A reflection coefficient design method in a reconfigurable reflector-assisted wireless communication system

Abstract

The present invention discloses a method for designing reflection coefficients in a reconfigurable reflector-assisted wireless communication system. In a downlink of a communication system assisted by a reconfigurable reflector, for each user k (k=1, ...K), a reflection coefficient Φ with an optimal signal-to-noise ratio (SNR) is designed. opt‑k ; Reflection coefficient of optimal reflection surface #imgabs0# where β k ∈(0, 1) represents the weight of the kth user, and the reflection coefficient that maximizes the system sum rate is selected. The optimized reflection coefficient of the reflection surface can be obtained by inputting the channel matrix between the reconfigurable reflection surface and the user and the base station, the transmission power of the base station, and the weight between users. Compared with the random reflection coefficient design method, this method has significantly improved performance.

Description

Technical Field

[0001] The invention relates to a reflection coefficient design method in a reconfigurable intelligent surface (RIS) assisted wireless communication system, which can improve the system and rate and belongs to the technical field of wireless communication. Background Art

[0002] Since entering the 5G (The 5th Generation of Mobile Communication Systems, 5G) era, technology has developed more rapidly. People have more and more demands for technologies such as autonomous driving, telemedicine, and virtual reality. By 2020, in the 5G and Internet of Things era, network capacity will increase by about 1,000 times, serving at least 50 billion wireless communication devices. From the perspective of the communication field, this actually puts forward a higher demand for data transmission rate. In order to meet this demand, people have proposed solutions such as massive multiple input multiple output (Massive MIMO), millimeter wave (Millimeter wave, mmWave) communication, and ultra-dense network (Ultra-Dense Network, UDN) to improve communication reliability. However, hardware cost and energy consumption are still key issues in actual systems. For example, the use of massive MIMO technology, the deployment of base stations with a large number of antennas, so a large number of radio frequency chains are required, which will undoubtedly increase the cost. For example, the use of millimeter wave technology, because the millimeter wave has a large bandwidth, narrow beam, and strong directivity, the signal is easily blocked. Secondly, the base station has high power consumption and a small coverage range, and a large number of base stations need to be deployed, which makes the energy consumption and cost high.

[0003] Reconfigurable Intelligent Surface (RIS) has attracted extensive attention from researchers in recent years as a technology that can effectively reduce energy consumption and has relatively low cost. Reconfigurable intelligent surface is composed of a large number of low-cost passive devices. Since reconfigurable intelligent surface is a passive device without dedicated power consumption, it is envisioned as a green and cost-effective solution to improve the spectrum efficiency and energy efficiency of future cellular networks. Specifically, RIS is a planar array composed of a large number of passive devices such as low-cost printed dipoles, which is controlled by a controller connected to the base station. Each reflective unit in the reconfigurable reflective surface can independently adjust the phase of the incident signal to coordinately change the propagation of the reflected signal. Unlike traditional receivers and transmitters, RIS helps to transmit information without generating new signals and has the ability to control the radio propagation environment. The base station can appropriately adjust the reflection coefficient of RIS through the controller, and can add an additional reflection link at the signal receiving end to enhance the power of the received signal. At the same time, it can also reversely design the phase at the non-signal receiving end to reduce co-channel interference. Not only that, reconfigurable reflective surfaces also retain all the advantages of massive MIMO systems, such as the ability to concentrate a large amount of energy in three-dimensional space, paving the way for wireless charging, remote sensing and data transmission. Therefore, reconfigurable reflective surfaces have been widely studied. Summary of the invention

[0004] Purpose of the invention: Most of the research on reconfigurable reflecting surfaces is to achieve the goal of maximizing the system and rate by solving a series of complex optimization problems. However, solving the optimization problem is bound to increase the complexity and calculation time. Therefore, in view of these shortcomings, the present invention proposes a reflection coefficient design method for a reconfigurable reflecting surface assisted wireless communication system. The method is simple and feasible, and can effectively reduce the calculation time. Compared with the random reflection coefficient of the reconfigurable reflecting surface, it has a more obvious improvement.

