A beam joint optimization method based on RIS-assisted decellularized mMIMO system

Abstract

The present invention discloses a beam joint optimization method based on a RIS-assisted decellularized mMIMO system. Based on the characteristic of active RIS in amplifying signals, the present invention uses an active RIS-assisted downlink decellularized mMIMO system to enhance signal strength. Based on the data expression received by the downlink user, the downlink rate expression of the active RIS-assisted decellularized mMIMO system is given; then, considering the maximum power limit of the access point and the active RIS, an alternating solution algorithm is designed, including the use of quadratic transformation technology and dual gradient method, which solves the downlink and rate maximization optimization problem, and jointly optimizes the downlink access point beamforming sequence and the active RIS reflection beam. The present invention reduces the impact of multiplicative fading on the RIS-assisted channel, effectively solves the problem of inter-user interference and noise interference amplification caused by the use of active RIS, and improves system performance.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a beam joint optimization method based on a RIS-assisted decellularized mMIMO system. Background Art

[0002] In recent years, as a new user-centric network architecture, decellularized mMIMO technology has become one of the key technologies for realizing 5G and industrial Internet of Things. The decellularized mMIMO system adopts a cell-free deployment mode, that is, by deploying a large number of access points APs around the user to replace the central base station of the cellular cell. This deployment mode not only effectively solves problems such as inter-cell interference and handover, but also shortens the communication distance and significantly reduces the path loss. In addition, a large number of APs also provide additional spatial degrees of freedom and diversity gain, thereby improving the system capacity. These advantages have led to the extensive research of the decellularized mMIMO system.

[0003] Reconfigurable smart reflector (RIS) is a plane composed of multiple passive reflective units. Its function is to reflect the passing signal after applying a phase. Therefore, using RIS in the decellularized mMIMO system can achieve coherent superposition of signals and enhance signal strength. At the same time, its passive characteristics make it not increase additional system energy consumption. However, in the RIS-assisted communication system, the channel reflected by RIS is the product of AP-RIS channel and RIS-user channel. This multiplicative fading makes the passive RIS extremely limited in improving the system performance. Therefore, active RIS with the characteristic of amplifying signals is used to replace passive RIS to further improve performance. A large number of studies have also shown that active RIS effectively improves the system rate, especially under harsh propagation environment conditions. Although active RIS amplifies the signal of the desired user, it also amplifies the signals of other users and the noise of RIS, causing more serious interference between users and noise interference. Summary of the invention

[0004] To solve the above problems, the present invention proposes a beam joint optimization method based on RIS-assisted decellularized mMIMO system, which jointly optimizes the beamforming at the AP and the active RIS reflection beam by solving a downlink and rate maximization optimization problem to maximize the downlink and rate.

[0005] A beam joint optimization method based on a RIS-assisted decellularized mMIMO system comprises the following steps:

[0006] Step (1) establishing a Rayleigh fading channel model for the decellularized mMIMO system assisted by active RIS;

[0007] Step (2) based on the Rayleigh fading channel model, an expression of the received data of the user in the downlink data transmission phase is given, thereby deriving an expression of the downlink user rate;

[0008] Step (3) Taking the user's sum rate as the objective function, the downlink AP beam sequence and the active RIS reflection coefficient as the optimization variables, a sum rate maximization optimization problem with AP and active RIS power constraints is given;

[0009] Step (4) proposes an alternating optimization algorithm, using the primal dual subgradient method to solve the downlink AP beamforming and active RIS reflection beam respectively.

