A smart reflector phase shift allocation method suitable for 6G mobile communication environment

Abstract

The present invention discloses a phase shift allocation method for an intelligent reflecting surface suitable for a 6G mobile communication environment, comprising: step 1, collecting channel state information between a base station, an intelligent reflecting surface, and edge users within the base station in the 6G mobile communication environment; step 2, initializing heuristic information and pheromones on a construction graph based on an ant colony optimization algorithm; step 3, generating multiple artificial ants to complete a walk on the construction graph; recording the paths and corresponding phase shift allocation vectors; step 4, selecting the optimal phase shift allocation vector; step 5, selecting to execute step 7 or 6 based on a stopping condition; step 6, updating the pheromones on the construction graph and jumping to step 3; step 7, stopping and outputting the phase shift allocation vector to complete the allocation. Based on the ant colony optimization tool, the present invention proposes a phase shift control method using an intelligent reflecting surface to improve the communication rate of cell edge users, thereby maximizing the system's achievable information rate.

Description

Technical Field

[0001] The present invention relates to a reflection surface phase shift allocation method, in particular to an intelligent reflection surface phase shift allocation method suitable for a 6G mobile communication environment. Background Art

[0002] Current and next-generation (6G) cellular networks offer ever-increasing data rates, but also come with increasing energy consumption. Reducing this energy loss is a hot topic in next-generation cellular network research, and energy efficiency is becoming a key indicator for sustainable and green cellular communication networks. Intelligent Reflecting Surfaces (IRS), a new technology for signal transmission and reception, have the potential to significantly reduce energy consumption and are being widely researched.

[0003] IRS is a programmable plane composed of a large number of metal square units. Each unit can be independently digitally controlled to apply different reflection amplitude, phase, polarization and frequency response to the incident signal. The most important of these is the difficult control of the phase of the incident signal.

[0004] In a 6G wireless mobile communication network scenario where base stations are equipped with massive MIMO (multiple in multiple out) technology, CoMP (coordinated multipoint) transmission is used between base stations, with the assistance of an Intelligent Reflecting Surface (IRS) to improve the signal quality for users at the cell edge. The IRS transmits the received signal from the base station to the cell edge users. However, determining the phase shift of the reflected signal by each reflector in the IRS to maximize the information rate available to the cell edge users is a pressing issue. Summary of the Invention

[0005] Purpose of the invention: The technical problem to be solved by the present invention is to provide an intelligent reflection surface phase shift allocation method suitable for 6G mobile communication environment in response to the shortcomings of the existing technology.

[0006] In order to solve the above technical problems, the present invention discloses an intelligent reflection surface phase shift allocation method suitable for 6G mobile communication environment, comprising the following steps:

[0007] Step 1: collecting channel state information between the base station, the smart reflective surface, and the edge user within the base station in the 6G mobile communication environment;

[0008] Step 2: Initialize the heuristic information and pheromone on the construction graph according to the ant colony optimization algorithm;

[0009] Step 3: Generate multiple artificial ants to complete the ant walking on the construction graph; record the ant walking path and the corresponding phase shift allocation vector;

[0010] Step 4: Select the optimal phase shift allocation vector;

[0011] Step 5: Determine whether the stop condition is met. If so, execute step 7; otherwise, execute step 6.

[0012] Step 6, update the pheromone on the construction graph and jump to step 3;

[0013] Step 7: Stop and output the phase shift allocation vector to complete the intelligent reflection surface phase shift allocation in the 6G mobile communication environment.

[0014] The channel state information described in step 1 is collected by the intelligent reflecting surface IRS as follows:

[0015] Assume that in the 6G mobile communication environment, there are N cellular cells, K users at the cell edge, and N users per user. r The intelligent reflector IRS contains M independent reflective units; the intelligent reflector IRS collects the channel coefficient matrix H between the kth user and the nth base station BS respectively. n,k , the complex channel coefficient matrix G between the nth base station BS and the intelligent reflecting surface IRS n,r , and the complex channel coefficient matrix H between the intelligent reflecting surface IRS and the kth user r,k .

[0016] The heuristic information and pheromone on the initial construction graph described in step 2 means: each edge e in the initial construction graph s,s+1 Pheromone τ distributed on (i,l) s,s+1 (i,l) and heuristic information η s,s+1 (i, l), where s represents the current state index, s+1 represents the next state index, i represents the smart reflector unit index, and l represents the phase shift parameter index.