[0005] Technical solution: A method for designing reflection coefficient in a reconfigurable reflector-assisted wireless communication system, comprising the following steps:

[0006] Step 1: In the downlink of the communication system assisted by the reconfigurable reflector, the base station is configured with M antennas and N reflector units, and provides services for K single-antenna users at the same time. The base station adjusts the reflection coefficient of the reconfigurable reflector through the controller to improve the communication quality of the system and increase the transmission rate. IUk is the channel vector from the reflection surface to the kth user, H BI is the channel matrix from the base station to the reflection surface, h BUk is the direct path channel vector from the base station to the kth user, the base station transmit power P, and the noise power of the kth user is Where k = 1, 2, ..., K, let the integer T represent the calculation accuracy; set the initial value of the precoding matrix and the initial value of the reflection coefficient of the reflection surface parameters W and Φ respectively, where P, All are positive numbers;

[0007] Step 2: For each user k (k = 1, ... K), design the reflection coefficient Φ with the best signal-to-noise ratio (SNR) opt-k ;

[0008] Step 3: Optimal reflectance of the reflective surface where β k ∈(0,1) represents the weight of the kth user, and β=[β1,β2,…,β K ].

[0009] Further, in step 2, the reflection coefficient of the reflection surface with the optimal SNR for the kth user is Φ opt-k =diag(v opt -k ), where diag(·) represents the vector v opt-k is a diagonal matrix with diagonal elements, and

[0010]

[0011] Where j is an imaginary unit, {·} + represents the pseudo-inverse, and angle(·) represents the phase of the complex number.

[0012] Furthermore, in step 3, β that can maximize the system and rate is selected. The specific steps are:

[0013] Step 301: Let γ = 1 / T, and β k The value range is (0,γ,2γ,…,1), and the weight factor β is introduced i ∈(0,1), calculate to satisfy The feasible solution set B of

[0014] Step 302: For all β in set B, calculate the system sum rate;

[0015] Step 303: Output β that maximizes the system sum rate.

[0016] Further, in step 301, the method for generating the feasible solution set B of β is as follows:

[0017] Step 30101: Let q = 0, L (q) ←K, which means assigning the value of the number of users K to L (q) , L (q) represents the value of variable L in the qth iteration, b (q) Represented by variable b1 (q) , …b L (q) The feasible solution composed of (q) =1, c (q) Represents the value at the qth iteration, satisfying The set of all feasible solutions of is B;

[0018] Step 30102: If L (q) =1, then a feasible solution is returned. And end the current loop; if the variable L (q) =2, then a feasible solution is returned. And end the current loop; otherwise, set t←0;

[0019] Step 30103: Order calculate All feasible solutions of , the specific method is: let q←q+1, L (q )=L (q-1) -1, Represents the variable To variable The feasible solution composed of Jump to step 30102;

[0020] Step 30104: If t=T, terminate the current loop, otherwise set t←t+1 and return to step 30103.

[0021] Further, in step 302, the sum rate of the system is calculated according to the following formula:

[0022]

[0023] Among them, SINR k represents the signal-to-interference-noise ratio of the kth user, and the calculation formula is:

[0024]

[0025] Among them, Φ opt is the reflection coefficient of the optimized reconfigurable reflective surface, let h k =h IUk Φ opt H BI +h BUk represents the equivalent channel of the kth user, w k represents the beamforming vector of the kth user, w j represents the beamforming vector of the jth user, in(·) H stands for conjugate transpose.

[0026] Beneficial effects: Compared with the prior art, the reflection coefficient design method in a reconfigurable reflector-assisted wireless communication system provided by the present invention has the following advantages:

[0027] (1) Wide range of applications. The solution proposed in the present invention can be flexibly applied to a system of reconfigurable reflector-assisted transmission, and can be applied to scenarios with a small number of users as well as scenarios with a large number of users.

[0028] (2) The sum rate of the system is significantly improved. The scheme proposed by the present invention takes a short time, has accurate calculation results, and is simple and easy to implement. Compared with the reconfigurable reflection surface random reflection coefficient scheme, it can achieve good performance improvement with lower implementation complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a reflection coefficient design method in a reconfigurable reflector-assisted wireless communication system;

[0030] Figure 2 The simulation scene diagram of the reflection coefficient design method in a reconfigurable reflector-assisted wireless communication system in the present invention is shown in FIG. 1 , wherein the simulation parameters are: the number of users K = 2, the channel matrix H from the base station to the reflector BI , the channel vector h from the reflection surface to the kth user IUK is subject to Rayleigh fading, and the direct channel h from the base station to user k is BUK Is subject to Les decline.