[0010] Furthermore, the Rayleigh fading channel model in step (1) ,in, Indicates AP and The synthetic channel sequence between users is Indicates AP and The sequence of direct channels between users, Indicates the number of antennas each AP has. Indicates The channel sequence between each user and the active RIS, An acronym for Active RIS. Indicates the number of reflection units in the active RIS. Indicates The channel matrix between APs and active RIS, Indicates The channel sequence between the first antenna of each AP and the active RIS; Indicates The first Channel sequence between the root antenna and the active RIS; represents the reflection coefficient matrix of active RIS, represents the amplification of the first unit of the active RIS, Indicates the active RIS The amplification of the unit, Indicates the phase of the first unit of the active RIS, Indicates the active RIS The phase of each unit, represents the vector diagonalization operation, Indicates that the base is a natural number The exponential operation of represents the conjugate transpose operation, Represents a complex set.

[0011] Furthermore, in the step (2) during the downlink data transmission phase, Data received by users The expression is ,in, Indicates that all APs and The synthetic channel sequence between users is Indicates that all APs and The precoding sequence between users, Indicates The data symbol of each user, and ; Indicates The data symbol of each user, Indicates the number of users, Indicates that all APs and The precoding sequence between users, represents the noise sequence of active RIS, whose elements are independent and identically distributed , represents the noise power of the active RIS, Indicates The noise of each user follows the distribution , represents the noise power of the user, represents the expectation operation, represents the conjugate operation, represents a cyclically symmetric complex Gaussian distribution, Represents a complex set.

[0012] Further, based on the user data expression obtained in step (2), the first The downlink rate of a user is , whose expression is ,in, represents the logarithm operation with base 2, Indicates The signal-to-interference-noise ratio of a user is expressed as ,in, represents the Euclidean norm; according to the rate expression of each user in step (2), the sum rate expression of the users in step (3) is , considering the power constraints at the AP and active RIS, the downlink and rate maximization optimization problem P1 is expressed as

[0013] in, Indicates AP and The precoding sequence between users, Indicates the maximum transmit power of the AP. represents the channel matrix between all APs and active RIS, represents the channel matrix between the first AP and the active RIS, Indicates The channel matrix between APs and active RIS, Indicates the maximum amplified power of the active RIS, Indicates the active RIS The magnitude of the amplification factor of each element, Indicates the maximum amplitude of the active RIS amplification factor, Represents a complex set.

[0014] Furthermore, the alternating optimization algorithm proposed in step (4) includes the following steps:

[0015] Step (4.1) introduces auxiliary variables and , , using the Lagrange dual transformation and quadratic transformation theorem, the objective function of P1 is rewritten as:

[0016] in, It represents the real part operation;

[0017] Step (4.2) is given Under the conditions of ; Omit the Irrelevant terms, P1 is equivalent to in, represents the Kronecker product, , , The dimension is The identity matrix of , , , , , express The identity matrix List, The dimension is The identity matrix of represents a complex set;

[0018] On this basis, the Lagrangian augmented function of problem P2 Expressed as ,in, and represents auxiliary variables, , , ; For a given The feasible solution for the iteration is , using the original dual subgradient method, we get The feasible solution is , , , is the step length, Indicates positive value operation;

[0019] Step (4.3) is given Under the conditions of ; Omit the Irrelevant terms, P1 is equivalent to

[0020] ,in , , , Represents vectorized operations; , The dimension is The identity matrix of , express No. Similarly, the Lagrangian augmented function of problem P3 is expressed as ,in, and represents auxiliary variables, , , , Similarly, for a given The feasible solution for the iteration is , using the original dual subgradient method, we get The feasible solution is , , , ; Step (4.4) repeats steps (4.1)-(4.3), and terminates the loop when the objective function converges to obtain the optimal solution .

[0021] The beneficial effects achieved by the present invention are:

[0022] 1. By jointly optimizing the AP beam and the active RIS reflected beam, the coherent superposition of signals is achieved, the power of the desired signal is enhanced, and the serious inter-user interference and RIS noise interference caused by the active RIS amplified signal are reduced, effectively improving the downlink speed;

[0023] 2. The reflection unit of active RIS is equipped with an active load, so that the amplitude of its reflection coefficient can be greater than 1. Therefore, active RIS has the ability to amplify the signal, but it also amplifies the noise generated by the active load circuit so that it cannot be ignored, which leads to a different downstream rate expression from that when using passive RIS.

