A high-speed joint resource allocation method in an active IRS-assisted cell-free massive MIMO system

Abstract

The application provides a high-rate joint resource allocation method in an active IRS-assisted cell-free massive MIMO system, comprising the following steps: establishing an active intelligent reflecting surface IRS-assisted cell-free massive MIMO system uplink model. Jointly optimizing power allocation, IRS reflection coefficient and received beamforming sum rate maximization problem modeling. The problem is divided into user power allocation, IRS reflection system design and received beamforming sub-problems. The alternating optimization method is used to design the rate optimization algorithm and resource allocation scheme. The application firstly solves the user power allocation scheme based on the continuous convex approximation method, then solves the active IRS reflection coefficient based on the fractional programming method, and finally designs the received beamforming based on the generalized Rayleigh entropy closed-form solution. The application can effectively improve the sum rate of the active IRS-assisted cell-free massive MIMO system, has the advantage of high system rate performance, and can effectively utilize various resources of the communication system.

Description

Technical Field

[0001] This invention belongs to the field of mobile communications and relates to a resource allocation method for mobile communication systems, particularly a high-rate joint resource allocation method for a cellless massive MIMO system assisted by an active intelligent reflector IRS. Background Technology

[0002] The capacity of communication networks is projected to increase a thousandfold over the next decade, making ubiquitous wireless connectivity a reality. However, the main challenges facing future wireless communications include high network complexity, high hardware costs, and ever-increasing energy consumption. To address these challenges, there is an urgent need to research and develop efficient, spectrum-friendly, and resource-saving wireless network solutions. Among numerous candidate technologies, intelligent reflective surface (IRS) stands out due to its advantages such as low cost, low power consumption, programmability, and ease of deployment.

[0003] An IRS (Inductively Coupled Surface) is an artificial electromagnetic surface structure with programmable electromagnetic properties, composed of a large number of carefully designed electromagnetic units. By applying control signals to the adjustable components on these units, their electromagnetic properties can be adjusted in real time. This flexible programming capability makes it possible to intelligently and actively control spatial electromagnetic waves, thereby enabling precise control of parameters such as the amplitude, phase, polarization, and frequency of the electromagnetic field. To overcome the inherent limitations of passive IRS, especially in harsh transmission environments with low signal-to-noise ratios or severe path loss in reflected channels, a novel active IRS architecture has been introduced. Active IRS integrates a reflective power amplifier in each IRS unit, achieving phase modulation and high-gain amplification of the reflected signal, thus effectively compensating for path loss caused by multiplicative fading.

[0004] In active IRS-assisted cellless massive MIMO systems, power allocation, IRS reflection coefficient design, and receiver beamforming design are generally interdependent and closely related in addressing the resource optimization problem of maximizing sum and rate. However, research on high-rate resource allocation in active IRS-assisted cellless massive MIMO is relatively limited. Therefore, there is an urgent need to develop a joint resource allocation method based on maximizing sum and rate in active IRS-assisted cellless massive MIMO. Summary of the Invention

[0005] 1. The technical problem to be solved:

[0006] How to allocate joint resources based on maximizing and rate in cell-free massive MIMO with active IRS-assisted resource allocation.

[0007] 2. Technical Solution:

[0008] To address the above problems, this invention provides a high-rate joint resource allocation method for a cell-free massive MIMO system assisted by an active IRS, comprising the following steps:

[0009] Step S1: Establish an uplink model for a cellless massive MIMO system assisted by an active intelligent reflector IRS; in this system, R active IRSs equipped with M reflective elements assist B access points (APs) equipped with N antennas to provide services to K single-antenna users. The channels between the APs, IRSs and users follow a Ricean distribution. Each AP performs beamforming processing on the received uplink signal and then transmits it back to the central processing unit (CPU) via the forward backhaul link for merging and decoding.

[0010] Step S2: Model the problem of maximizing the sum of power allocation, IRS reflection coefficient, and received beamforming; the optimization problem is modeled as follows:

[0011] ,

[0012] in: For the first The signal-to-interference-plus-noise ratio (SIR) of each user, k=1,…,K; h r,k Indicates the first The IRS and the first Channel vectors between users, r=1,…,R; For the first thermal noise variance at each RIS point; p k Indicates the first Transmission power per user, P max,k This indicates the corresponding maximum transmit power; This indicates the maximum reflected power of the active IRS. Representing the The reflection coefficient matrix of an active IRS; This indicates the maximum reflection amplitude of each element in the IRS; For the corresponding to the first The first user's Beamforming for each AP terminal.

[0013] Step S3: Decompose the problem into user power allocation, IRS reflection system design, and receive beamforming sub-problem.

[0014] Step S4: Design a rate optimization algorithm and resource allocation scheme using the alternating optimization method; first, given... Solve for the power distribution coefficient { }, then give Solve for the reflection coefficient matrix of the active IRS ; finally given Solving for receive beamforming Based on the alternating optimization method, fractional programming, continuous convex approximation, generalized Rayleigh entropy, and standard convex optimization tools are used to solve the above subproblems, and the algorithm implementations are given. The suboptimal solution.

