Multi-hop RIS signal coverage method without CSI dependency

Abstract

The invention discloses a multi-hop RIS (Radio Information System) signal coverage method without CSI (Channel State Information) dependency, which belongs to the field of communication, and is characterized in that modeling is carried out on an incident signal of each RIS based on phase compensation, the modeling is more accurate compared with a traditional point source model, a codebook solution method capable of uniformly covering the next RIS is designed in the transmission stages of BS-RIS-RIS and RIS-RIS-RIS, and the coverage efficiency of the next RIS is improved. According to the method, the energy transfer efficiency of the next RIS can be improved, an optimization problem can be established to obtain an RIS codebook for beamforming only by considering a signal transmitting position and a signal receiving position needing to be enhanced during signal modeling, a complex channel estimation process is not needed, computing resources are saved, and on the premise that channel state information is not needed, the energy transfer efficiency of the next RIS can be improved. A phase control matrix of each RIS block in the multi-hop RIS is obtained, and signal enhancement in a traditional communication signal range scene such as a mine hole is achieved by controlling each RIS block.

Description

Technical Field

[0001] The present invention belongs to the field of communications, and more specifically, relates to a multi-hop RIS signal coverage method without CSI dependency. Background Art

[0002] Reconfigurable Intelligent Surface (RIS) is one of the new 6G candidate technologies. It is an artificial electromagnetic surface structure with programmable electromagnetic properties, usually composed of a large number of carefully designed electromagnetic units. By controlling the state of each electromagnetic unit on the surface, an electromagnetic field with controllable parameters such as amplitude, phase, polarization and frequency can be formed, thereby actively regulating electromagnetic waves in space. It has revolutionized the dilemma of the unchangeable wireless environment in traditional communications.

[0003] Taking the signal transmission source as the base station and the signal receiving source as the user end as an example, the basic scenario of multi-hop RIS-assisted communication is as follows: Figure 1 In real life, there are some communication scenarios such as corridors and tunnels that are narrow or have corners. In these scenarios, it is difficult for the base station signal to be transmitted to the user end. In order to solve this problem, RIS is artificially introduced to achieve signal relay with the help of multiple RIS, and finally achieve signal coverage to the user end, so as to enhance the signal power on the user side and improve the user communication quality.

[0004] In the process of realizing multi-hop RIS auxiliary signal coverage, the difficulties that need to be solved are as follows:

[0005] 1. How to obtain channel information (CSI) and establish a channel model. After the introduction of RIS, the number of communication links increased exponentially, and channel estimation became too difficult. Moreover, since RIS is mainly a passive reflection and does not have perception capabilities, it is even more difficult to obtain CSI between the base station and RIS and between RIS and the user. In addition, because the acquisition of CSI requires signal feedback and time delay, in actual communication systems, especially fast response communications, the location of user terminals often changes rapidly, which further increases the difficulty of obtaining CSI.

[0006] 2. How to build an accurate channel model. The existing channel models are basically for single-hop RIS, but in the RIS-RIS-USER (RIS) section of multi-hop RIS, because the signal source is RIS, and the signal emitted by RIS is not consistent in all directions, the traditional point source model is no longer applicable and needs to be remodeled.

[0007] 3. What is the goal of beamforming in the BS(RIS)-RIS-RIS segment? The beamforming algorithm for single-hop RIS is very mature nowadays, but this algorithm is beamforming for one point or one direction. In multi-hop, this will cause the energy received by the next-hop RIS to be too concentrated, resulting in poor beamforming effect for the next-hop RIS. Therefore, how to design the beamforming in the BS(RIS)-RIS-RIS segment is very important. Summary of the invention

[0008] In view of the above defects or improvement needs of the prior art, the present invention provides a multi-hop RIS signal coverage method without CSI dependence, which can be used for beamforming in a multi-hop RIS assisted communication scenario to improve the communication quality of users in the communication system and achieve stable and efficient communication.

[0009] To achieve the above object, according to a first aspect of the present invention, a multi-hop RIS signal coverage method without CSI dependency is provided, which is applied to a multi-hop RIS assisted communication scenario. In this scenario, the signal y transmitted by the signal transmission source in RIS1, RIS2, ..., RIS K Arriving at a signal receiving source, the method comprises:

[0010] S1, using the phase compensation model to calculate the signal transmission source to RIS1, RIS1 to RIS2, ..., RIS K The path loss and phase shift to the signal receiving source, and according to the additional phase of each RIS, the receiving signal of each RIS and the user end is obtained;

[0011] S2, RIS2, RIS3, RIS4, ..., RIS k+1 The unit with the smallest received signal energy is the target unit. By maximizing the received signal energy of the target unit, RIS1, RIS2, RIS3, ..., RIS k The codebooks Φ1, Φ2, Φ3, ..., Φ k The optimal solution of

[0012] S3, RIS is obtained by maximizing the energy of the received signal at the user end K The codebook Φ K The optimal solution of

[0013] S4, transmitting the optimal codebook solution of each RIS to each RIS to achieve beamforming;

[0014] Among them, RIS k is the kth RIS, k=1,2…,K-1, K is the number of RIS.