[0024] 3. The alternating optimization algorithm proposed by the method of the present invention avoids matrix inversion operations and has lower computational complexity than the commonly used traditional alternating direction multiplication method. Therefore, when facing large-scale systems, the method of the present invention has higher scalability. In addition, in terms of convergence, simulation shows that the method of the present invention can converge after only about 4 iterations, which has high feasibility.

[0025] 4. The present invention is based on a de-cellularized large-scale MIMO system. The difference from the traditional cellular MIMO system is that the latter no longer divides cells and removes the central base station. A large number of dispersed access points use the same time-frequency resources to serve all users in the area at the same time. Therefore, the scenario is more complex than the traditional cellular network and the optimization capability is stronger. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a graph showing the relationship between the downlink sum rate and the number of active RIS reflector units;

[0027] Figure 2 It is the relationship diagram between average rate and number of users;

[0028] Figure 3 It is the number of iterations of the beam joint optimization method;

[0029] Figure 4 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0030] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the accompanying drawings, and the words "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0031] like Figure 4 As shown, this embodiment provides a beam joint optimization method based on RIS-assisted decellularized mMIMO system, which aims to solve the downlink and rate maximization optimization problem, jointly design AP beamforming and active RIS reflection beam, maximize the downlink and rate, and reduce the serious inter-user interference and noise interference caused by active RIS. The method includes the following steps:

[0032] Step (1) establishes a Rayleigh fading channel model of the decellularized mMIMO system assisted by active RIS; wherein the active RIS assisted decellularized mMIMO system includes APs and single-antenna users, each AP contains In addition, we used an active RIS for auxiliary communication, which contains A reflective unit.

[0033] In step (1), the Rayleigh fading channel model ,in, Indicates AP and The synthetic channel sequence between users is Indicates AP and The sequence of direct channels between users, Indicates the number of antennas each AP has. Indicates The channel sequence between each user and the active RIS, An acronym for Active RIS. Indicates the number of reflection units in the active RIS. Indicates The channel sequence between the first antenna of each AP and the active RIS; Indicates The first Channel sequence between the root antenna and the active RIS; represents the reflection coefficient matrix of active RIS, represents the amplification of the first unit of the active RIS, Indicates the active RIS The amplification of the unit, Indicates the phase of the first unit of the active RIS, Indicates the active RIS The phase of each unit, represents the vector diagonalization operation, Indicates that the base is a natural number The exponential operation of represents the conjugate transpose operation, Represents a complex set.

[0034] Step (2) based on the Rayleigh fading channel model, an expression of the received data of the user in the downlink data transmission phase is given, thereby deriving an expression of the downlink user rate;

[0035] In the downlink data transmission phase, the step (2) Data received by users The expression is ,in, Indicates that all APs and The synthetic channel sequence between users is Indicates that all APs and The precoding sequence between users, Indicates The data symbol of each user, and ; Indicates The data symbol of each user, Indicates the number of users, Indicates that all APs and The precoding sequence between users, represents the noise sequence of active RIS, whose elements are independent and identically distributed , represents the noise power of the active RIS, Indicates The noise of each user follows the distribution , represents the noise power of the user, represents the expectation operation, represents the conjugate operation, represents a cyclically symmetric complex Gaussian distribution, Represents a complex set.

[0036] Step (3) Taking the user's sum rate as the objective function, the downlink AP beam sequence and the active RIS reflection coefficient as the optimization variables, a sum rate maximization optimization problem with AP and active RIS power constraints is given;

[0037] According to the user data expression obtained in step (2), the first The downlink rate of a user is , whose expression is ,in, represents the logarithm operation with base 2, Indicates The signal-to-interference-noise ratio of a user is expressed as ,in, represents the Euclidean norm; according to the rate expression of each user in step (2), the sum rate expression of the users in step (3) is , considering the power constraints at the AP and active RIS, the downlink and rate maximization optimization problem P1 is expressed as in, Indicates AP and The precoding sequence between users, Indicates the maximum transmit power of the AP. represents the channel matrix between all APs and active RIS, represents the channel matrix between the first AP and the active RIS, Indicates The channel matrix between APs and active RIS, Indicates the maximum amplified power of the active RIS, Indicates the active RIS The magnitude of the amplification factor of each element, Indicates the maximum amplitude of the active RIS amplification factor, Represents a complex set.