[0031] Figure 3 This is a performance simulation diagram of a reflection coefficient design method in a reconfigurable reflector-assisted wireless communication system in the present invention. As the number of base station antennas continues to increase, the performance of the optimized solution and the random reflection coefficient solution are compared.

[0032] Figure 4 This is a performance simulation diagram of a reflection coefficient design method in a reconfigurable reflector-assisted wireless communication system in the present invention. As the number of reflective units on the reconfigurable reflector increases, the performance of the optimized solution and the random reflection coefficient solution is compared. DETAILED DESCRIPTION

[0033] Table 1 is a table of system and rate simulation parameters for the present invention. In the simulation parameters, the number of users is 2, the signal-to-noise ratio is 10 dB, the base station transmission power is 15 dBm, and the Rice factor is 0.7. The number of base station antennas ranges from 50 to 300, and the number of reconfigurable reflector reflection units ranges from 8 to 64.

[0034] Table 1

[0035]

[0036] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0037] A reflection coefficient design method in a reconfigurable reflector-assisted wireless communication system is proposed. Assume that the number of users in the cell is K, the base station has M antennas, each user is equipped with only one transceiver antenna, and the reflector has N reflection units. The signal received by the kth user comes from two directions, one is the direct signal from the base station, and the other is the reflected signal from the reflector. Therefore, the signal expression received by user k is written as:

[0038] y k =(h IUk ΦH BI +h BUk )x+n k

[0039] in, s i It represents the signal flow sent by the base station to the user, satisfying represents the statistical expectation, I is the unit matrix, and in order to maintain generality, the noise received by the receiver It is subject to the mean of 0 and the variance of The cyclic symmetric complex Gaussian distribution is Φ, which is the reflection coefficient of the reconfigurable reflector. The expression is:

[0040]

[0041] like Figure 1 As shown, the specific implementation steps of the present invention mainly include the following eight steps:

[0042] Step 1: Input various parameters and information required by the algorithm. This step requires inputting all channels, h IUk is the channel vector from the reflection surface to the kth user, H BI is the channel matrix from the base station to the reflection surface, h BUk is the direct path channel vector from the base station to the kth user. Then set the initial value of the precoding matrix, the initial value of the reflection coefficient of the reflection surface, and the base station transmission power to W, Φ, P, and s respectively. k 2 (P、s k 2 are all positive numbers).

[0043] Step 2: Introduce parameter v = [v1, v2, ..., vN]H, and convert the user's signal-to-noise ratio expression into:

[0044]

[0045] in In order to maximize the signal-to-noise ratio of user k, according to the triangle inequality, the phase of the reflected path should be the same as the phase of the direct path. That is, the expression is satisfied Knowable So we can find the vector v opt-k The phase of each element in opt-k =diag(v opt-k ), so the reflection coefficient that maximizes the signal-to-noise ratio of user k can be obtained.

[0046] Step 3: Assume that γ is an arbitrary real number, then the kth user β k The value range of weight is (0,γ,2γ,…,1).

[0047] Step 301: Introduce weight factor β i ∈(0,1), let γ=1 / T, and β k The value range is (0,γ,2γ,…,1), and the calculation satisfies The feasible solution set B of

[0048] Step 302: For all β in set B, calculate the system sum rate;

[0049] Step 303: Output β that maximizes the system sum rate.

[0050] Step 30101: Let q = 0, L (q) ←K, remember c (q) =1, satisfied The set of all feasible solutions of is B;

[0051] Step 30102: If L (q) =1, then a feasible solution is returned. And end the current cycle; if L (q) =2, then a feasible solution is returned. And end the current loop; otherwise, set t←0;

[0052] Step 30103: Order calculate All feasible solutions of , the specific method is: let q←q+1, L (q) =L (q-1) -1, Jump to step 30102;

[0053] Step 30104: If t=T, terminate the current loop, otherwise set t←t+1 and return to step 30103.