[0015] According to a second aspect of the present invention, there is provided an electronic device, comprising: a computer-readable storage medium and a processor;

[0016] The computer-readable storage medium is used to store executable instructions;

[0017] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method as described in the first aspect.

[0018] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute the method as described in the first aspect.

[0019] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0020] The multi-hop RIS signal coverage method without CSI dependence provided by the present invention models the incident signal of each RIS based on phase compensation, which is more accurate than the traditional point source model. In the transmission stages of BS-RIS-RIS and RIS-RIS-RIS, a codebook solution method that can evenly cover the next RIS is designed, thereby improving the energy transfer efficiency of the next RIS. When modeling the signal, only the signal transmission position and the signal receiving position that needs to be enhanced need to be considered, and an optimization problem can be established to obtain the RIS codebook for beamforming. A complex channel estimation process is not required, and computing resources are saved. The phase control matrix of each RIS in the multi-hop RIS can be obtained without the need for channel state information (CSI). By controlling each RIS, signal enhancement in traditional communication signal extremely poor scenes such as mines is achieved. In addition, compared with the point-to-point beamforming algorithm that repeatedly uses a single-hop RIS in the multi-hop RIS, the present invention uses a multi-beam algorithm in the BS (RIS)-RIS-RIS section to evenly cover the next RIS, thereby making the energy transmission efficiency of the entire multi-hop RIS higher. In summary, the method provided by the present invention can be used for beamforming in a multi-hop RIS-assisted communication scenario to improve the communication quality of users in the communication system and achieve stable and efficient communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a basic scenario of multi-hop RIS-assisted communication;

[0022] Figure 2 A flow chart of a multi-hop RIS signal coverage method without CSI dependency provided by an embodiment of the present invention;

[0023] Figure 3A schematic diagram of calling the CVX toolbox in MATLAB provided by an embodiment of the present invention;

[0024] Figure 4 An experimental scene diagram provided for an embodiment of the present invention;

[0025] Figure 5 It is the signal power diagram without RIS;

[0026] Figure 6 It is the signal power diagram using the single-hop algorithm;

[0027] Figure 7 This is a signal power diagram using the method provided by the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0029] The embodiment of the present invention provides a multi-hop RIS signal coverage method without CSI dependence, which is applied to a multi-hop RIS assisted communication scenario. In this scenario, the signal y emitted by the signal emission source in RIS1, RIS2, ..., RIS K Arriving at a signal receiving source, the method comprises:

[0030] S1, followed by RIS2, RIS3, RIS4, ..., RIS k+1 The unit with the smallest received signal energy is the target unit. By maximizing the received signal energy of the target unit, RIS1, RIS3, RIS2, RIS3, ..., RIS4 are obtained respectively. k The codebooks Φ1, Φ2, Φ3, ..., Φ K The optimal solution of .

[0031] S2, RIS is obtained by maximizing the energy of the received signal at the user end K The codebook Φ K The optimal solution of

[0032] S3, transmitting the optimal codebook solution of each RIS to each RIS to achieve beamforming;

[0033] Among them, RIS k is the kth RIS, k=1,2…,K-1, K is the number of RIS.

[0034] The method provided in the embodiment of the present invention is further described below by taking the signal transmitting source as a base station and the signal receiving source as a user terminal as an example.

[0035] First, build a signal model.

[0036] Assuming that the number of units in each RIS is N, and there are K RIS in total, based on the phase compensation model, the incident signal of the n1th unit of the first RIS (i.e., RIS1, the RIS closest to the BS) can be obtained as follows:

[0037]

[0038] Among them, y in Indicates the transmission signal. Represents RIS k The nth k The incident signal at the unit, It represents the distance between BS and the n1th unit of the first RIS, and λ is the wavelength of the signal.