[0017] Pheromone τ as described in step 2 s,s+1 (i,l), is calculated by the following formula:

[0018] τ s,s+1 (i,l)=(τ max +τ min ) / 3

[0019] Among them, the pheromone upper limit τ is set max =8, pheromone lower limit τ min=3, s=0,1,...,M-1, i=1,2,...,M, l=1,2,...,2 b , where b is the binary bit value used to represent the total number of phase shift levels, and its value is generally between 2 and 6.

[0020] The heuristic information η described in step 2 s,s+1 (i,l), is calculated by the following formula:

[0021]

[0022] in, is the interference and noise matrix after independent phase shift selection, represents the beamforming matrix combination of all N base stations for the jth user, W j The conjugate transpose of express The conjugate transpose of represents the beamforming matrix combination of all N base stations for the kth user, where [.] T represents transpose, [.] H represents conjugate transpose; W n,k represents the transmit beamforming matrix between the nth base station BS and the kth user; σ 2 is the noise power received by each antenna of the user, Indicates N r -dimensional identity matrix; is the combination of the channel matrix complex quantities between the base station and the user after independent phase shift selection, The matrix elements in Defined as: Φ i,l Represents the phase shift parameter set selected by the i-th reflector unit of the intelligent reflector IRS The lth element in The diagonal matrix formed is

[0023] The method for completing the ant walking on the construction graph described in step 3 includes: generating N ant There are artificial ants, each ant has a probability P(e s,s+1 (i,l)) select edge e s,s+1 (i, l), record the edges that the ant moves M times, where the number of times each ant moves is equal to the number of reflection units, select the phase shift parameters corresponding to the edges in the path to form a phase shift allocation vector θ; record the ant's walking path and the phase shift allocation vector θ.

[0024] The probability P(e) described in step 3 s,s+1(i,l)), is calculated by the following formula:

[0025]

[0026] Among them, e s,s+1 (i,l) represents the (i,l)th edge between state s and the next state s+1, represents the set of all edges between state s and the next state s+1, τ s,s+1 (i,l) represents edge e s,s+1 The pheromone content on (i, l), η s,s+1 (i,l) represents edge e s,s+1 The prior heuristic information value on (i, l); the number of ants N ant Set between 5-20.

[0027] The method for selecting the optimal phase shift allocation vector in step 4 includes: calculating the system information rate of the phase shift allocation vector θ selected by each ant recorded in step 3, and selecting the phase shift allocation vector corresponding to the maximum system information rate as the optimal phase shift allocation vector.

[0028] The system information rate R described in step 4 sys , suppose each ant chooses a phase shift allocation vector θ=[θ1,θ2,…,θ M ] T , calculated using the following formula:

[0029]

[0030] in, is the interference and noise matrix, is the complex channel coefficient matrix between IRS and the kth user, is the combination of the complex channel coefficient matrix, Assume that the phase shift allocation vector θ selected by each ant is [θ1,θ2,…,θ M ] T .

[0031] The determination of whether the stopping condition is satisfied in step 5 is to determine whether the preset maximum number of iterations is reached.

[0032] The update described in step 6 builds the pheromone τ on the graph s,s+1 The method for (i,l) is as follows:

[0033]

[0034] Wherein, ρ is set as the pheromone volatilization constant, which is set to ρ = 0.2; Optimal Path Maximum system information rate The corresponding path, e s,s+1 (i,l) is the optimal path The edges included in , C is a constant used to control the influence of the maximum system information rate on pheromone, and its value is set to C = 0.05.

[0035] Beneficial effects:

[0036] This paper aims to improve the communication quality of users at the cell edge using an IRS. It proposes a phase shift allocation method to control the phase of each IRS unit. By having the IRS reflect base station signals to cell edge users, the communication rate is increased, maximizing the system's achievable information rate. Experiments have demonstrated that the proposed method achieves excellent performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more apparent.

[0038] Figure 1 Schematic diagram of the scene of the present invention.

[0039] Figure 2 The corresponding constructed diagram for the IRS phase shift control problem.

[0040] Figure 3 This is the topology diagram of the simulation scene.

[0041] Figure 4 Schematic diagram of real-time system capacity when the number of IRS reflection units is 100.

[0042] Figure 5 Schematic diagram of real-time system capacity when the number of IRS reflection units is 200.

[0043] Figure 6 Schematic diagram of real-time system capacity when the number of IRS reflection units is 300.

[0044] Figure 7 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0046] like Figure 7 As shown, the present invention discloses an intelligent reflection surface phase shift allocation method suitable for 6G mobile communication environment.