[0015] Given Solve for the power distribution coefficient { The specific method is as follows:

[0016] when Given a variable, an auxiliary variable is introduced. and The rate maximization problem is transformed into:

[0017]

[0018] in , For the first The AP and the first Channel matrix between IRS No. The channel between an AP and the i-th user This represents the noise variance.

[0019] The standard convex optimization tool solver is used to find the user's optimal power allocation scheme in the current iteration.

[0020] Given Solve for the reflection coefficient matrix of the active IRS The specific method is as follows: when Once given, introduce auxiliary variables. Using fractional programming, the original problem is transformed into solving the following convex optimization problem:

[0021]

[0022] in: , , , , , , , , ;

[0023] Use standard convex optimization tools to solve for the design scheme of the active IRS reflection coefficient in the current iteration.

[0024] when After fixing, solve the receiving beamforming problem. as follows:

[0025] ,

[0026] make , , , , ,

[0027] Through mathematical transformation, the signal-to-noise ratio in the objective function is expressed as: ,in: ;

[0028] The optimal solution is obtained by considering the generalized Rayleigh entropy form of the signal-to-noise ratio described above. for The eigenvector corresponding to the largest eigenvalue.

[0029] 3. Beneficial effects:

[0030] The resource allocation method for maximizing sum and rate in a cell-free massive MIMO system provided by this invention has the advantages of high system rate performance and can effectively utilize various resources of the communication system. This method fully utilizes the inherent structure of the original optimization problem. First, it decomposes the complex joint optimization problem into a power allocation subproblem, an active IRS reflection coefficient subproblem, and a receive beamforming subproblem. Then, it introduces auxiliary variables to transform the power allocation subproblem into a convex optimization problem. Next, it uses fractional programming to transform the active IRS reflection coefficient design subproblem into a convex optimization problem. Finally, it uses generalized Rayleigh entropy to solve the receive beamforming problem. Based on the above iterative optimization algorithm, a joint resource allocation scheme is obtained. Attached Figure Description

[0031] Figure 1 This is a flowchart of a high-rate resource allocation method in a cellless massive MIMO system assisted by an active IRS, as described in an embodiment of the present invention.

[0032] Figure 2 The figures show simulation results for different numbers of active IRSs for the optimization scheme proposed in the embodiments of the present invention.

[0033] Figure 3 The figures show simulation results of the optimization scheme proposed in this embodiment of the invention under different numbers of IRS elements. Detailed Implementation

[0034] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] To improve system speed in the uplink of an active IRS-assisted cellless massive MIMO system, a high-speed joint resource allocation method for such systems is proposed, such as... Figure 1 As shown, it includes the following steps:

[0036] Step S1: Establish an uplink model for a cellless massive MIMO system assisted by an active intelligent reflector (IRS).

[0037] The present invention relates to an active IRS-assisted cellless massive MIMO system, specifically an active IRS-assisted massive MIMO system. In this system, R active IRSs equipped with M reflectors assist B access points (APs) equipped with N antennas to provide services to K single-antenna users. The channels between the APs, IRSs, and users follow a Ricean distribution. Each AP performs beamforming processing on the received uplink signal and then transmits it back to the CPU via a forward backhaul link for combining and decoding. The decoded signal can be represented as:

[0038]

[0039] in: In order to receive signals, For the first The AP and the first Channel matrix between IRS No. The channel between an AP and the i-th user and They represent the first time. The RIS and the first The additive white Gaussian noise vector at each base station.

[0040] Step S2: Model the problem of jointly optimizing power allocation, IRS reflection coefficient and receiver beamforming to maximize the rate.

[0041] In the uplink of an active IRS-assisted cellless massive MIMO system, the rate maximization problem is as follows:

[0042]

[0043] in: For the first Signal-to-interference-plus-noise ratio per user.

[0044] Step S3: Decompose the problem into user power allocation, IRS reflection system design, and receive beamforming sub-problem.

[0045] Step S4: Design a rate optimization algorithm and resource allocation scheme using the alternating optimization method.

[0046] 1) Given Solve

[0047] when Given the given information, the original problem can be transformed into:

[0048]

[0049] in ;

[0050] By introducing auxiliary variables and The above subproblem can be transformed into:

[0051]

[0052] The atomic problem is transformed into a convex optimization problem, and the optimal power allocation scheme for the user in the current iteration is solved using the standard convex optimization tool solver.

[0053] 2) Given Solve

[0054] Given Solve The subproblem can be modeled as:

[0055]

[0056] To facilitate the decoupling of variables, auxiliary variables are first introduced. , ,make , , Then, through mathematical transformation, the original objective function can be transformed into:

[0057]

[0058] Then introduce auxiliary variables. By using fractional programming, the original problem is transformed into solving the following convex optimization problem.

[0059]

[0060] The original problem is transformed into a convex optimization problem, and standard convex optimization tools are used to solve for the design scheme of the active IRS reflection coefficient in the current iteration.

[0061] 3) Fixed Solve

[0062] when After fixing, solve The subproblems are as follows:

[0063]

[0064] For ease of solution, let , , , , .