[0039] It can be understood that the phase compensation model between the base station and the first RIS is: in, and are the path loss and phase shift between the base station and the first RIS respectively. For two signal transmission points A and B, the transmission signal y of A is known. A As well as the path loss and phase shift between A and B, we know that the received signal at B is: A , the product of the path loss between A and B and the phase shift. In addition, if the transmission point A is RIS, then according to the characteristics of RIS, the received signal at B should be y A , the product of the path loss and phase shift between A and B, and the additional phase of RIS. Therefore, combined with the codebook of the first RIS (ie, RIS1), The received signal of the n2th unit of RIS2 can be obtained as:

[0040]

[0041] in, is the distance between the n1th unit of RIS1 and the n2th unit of RIS2, is the additional phase of the n1th unit of RIS1, is the codebook of the n1th unit of RIS1, Represents the additional phase of the n1th unit of RIS1.

[0042] Similarly, RIS k+1 The nth k+1 The received signal of each unit is:

[0043]

[0044] in, Represents RIS k The nth k Units and RIS k+1 The nth k+1 The distance between units. Represents RIS k The nth k Additional phases for each unit.

[0045] Similarly, by induction, we can get the kth RIS (i.e. RIS k ) incident signal, also known as RIS k The received signal According to the last RIS (i.e. Kth RIS, RIS K ) of the incident signal Y K and codebook Φ K Then the receiving signal of USER is:

[0046]

[0047] in Indicates the nth RIS from the receiving end to the Kth RIS K The distance between units.

[0048] Second, formulate the optimization problem.

[0049] (1) For BS-RIS-RIS:

[0050] In order to achieve uniform coverage of the next-hop RIS, we set up an optimization problem based on the signal energy of each unit of the next RIS. Combined with the signal model, we know Y1, and we can get the incident signal of the second RIS in is a variable that is only related to Φ1. Therefore, the energy of the unit with the smallest receiving energy in the second RIS is maximized to obtain the optimal solution of Φ1. The form of the optimization problem is as follows:

[0051]

[0052] (2) For RIS-RIS-RIS:

[0053] Assuming that the codebooks of the first k-1 RIS blocks are known, the incident signal Y of the kth RIS block can be obtained: k ,but is a single kTherefore, by maximizing the energy of the unit with the smallest receiving energy in the k+1th RIS, the codebook Φ of the kth RIS can be obtained. k The optimal solution of the optimization problem is:

[0054]

[0055] (3) For RIS-RIS-USER:

[0056] After obtaining the codebooks of the previous K-1 RIS blocks in sequence, combined with the signal model, the received signal at the user end can be obtained as:

[0057]

[0058] By maximizing the received energy at the user end, RIS can be obtained. K The optimal solution of the codebook is:

[0059]

[0060] Among them, a≥2.

[0061] Then, the optimization problem is solved to obtain the codebook.

[0062] ① For BS-RIS-RIS and RIS-RIS-RIS, this problem is a max-min problem. For this type of problem, the interior point method, SQP, fminimax (MATLAB built-in function) and other methods can be used to solve it. The embodiment of the present invention does not limit the uniqueness of this. As an example, the embodiment of the present invention uses the MA algorithm to solve as follows:

[0063] For the form max Φ min n {y n} optimization problem, first transform it:

[0064]

[0065] Let f(y) = max n {-y n}, combined with Morean-Yosida Approximation:

[0066]

[0067] So the original problem can be written as:

[0068]

[0069] Because M λ(f) It is globally differentiable, so the steepest gradient descent method can be used to solve it.

[0070] ②For RIS-RIS-USER, this type of optimization problem can be rewritten as:

[0071]

[0072] in

[0073]

[0074] R=QQ H

[0075]

[0076] The above problem can be solved by manifold optimization, gradient descent, Lagrangian dual method and other methods, and the embodiment of the present invention does not limit the uniqueness of this. As an example, the embodiment of the present invention constructs a relaxed constraint and uses a semidefinite programming method to solve the optimization problem. The specific steps are as follows:

[0077] First, by defining the equation V = ww H , it should be noted that w H Rw=tr(Rww H ), relax the constraints to V ≥ 0 and rank(V) = 1. The optimization problem can be rewritten as:

[0078]

[0079] Then use the CVX toolbox in MATLAB to solve such problems. The specific calling method is as follows Figure 3 As shown, after solving V, perform eigenvalue decomposition on V = U∑U H , and then perform Gaussian randomization: ① Randomly generate 100 complex Gaussian vectors r with mean 0 and variance 1, and calculate 100 times Get 100 ②100 Substitute into the expression Compare the largest value among the 100 results As the vector to be restored. ③ Phase angle recovery:

[0080] The phase angle of the obtained w is the required codebook, and the specific calculation method is: Phase = arg (w); arg represents the phase angle.