[0047] The present invention considers a multi-user MIMO wireless communication system assisted by an intelligent reflecting surface (IRS), which includes N BSs, K cell edge users and 1 IRS. Figure 1 As shown in the figure, a base station (BS) equipped with multiple antennas provides downlink communication services to multiple cell edge users. Each base station is equipped with N t (N t >1) transmit antennas, and each cell edge user is equipped with N r (N r >1) receiving antennas, and the IRS has M reflecting units. The set of BS, user and IRS is represented by and express. and They represent the complex channel coefficient matrix between the kth user and the nth BS, the complex channel coefficient matrix between the nth BS and the IRS, and the complex channel coefficient matrix between the IRS and the kth user, respectively.

[0048] The signal sent by the nth BS can be expressed as

[0049]

[0050] in represents the transmit beamforming matrix between the nth BS and the kth user, represents d data streams sent to user k. The signal received by the kth user can be expressed as

[0051]

[0052] in is the additive white Gaussian noise received by the kth user, represents the complex Gaussian distribution, σ 2 is the noise power received by each antenna of the user, Indicates N r dimensional identity matrix. represents the phase shift matrix used by IRS, diag represents the diagonal matrix, θ m ∈0,2π) represents the phase shift parameter of the mth reflector unit. In order to ensure the simplicity of actual implementation, the present invention considers the discrete value of the RIS unit phase shift parameter and uses b-bit binary values ​​to represent different phase shift levels. In this way, the total number of phase shift levels is 2 b Furthermore, assuming that the discrete phase shift levels are uniformly distributed in the interval [0,2π], for each IRS unit, the discrete phase shift parameter θi It can be expressed as

[0053]

[0054] For simplicity, define in[.] T represents transpose. Formula (2) can be rewritten as

[0055]

[0056] The information rate that the kth user can obtain is

[0057]

[0058] in[.] H represents the conjugate transpose,

[0059] The problem of maximizing the information rate that all edge users can obtain can be expressed as

[0060]

[0061]

[0062]

[0063] The above problem is a combinatorial optimization problem. Although it can be solved by exhaustive search method, the required computational complexity is too high to be applied in practice.

[0064] The present invention aims to improve the communication quality of users at the edge of a cellular cell by using an IRS. A phase shift allocation method is proposed to control the phase of each unit of the IRS. The IRS reflects the base station signal to the users at the edge of the cell to improve their communication rate and maximize the information rate that can be obtained by the system.

[0065] To achieve the above-mentioned object of the invention, the present invention proposes a low-complexity phase shift allocation method based on ACO (ant colony optimization). In ACO, artificial ants construct solutions by moving on a construction graph. In each iteration, each ant continuously constructs partial solutions by moving from one edge to another along the edge of the construction graph. When the complete solution is constructed, the ants will leave a certain amount of pheromone on the edges they pass through. The amount of pheromone is related to the quality of the solution. The better the quality of the solution, the greater the amount of pheromone. The ants in the next iteration are guided by pheromones to further search for promising areas in the solution space and update the pheromones. A further detailed description of the solution construction and pheromone update process performed in each iteration is as follows:

[0066] (1) Deconstruction

[0067] The corresponding construction diagram of the problem in formula (6) is as follows Figure 2 As shown. Each edge e s,s+1 (j) corresponds to the phase shift selected by a reflector unit in the IRS, s represents the current state index, and s+1 represents the next state index. Each ant starts from state 0 and reaches the next state by selecting an edge. For the problem in the present invention, at state 0, there are M units that need to select phase shift parameters, and each unit can select 2 phase shift parameters. b Therefore, the number of edges available for ants to choose is M·2 b Each time the ant moves, it selects a phase shift parameter for an IRS unit. When it reaches the next state, the number of edges available for the ant to choose will decrease by 2. b , so after M moves, the ant selects M edges to reach the final state M, and the phase shift parameters corresponding to these edges are the solution to the problem.

[0068] In the process of constructing the solution, the ant selects the phase shift of the IRS unit through a random mechanism. In state s, the ant selects an edge to reach the next state s+1 in a probabilistic way. The probability of selecting the edge (i, l) is:

[0069]

[0070] Among them, e s,s+1 (i,l) represents the (i,l)th edge between state s and the next state s+1, represents the set of all edges between state s and the next state s+1, τ s,s+1 (i,l) represents edge e s,s+1 The pheromone content on (i, l), η s,s+1 (i,l) represents edge e s,s+1 The value of the prior heuristic information on (i,l).