[0038] Step (4) proposes an alternating optimization algorithm, using the primal dual subgradient method to solve the downlink AP beamforming and active RIS reflection beam respectively.

[0039] The following steps are involved:

[0040] Step (4.1) introduces auxiliary variables and , , using the Lagrange dual transformation and quadratic transformation theorem, the objective function of P1 is rewritten as:

[0041] in, It represents the real part operation;

[0042] Step (4.2) is given Under the conditions of ; Omit the Irrelevant terms, P1 is equivalent to in, represents the Kronecker product, , , The dimension is The identity matrix of , , , , , express The identity matrix List, The dimension is The identity matrix of represents a complex set;

[0043] On this basis, the Lagrangian augmented function of problem P2 Expressed as ,in, and Represents auxiliary variables , , ; For a given The feasible solution for the iteration is , using the original dual subgradient method, we can get The feasible solution is , , , is the step length, Indicates positive value operation;

[0044] Step (4.3) is given Under the conditions of ; Omit the P1 is equivalent to ,in, , , , represents vectorized operations, , The dimension is The identity matrix of , express No. Similarly, the Lagrangian augmented function of problem P3 is expressed as ,in, and represents auxiliary variables, , , , Similarly, for a given The feasible solution for the iteration is Using the original dual subgradient method, we get The feasible solution is , , , ;

[0045] Step (4.4) Repeat steps (4.1)-(4.3), and terminate the loop when the objective function converges to obtain the optimal solution. .

[0046] The active RIS-assisted decellularized mMIMO system comprises APs and single-antenna users, each AP contains In addition, we used an active RIS for auxiliary communication, which contains A reflective unit.

[0047] This embodiment substitutes the obtained optimal solution into the rate formula to calculate the downlink rate of each user. Figure 1 For the active RIS-assisted decellularized mMIMO system, the downlink sum rate increases with the increase of the number of reflection units and the maximum amplitude of the amplification factor. This is because whether increasing the reflection unit or increasing the amplification factor can enhance the ability of active RIS to resist multiplicative fading, thereby improving the rate. Compared with the case without optimization, the joint optimization method proposed in the present invention realizes the coherent superposition of signals, significantly improves the downlink sum rate, and the improvement effect also increases with the increase of the number of reflection units and the maximum amplitude. For example, when the number of reflection units is 64, the present invention improves by 14.5% when the maximum amplitude is 5 compared with no optimization, and improves by 27.5% when the maximum amplitude is 10.

[0048] See also Figure 2 It can be found that the average downlink rate decreases with the increase of the number of users. This is because the interference between users gradually increases. However, the joint optimization method proposed in the present invention effectively reduces the interference between users and noise interference by realizing signal coherent superposition, so it always has the best performance.

[0049] See also Figure 3 The joint optimization method proposed in the present invention can converge quickly in both small-scale and large-scale systems, with about 4 iterations, indicating that the optimization method proposed in the present invention has high feasibility.