[0081] Finally, the codebook is assigned to RIS to achieve signal enhancement

[0082] After the codebook is obtained through the above three steps, the codebook is transmitted to the RIS through computers, embedded development platforms and other tools based on serial communication or wireless communication, and the diode switch state of each unit of the RIS is controlled to achieve independent phase control of the incident electromagnetic wave by each electromagnetic unit, so as to achieve optimal beam pointing in the target direction, and then achieve signal coverage and enhancement of the blind area through relay transmission between multiple RIS.

[0083] In order to verify the effectiveness of the method provided by the present invention, a two-hop RIS scenario is designed to compare the signal power when there is no RIS, when both RISs use a single-hop RIS algorithm, and when the method provided by the present invention is used. Figure 4 The signal power without RIS is as follows. Figure 5 As shown in Figure 2, the signal power of the single-hop algorithm is as follows: Figure 6 As shown, the signal power of the algorithm of the present invention is as follows Figure 7 As shown in the results, the method provided by the present invention has a gain of about 17dBm compared to the method without RIS, and a gain of about 10dBm compared to the single-hop algorithm.

[0084] An embodiment of the present invention provides an electronic device, including: a computer-readable storage medium and a processor;

[0085] The computer-readable storage medium is used to store executable instructions;

[0086] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method described in any of the above embodiments.

[0087] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method described in any of the above embodiments.

[0088] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-hop RIS signal coverage method without CSI dependence is applied to a multi-hop RIS assisted communication scenario. In this scenario, the signal y emitted by the signal transmitting source in RIS1, RIS2, ..., RIS K Arrival signal receiving source, characterized in that The method comprises: S1, using the phase compensation model to calculate the signal transmission source to RIS1, RIS1 to RIS2, ..., RIS K The path loss and phase shift to the signal receiving source, and according to the additional phase of each RIS, the receiving signal of each RIS and the user end is obtained; S2, RIS2, RIS3, RIS4, ..., RIS k+1 The unit with the smallest received signal energy is the target unit. By maximizing the received signal energy of the target unit, RIS1, RIS2, RIS3, ..., RIS k The codebooks Φ1, Φ2, Φ3, ..., Φ k The optimal solution of S3, RIS is obtained by maximizing the energy of the received signal at the user end K The codebook Φ k The optimal solution of S4, transmitting the optimal codebook solution of each RIS to each RIS to achieve beamforming; Among them, RIS k is the kth RIS, k=1,2…,K-1, K is the number of RIS.

2. The method according to claim 1, characterized in that RIS k+1 The received signal is: in, For RIS k+1 The nth k+1 The received signal of each unit, For RIS k The nth k Units and RIS k+1 The nth k+1 The distance between the units, For RIS k The nth k Additional phase of each unit, For RIS k The nth k The codebook of units, n1、n k 、n k+1 are all equal to 1, 2, ..., N, where N is the total number of RIS units. For RIS k The incident signal of the n1th unit, d n1 is the distance from the signal transmitting source to the n1th unit of RIS1, and λ is the signal wavelength.

3. The method according to claim 2, characterized in that The received signal at the user end is: in, For client to RIS K The nth K The distance between units, n K Equal to 1, 2,…, N.

4. The method according to claim 1, characterized in that In step S1, the energy of the received signal of the target unit is maximized by establishing a Max-min problem; The Max-min problem is:

5. The method according to claim 4, characterized in that The MA algorithm is used to solve the Max-min problem, including: (1) Order The Max-min problem is transformed into: (2) Yes Performing the Moreau-Yosida approximation, we get Thus Further transformed into min Φ M λ (f); where λ is the regularization parameter, is the unit simplex, P Δ is the function projected onto Δ; (3) Solve using the fastest gradient descent method Get codebook φ k The optimal solution of .

6. The method according to claim 1, characterized in that In step S2, by establishing the optimization problem Maximize the energy of the received signal at the user end; where a≥2.

7. The method according to claim 6, characterized in that The optimization problem is solved by using a semidefinite programming method, including: (1) Convert the optimization problem into max w w H Rw; among them, (2) Let V = ww H , establish the relaxed constraints V ≥ 0 and rank (V) = 1, and transform the objective function into: (3) The CVX toolbox in Matlab is used to solve the converted objective function to obtain the value of Φ, and V is subjected to eigenvalue decomposition and Gaussian randomization to obtain Φ K The optimal solution of .

8. An electronic device, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to any one of claims 1 to 7.

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