplier, it can be solved using the bisection method. The To the The columns are identity matrices, and the rest are zero.
[0079] S40. After obtaining the precoding matrix, calculate the stream weight matrix from each base station to each user. :
[0080]
[0081] S50. Using the calculated flux weight matrix, establish the following optimization problem:
[0082]
[0083] The constraints are satisfied:
[0084]
[0085]
[0086] The stream allocation matrix for each user can be obtained. :
[0087]
[0088] in: express The l One diagonal element, for Less than zero The indices of the diagonal elements. .
[0089] The flux number distribution matrix obtained from the above solution This can be viewed as a weighted matrix of the number of streams serving all base stations for each user k. The matrix is obtained by arranging the blocks diagonally. , obtain in sequence The indices of the diagonal elements that are less than zero form a set. Then use sets Construct the stream number allocation matrix Soon Subscript in set The diagonal elements are set to 1, and the remaining diagonal elements are set to zero. This process greatly reduces the computational difficulty of stream allocation, achieving a computational complexity of linear order.
[0090] Repeat steps S10-S50 until the system capacity converges, obtaining the optimal joint precoding and stream allocation matrix. Used for data transmission.
[0091] The above iterative calculation process pre-initializes the base station. i To users k Stream number allocation matrix and precoding matrix This ensures that the user's maximum number of received streams and the base station's maximum transmit power limits are met, respectively.
[0092] When applying the above method to a cell-free MIMO system, the base stations in the system are divided into serving base stations and cooperating base stations. Each cooperating base station first sets its own channel matrix... The data is sent to the serving base station, which first initializes the base station. i To users k Stream number allocation matrix and precoding matrix This ensures that the maximum number of received streams for users and the maximum transmit power limit for the base station are met, respectively. Then, the precoding and stream allocation matrices are solved to maximize system capacity. Taking the above solution method as an example, steps S10-S50 can be iteratively calculated on the serving base station, and the calculation results are then combined with the precoding and stream allocation matrices. It is sent to each cooperating base station to reduce the computational load on the cooperating base stations and the amount of information exchange between the cooperating base stations and the service base station, and to facilitate system maintenance.
[0093] Example
[0094] As shown in Figure 1, taking three base stations as an example, one base station acts as the serving base station, and the other two base stations act as cooperating base stations. After obtaining the channel information of the cooperating base stations, the serving base station calculates the joint precoding and stream allocation matrix using the method of this invention. And send it to the cooperating base station, the cooperating base station uses the received data. Sending data will maximize system capacity.
[0095] Figure 2 shows a comparison of the proposed precoding technique with existing precoding techniques based on continuous convex approximation in a four-base station cooperative transmission scenario in one implementation – a system capacity comparison. Figure 3 This paper compares the proposed precoding technique with existing precoding techniques based on continuous convex approximation in a four-base station collaborative transmission scenario—specifically, their runtime. Figure 2 and Figure 3 It can be seen that the method in this case can achieve the same system capacity as the precoding technique based on the continuous convex approximation method with 1% of the computation time, and the method in this case can improve the system capacity compared with the classic maximum ratio transmission precoding technique.
[0096] Figure 4 illustrates the system capacity performance of the proposed joint precoding and stream allocation scheme compared to individual precoding methods in a four-base station cooperative transmission scenario according to one embodiment of the present invention. As can be seen from Figure 4, the joint precoding and stream allocation scheme proposed in this invention achieves higher system capacity than individual precoding schemes, demonstrating the effectiveness of the method.
[0097] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods or systems disclosed herein can be implemented using software plus necessary general-purpose hardware, or they can be implemented using dedicated hardware including dedicated integrated circuits, dedicated CPUs, dedicated memory, dedicated components, etc. Generally, any function performed by a computer program can be easily implemented using corresponding hardware, and the specific hardware structure used to implement the same function can be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the purposes of this disclosure, software program implementation is more often a preferred implementation method.
[0098] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.
Claims
1. A joint precoding and stream allocation method for cell-free MIMO systems, characterized in that: The method establishes an objective function regarding system capacity, including the maximum transmit power of each base station. Maximum number of streams received per user constraint As a constraint, the optimal joint precoding and stream number are obtained by maximizing the system capacity; The method described above uses a weighted minimum mean square error approach to transform the system capacity maximization problem into the following problem, thereby reducing the complexity of solving the problem: The constraints are satisfied: in: For base stations i A collection of user terminals that provide services. k For user identification, Let k be the precoding matrix. Assign a matrix to the stream number of user k. From base station i to user k The precoding matrix, For base stations i Maximum limited transmit power Let k be the set of all base stations serving user k. For base stations i To users k The actual number of data streams sent. Let k be the number of receiving antennas for user k. Assign a stream number matrix from base station i to user k. Here is the auxiliary weight matrix for user k. This represents the channel matrix from base station i to user k. Let blkdiag be the minimum mean square error reception matrix for user k, where blkdiag represents the matrix arranged diagonally in blocks. j and p represents the row and column indices of the matrix. K Total number of users; Let be the channel matrix for user k.
2. The method according to claim 1, characterized in that, An optimization method based on block coordinate descent is used to solve the transformed problem. The solution steps include: For each user k, calculate the minimum mean square error reception matrix. : ； For each user k, calculate the auxiliary weight matrix. : ； Parallel computation of the precoding matrix from each base station to each user : ； Calculate the flow weight matrix from each base station to each user. : ； For each user k, arrange all its stream weight matrices in a block diagonal manner to obtain the matrix. Collect the indices of the diagonal elements that are less than zero in sequence to form a set. ; Using sets Construct a stream allocation matrix for each user : Repeat the above steps until the system capacity converges, obtaining the optimal joint precoding and stream allocation matrix. Used for data transmission; In the above steps: ; For Lagrange multipliers; The To the The columns are identity matrices, and all other elements are zero; express The l-th diagonal element.
3. A cell-free MIMO system, the system comprising a serving base station and a cooperating base station, characterized in that: The serving base station obtains the channel matrix of each cooperating base station, uses the method described in any one of claims 1-2 to obtain the optimal joint precoding and stream allocation matrix, and sends it to the cooperating base station. The cooperating base station uses the received joint precoding and stream allocation matrix to transmit data.
4. A serving base station, characterized in that: The serving base station uses the channel matrix of each cooperating base station to obtain the optimal joint precoding and stream allocation matrix using the method described in any one of claims 1-2, and sends it to the cooperating base station for further data transmission.