[0065] Through mathematical transformation, the signal-to-noise ratio in the objective function can be expressed as:

[0066]

[0067] in: For the form of the generalized Rayleigh entropy described above, the optimal solution is obtained. for The eigenvector corresponding to the largest eigenvalue.

[0068] Example

[0069] The effectiveness of the resource allocation method for maximizing sum and rate proposed in this invention is verified through simulation on the Matlab platform. The simulation parameters are set as follows: number of APs. Number of active IRS Number of users Number of AP antennas Number of IRS components Thermal noise power IRS power gain Maximum reflected power .

[0070] Figure 2 The sum rate of a cellless massive MIMO system was compared under different numbers of active IRSs. The figure shows that the sum rate improves with increasing number of active IRSs. This is because, on the one hand, the added active IRSs provide additional multipath propagation between the base station and users, improving diversity gain; on the other hand, by optimizing the amplification factor and phase of the active IRS elements, a high-gain directional beam can be provided to enhance the signal reception strength at the intended destination and suppress interference to non-target users.

[0071] Figure 3 The sum rate of a cellless massive MIMO system was compared under different numbers of IRS elements. As shown in the figure, the sum rate increases with the number of IRS elements. More reflective elements in the IRS provide greater diversity gain, effectively eliminating thermal noise and inter-user interference, thus improving the received signal-to-noise ratio at the base station. Furthermore, increasing the scale of IRS deployment typically provides more degrees of freedom to generate directional beams, further eliminating inter-user interference and improving system performance.

[0072] In summary, the method proposed in this invention can effectively improve the sum and rate of active IRS-assisted cellless massive MIMO systems. Furthermore, the implementation steps are relatively simple, and the results are significant. This fully demonstrates the effectiveness of the joint resource allocation method based on maximizing sum and rate in active IRS-assisted cellless massive MIMO systems proposed in this invention.

Claims

1. A high-rate joint resource allocation method in an active IRS-assisted cellless massive MIMO system, characterized in that: Includes the following steps: Step S1: Establish an uplink model for a cellless massive MIMO system assisted by an active intelligent reflector IRS; in this system, R active IRSs equipped with M reflectors assist B access points (APs) equipped with N antennas to provide services to K single-antenna users. The channels between the APs, IRSs and users follow a Ricean distribution. Each AP performs beamforming processing on the received uplink signal and then transmits it back to the central processing unit (CPU) via the forward backhaul link for merging and decoding. Step S2: Model the problem of maximizing the sum of power allocation, IRS reflection coefficient, and received beamforming; the optimization problem is modeled as follows: ， in: For the first The signal-to-interference-plus-noise ratio (SIR) of each user, k=1,…,K; h r,k Indicates the first The IRS and the first Channel vectors between users, r=1,…,R; For the first thermal noise variance at each RIS point; p k Indicates the first Transmission power per user, P max,k This indicates the corresponding maximum transmit power; This indicates the maximum reflected power of the active IRS. Representing the The reflection coefficient matrix of an active IRS; This indicates the maximum reflection amplitude of each element in the IRS; For the corresponding to the first The first user's Receive beamforming at each AP terminal; Step S3: Decompose the problem into user power allocation, IRS reflection system design, and receive beamforming problem; Step S4: Design a rate optimization algorithm and resource allocation scheme using the alternating optimization method; first, given... Solve for the power distribution coefficient { }, then give Solve for the reflection coefficient matrix of the active IRS ; finally given Solving for receive beamforming Based on the alternating optimization method, fractional programming, continuous convex approximation, generalized Rayleigh entropy, and standard convex optimization tools are used to solve the above subproblems, and the algorithm implementations are given. The suboptimal solution.

2. The high-rate joint resource allocation method in an active IRS-assisted cellless massive MIMO system as described in claim 1, characterized in that: Given Solve for the power distribution coefficient { The specific method is as follows: when Given a variable, an auxiliary variable is introduced. and The rate maximization problem is transformed into: ， in , For the first The AP and the first Channel matrix between IRS No. The channel between an AP and the i-th user For noise variance; The standard convex optimization tool solver is used to find the user's optimal power allocation scheme in the current iteration.

3. The high-rate joint resource allocation method in an active IRS-assisted cellless massive MIMO system as described in claim 1, characterized in that: Given Solve for the reflection coefficient matrix of the active IRS The specific method is as follows: when Once given, introduce auxiliary variables. Using fractional programming, the original problem is transformed into solving the following convex optimization problem: ， in: , , , , , , , , ; Use standard convex optimization tools to solve for the design scheme of the active IRS reflection coefficient in the current iteration.

4. The high-rate joint resource allocation method in an active IRS-assisted cellless massive MIMO system as described in claim 1, characterized in that: when After fixing, solve the receiving beamforming problem. as follows: ， make , , , , , Through mathematical transformation, the signal-to-noise ratio in the objective function is expressed as: ,in: ; The optimal solution is obtained by considering the generalized Rayleigh entropy form of the signal-to-noise ratio described above. for The eigenvector corresponding to the largest eigenvalue.

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