[0071] Heuristic information is related to the properties of the problem being solved. Good heuristic information helps to solve the problem quickly. The problem solved by the present invention is to enable multiple edge users to obtain the maximum information rate by adjusting the IRS phase shift parameters. Therefore, for each reflector in the IRS, it tends to select the phase shift parameter that can maximize the current total information rate without considering the phase shift parameters of other reflectors. Therefore, for the i-th reflector in the phase shift set Choose a phase shift as your own phase shift parameter θ i To maximize the objective function in (6) While ignoring the influence of other reflection units on the information rate, the phase shift matrix can be expressed as Therefore, for the problem to be solved, the present invention defines heuristic information as:

[0072]

[0073] in Φ i,l Indicates the phase shift set selected by the i-th reflection unit of IRS The lth element in also

[0074] It is easy to see that when an edge contains a higher pheromone concentration and a larger heuristic information value, it is more likely to be selected by ants.

[0075] (2) Pheromone Update

[0076] The purpose of pheromone updating is to increase the pheromone content associated with high-quality solutions or potential high-quality solutions, while reducing the pheromone content associated with low-quality solutions. The pheromone updating rules of the algorithm are as follows:

[0077]

[0078] Where ρ is the pheromone volatility constant, Optimal Path Maximum system information rate The corresponding path, e s,s+1 (i,l) is the optimal path The edges included in , C is a constant used to control the influence of the maximum system information rate on pheromones.

[0079] In order to verify the performance of the phase shift allocation method proposed in the present invention, the following simulation is used. Each BS is located at a side length of The center of the hexagonal cell, the height between the BS and the IRS is 10m. The large-scale path loss is defined as Where L0 represents the power gain of the channel at the reference distance d0 = 1m, d x is the link communication distance, α is the path loss exponent, and L0 is set to -30dB in the simulation. The other parameters of the system are set as follows: d = 2, the number of user receiving antennas N r =2, noise power σ 2 =-80dBm. Figure 3 The topology shown in the figure includes three base stations and three edge users. The coordinates of the three base stations are (-300m, 0), (300m, 0) and All users are evenly distributed at the center of the circle The IRS is located at the center of a circle with a radius of 40m. The height of the BS is 10m. Set the number of BS transmitting antennas N t =6, the number of reflection units contained in the IRS is M=200, and the binary bit value b for controlling the total number of phase shift levels is b=4.

[0080] Figure 4-Figure 6 The figures show the real-time system information rates achieved by the method of the present invention, the random phase shift selection method, and the optimal exhaustive search method under three different conditions: the number of IRS reflection units M = 100, 200, and 300. The number of ants used in the method of the present invention is 10. In each time slot, the user's position and channel state information are updated. Table 1 shows the average system information rate achieved in 1000 simulations using random channel implementations.

[0081] Table 1

[0082]

[0083] It can be seen from the above simulation data that the method of the present invention can achieve performance close to the optimal method while reducing computational complexity. Compared with the random phase shift method, the method of the present invention can increase the system information rate by about 35%.

[0084] In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, wherein the computer storage medium is capable of storing a computer program that, when executed by the data processing unit, can execute the invention content of the method for allocating phase shifts of an intelligent reflector surface suitable for a 6G mobile communication environment provided by the present invention and some or all of the steps in each embodiment. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0085] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present invention can be implemented by means of computer programs and their corresponding general hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a computer program, i.e., a software product. The computer program software product can be stored in a storage medium and includes a number of instructions for enabling a device including a data processing unit (which can be a personal computer, server, single-chip microcomputer, MUU or network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0086] The present invention provides a method and concept for allocating phase shifts on intelligent reflective surfaces suitable for 6G mobile communication environments. While numerous methods and approaches exist for implementing this technical solution, the foregoing merely represents a preferred embodiment of the present invention. It should be noted that those skilled in the art may make improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are considered within the scope of protection of the present invention. Any components not specified in this embodiment may be implemented using existing technologies.