Claims

1. A beam joint optimization method based on RIS-assisted decellularized mMIMO system, characterized in that: The following steps are involved: Step (1) establishes the Rayleigh fading channel model of the decellularized mMIMO system assisted by active RIS; the Rayleigh fading channel model in step (1) ,in, Indicates AP and The synthetic channel sequence between users is Indicates AP and The sequence of direct channels between users, Indicates the number of antennas each AP has. Indicates The channel sequence between each user and the active RIS, An acronym for Active RIS. Indicates the number of reflection units in the active RIS. Indicates The channel matrix between APs and active RIS, Indicates The channel sequence between the first antenna of each AP and the active RIS; Indicates The first AP Channel sequence between the root antenna and the active RIS; represents the reflection coefficient matrix of active RIS, represents the amplification of the first unit of the active RIS, Indicates the active RIS The amplification of the unit, Indicates the phase of the first unit of the active RIS, Indicates the active RIS The phase of each unit, represents the vector diagonalization operation, Indicates that the base is a natural number The exponential operation of represents the conjugate transpose operation, represents a complex set; Step (2) provides an expression of the received data of the user in the downlink data transmission stage based on the Rayleigh fading channel model, thereby deriving an expression of the downlink user rate; in the downlink data transmission stage, the step (2) Data received by users The expression is ,in Indicates that all APs and The synthetic channel sequence between users is Indicates that all APs and The precoding sequence between users, Indicates The data symbol of each user, and ; Indicates The data symbol of each user, Indicates the number of users, Indicates that all APs and The precoding sequence between users, represents the noise sequence of active RIS, whose elements are independent and identically distributed , represents the noise power of the active RIS, Indicates The noise of each user follows the distribution , represents the noise power of the user, represents the expectation operation, represents the conjugate operation, represents a cyclically symmetric complex Gaussian distribution, represents a complex set; Step (3) takes the user's sum rate as the objective function, the downlink AP beam sequence and the active RIS reflection coefficient as the optimization variables, and gives the downlink sum rate maximization optimization problem with AP and active RIS power constraints; according to the user data expression obtained in step (2), the first The downlink rate of a user is , whose expression is ,in, represents the logarithm operation with base 2, Indicates The signal-to-interference-noise ratio of a user is expressed as ,in, represents the Euclidean norm; according to the rate expression of each user in step (2), the sum rate expression of the users in step (3) is , considering the power constraints at the AP and active RIS, the downlink and rate maximization optimization problem P1 is expressed as in, Indicates AP and The precoding sequence between users, Indicates the maximum transmit power of the AP. represents the channel matrix between all APs and active RIS, represents the channel matrix between the first AP and the active RIS, Indicates The channel matrix between APs and active RIS, Indicates the maximum amplified power of the active RIS, Indicates the active RIS The magnitude of the amplification factor of each element, Indicates the maximum amplitude of the active RIS amplification factor, represents a complex set; Step (4) proposes an alternating optimization algorithm, which uses Lagrange dual transformation and primal dual subgradient method to solve downlink AP beamforming and active RIS reflection beam respectively; the alternating optimization algorithm proposed in step (4) includes the following steps: Step (4.1) introduces auxiliary variables and ;in , using the Lagrange dual transformation and quadratic transformation theorem, the objective function of P1 is rewritten as: in, It represents the real part operation; Step (4.2) is given Under the conditions of ; Omit the Irrelevant terms, P1 is equivalent to in, represents the Kronecker product, , , The dimension is The identity matrix of , , , , , express The identity matrix List, The dimension is The identity matrix of represents a complex set; On this basis, the Lagrangian augmented function of problem P2 Expressed as ,in, and represents auxiliary variables, , , ; For a given The feasible solution for the iteration is , using the original dual subgradient method, we get The feasible solution is , , , is the step length, Indicates positive value operation; Step (4.3) is given Under the conditions of ; Omit the Irrelevant terms, P1 is equivalent to ,in , , , represents vectorized operations, , The dimension is The identity matrix of , express No. Similarly, the Lagrangian augmented function of problem P3 is expressed as ,in, and represents auxiliary variables, , , , Similarly, for a given The feasible solution for the iteration is Using the primal dual subgradient method, we get The feasible solution is , , , ; Step (4.4) Repeat steps (4.1)-(4.3), and terminate the loop when the objective function converges to obtain the optimal solution. .

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