[0054] In summary, a reflection coefficient design method in a reconfigurable reflector-assisted wireless communication system includes the following steps: Step 1: In the downlink of the communication system assisted by a reconfigurable reflector, the base station is configured with M antennas and N reflector units, and provides services for K single-antenna users at the same time. The base station adjusts the reflection coefficient of the reconfigurable reflector through a controller, thereby achieving the purpose of improving the communication quality of the system and increasing the transmission rate, h IUk is the channel vector from the reflection surface to the kth user, H BI is the channel matrix from the base station to the reflection surface, h BUk is the direct path channel vector from the base station to the kth user, the base station transmit power P, and the noise power of the kth user is Where k = 1, 2, ..., K, let the integer T represent the calculation accuracy; set the initial value of the precoding matrix and the initial value of the reflection coefficient of the reflection surface parameters W and Φ respectively, where P, are all positive numbers; Step 2: For each user k (k = 1, ... K), design the reflection coefficient Φ with the optimal signal-to-noise ratio (SNR) opt-k ; Step 3: Optimal reflection surface reflection coefficient where β k ∈(0,1) represents the weight of the kth user, and β=[β1,β2,…,β K ].

Claims

1. A method for designing reflection coefficient in a reconfigurable reflector-assisted wireless communication system, characterized in that: The following steps are involved: Step 1: In the downlink of the communication system assisted by the reconfigurable reflector, the base station is configured with M antennas and N reflector units, and provides services to K single-antenna users at the same time. The base station adjusts the reflection coefficient of the reconfigurable reflector through the controller, h IUk is the channel vector from the reflection surface to the kth user, H BI is the channel matrix from the base station to the reflection surface, h BUk is the direct path channel vector from the base station to the kth user, the base station transmit power P, and the noise power of the kth user is Where k = 1, 2, ..., K, let the integer T represent the calculation accuracy; set the initial value of the precoding matrix and the initial value of the reflection coefficient of the reflection surface parameters W and Φ respectively, where P, All are positive numbers; Step 2: For each user k, design the reflection coefficient Φ with the best signal-to-noise ratio (SNR) opt-k ; Step 3: Optimal reflectance of the reflective surface where β k ∈(0,1) represents the weight of the kth user, and β=[β1,β2,…,β K ]; In step 2, the reflection coefficient of the reflection surface with the optimal SNR for the kth user is Φ opt-k =diag(v opt-k ), where diag(·) represents the vector v opt-k is a diagonal matrix with diagonal elements, and Where j is an imaginary unit, {·} + represents the pseudo-inverse, and angle(·) represents the phase of the complex number; In step 3, select β that can maximize the system and rate. The specific steps are: Step 301: Let γ = 1 / T, and β k The value range is (0,γ,2γ,…,1), and the weight factor β is introduced i ∈(0,1), calculate to satisfy The feasible solution set B of Step 302: For all β in set B, calculate the system sum rate; Step 303: outputting β that maximizes the system sum rate; In step 301, the method for generating the feasible solution set B of β is as follows: Step 30101: Let q = 0, L (q) ←K, which means assigning the value of the number of users K to L (q) , L (q) represents the value of variable L in the qth iteration, b (q) Represented by variable b1 (q) , …b L (q) The feasible solution composed of (q) =1, c (q) Represents the value at the qth iteration, satisfying The set of all feasible solutions of is B; Step 30102: If L (q) =1, then a feasible solution is returned. And end the current loop; if the variable L (q) =2, then a feasible solution is returned. And end the current loop; otherwise, set t←0; Step 30103: Order calculate All feasible solutions of , the specific method is: let q←q+1, L (q) =L (q-1) -1, Represents the variable To variable The feasible solution composed of Jump to step 30102; Step 30104: If t=T, terminate the current loop, otherwise set t←t+1 and return to step 30103; In step 302, the sum rate of the system is calculated according to the following formula: Among them, SINR k represents the signal-to-interference-noise ratio of the kth user, and the calculation formula is: Among them, let h k =h IUk Φ opt H BI +h BUk represents the equivalent channel of the kth user, w k represents the beamforming vector of the kth user, w j represents the beamforming vector of the jth user, in(·) H stands for conjugate transpose.

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