Claims

1. A smart reflector phase shift allocation method suitable for 6G mobile communication environment, characterized in that: The following steps are involved: Step 1: collecting channel state information between the base station, the smart reflective surface, and the edge user within the base station in the 6G mobile communication environment; Step 2: Initialize the heuristic information and pheromone on the construction graph according to the ant colony optimization algorithm; Step 3: Generate multiple artificial ants to complete the ant walking on the construction graph; record the ant walking path and the corresponding phase shift allocation vector; Step 4: Select the optimal phase shift allocation vector; Step 5: Determine whether the stop condition is met. If so, proceed to step 7; otherwise, proceed to step 6. Step 6, update the pheromone on the construction graph and jump to step 3; Step 7: Stop and output the phase shift allocation vector to complete the smart reflection surface phase shift allocation in the 6G mobile communication environment; The channel state information described in step 1 is collected by the intelligent reflecting surface IRS as follows: Assume that in the 6G mobile communication environment, there are N cellular cells, K users at the cell edge, and N users per user. r The intelligent reflector IRS contains M independent reflective units; the intelligent reflector IRS collects the channel coefficient matrix H between the kth user and the nth base station BS respectively. n,k , the complex channel coefficient matrix G between the nth base station BS and the intelligent reflecting surface IRS n,r , and the complex channel coefficient matrix H between the intelligent reflecting surface IRS and the kth user r,k ; The heuristic information and pheromone on the initial construction graph described in step 2 means: each edge e in the initial construction graph s,s+1 Pheromone τ distributed on (i,l) s,s+1 (i,l) and heuristic information η s,s+1 (i, l), where s represents the current state index, s+1 represents the next state index, i represents the smart reflector unit index, and l represents the phase shift parameter index; Pheromone τ as described in step 2 s,s+1 (i,l), is calculated by the following formula: t s,s+1 (i,l)=(τ max +t min ) / 3 Among them, the pheromone upper limit τ is set max =8, pheromone lower limit τ min =3, s=0,1,...,M-1, i=1,2,...,M, l=1,2,...,2 b , where b is the binary bit value used to represent the total number of phase shift levels; The heuristic information η described in step 2 s,s+1 (i,l), is calculated by the following formula: in, is the interference and noise matrix after independent phase shift selection, represents the beamforming matrix combination of all N base stations for the jth user, W j The conjugate transpose of express The conjugate transpose of represents the beamforming matrix combination of all N base stations for the kth user, where [.] T represents transpose, [.] H represents conjugate transpose; W n,k represents the transmit beamforming matrix between the nth base station BS and the kth user; σ 2 is the noise power received by each antenna of the user, I Nr Indicates N r -dimensional identity matrix; is the combination of the channel matrix complex quantities between the base station and the user after independent phase shift selection, The matrix elements in Defined as: Φ i,l Represents the phase shift parameter set selected by the i-th reflector unit of the intelligent reflector IRS The lth element in The diagonal matrix formed is 2. The method for allocating phase shifts of an intelligent reflector surface suitable for a 6G mobile communication environment according to claim 1, characterized in that: The method for completing the ant walking on the construction graph described in step 3 includes: generating N ant There are artificial ants, each ant has a probability P(e s,s+1 (i,l)) select edge e s,s+1 (i, l), record the edges that the ant moves M times, where the number of times each ant moves is equal to the number of reflection units, select the phase shift parameters corresponding to the edges in the path to form a phase shift allocation vector θ; record the ant's walking path and the phase shift allocation vector θ.

3. The method for allocating phase shifts of an intelligent reflector surface suitable for a 6G mobile communication environment according to claim 2, wherein: The probability P(e) described in step 3 s,s+1 (i,l)), is calculated by the following formula: Among them, e s,s+1 (i,l) represents the (i,l)th edge between state s and the next state s+1, represents the set of all edges between state s and the next state s+1, τ s,s+1 (i,l) represents edge e s,s+1 The pheromone content on (i, l), η s,s+1 (i,l) represents edge e s,s+1 The prior heuristic information value on (i, l); the number of ants N ant Set between 5-20.

4. The method for allocating phase shifts of an intelligent reflector surface suitable for a 6G mobile communication environment according to claim 3, wherein: The method for selecting the optimal phase shift allocation vector in step 4 includes: calculating the system information rate of the phase shift allocation vector θ selected by each ant recorded in step 3, and selecting the phase shift allocation vector corresponding to the maximum system information rate as the optimal phase shift allocation vector.

5. The method for allocating phase shifts of an intelligent reflector surface suitable for a 6G mobile communication environment according to claim 4, wherein: The system information rate R described in step 4 sys , suppose each ant chooses a phase shift allocation vector θ=[θ1,θ2,…,θ M ] T , calculated using the following formula: in, is the interference and noise matrix, is the complex channel coefficient matrix between IRS and the kth user, is the combination of the complex channel coefficient matrix, Assume that the phase shift allocation vector θ selected by each ant is [θ1,θ2,…,θ M ] T .

6. The method for allocating phase shifts of an intelligent reflector surface suitable for a 6G mobile communication environment according to claim 5, characterized in that: The determination of whether the stopping condition is satisfied in step 5 is to determine whether the preset maximum number of iterations is reached.

7. The method for allocating phase shifts of an intelligent reflector surface suitable for a 6G mobile communication environment according to claim 6, wherein: The update described in step 6 builds the pheromone τ on the graph s,s+1 The method for (i,l) is as follows: Wherein, ρ is set as the pheromone volatilization constant, which is set to ρ = 0.2; Optimal Path Maximum system information rate The corresponding path, e s,s+1 (i,l) is the optimal path The edges included in , C is a constant used to control the influence of the maximum system information rate on pheromone, and its value is set to C = 0.05.

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