An active intelligent metasurface assisted MIMO communication system control method

Abstract

The application relates to an active intelligent super-surface assisted MIMO communication system control method, a communication system comprising a base station, an active intelligent super-surface and a plurality of user terminals, a direct link between the base station and the user terminals being blocked by an obstacle, the method comprising: establishing a received signal model and a quantization noise model at the base station, and obtaining an uplink rate of each user terminal and a sum rate of all users; performing signal processing at the base station by using maximum ratio combining and an analog-to-digital converter with a precision lower than a preset threshold; establishing an active intelligent super-surface phase shift optimization problem according to the received signal model and the uplink rate, solving the optimization problem to obtain an optimal phase shift matrix, and controlling the active intelligent super-surface to operate according to the optimal phase shift matrix. Compared with the prior art, the application can overcome the multiplicative fading effect of a passive intelligent super-surface, and the problem of reduced communication performance caused by an undesirable phase shift, improve the communication system performance, and reduce system energy consumption and hardware cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a method for controlling a MIMO communication system assisted by an active intelligent metasurface. BACKGROUND

[0002] Intelligent metasurfaces are considered as a breakthrough technology for the sixth generation of mobile networks (6G) and can reconfigure the radio propagation environment. On the one hand, intelligent metasurfaces can improve the sum rate of the system; on the other hand, it also has the advantages of low deployment cost and low energy consumption.

[0003] However, most contributions consider the intelligent metasurface as a passive component, but it also has some drawbacks. Without signal amplification, the received signal decays quickly due to the multiplicative fading effect, which limits the system performance.

[0004] A large part of the existing contributions assumes instantaneous channel state information (CSI) and perfect hardware at the base station, in addition to the wide application of massive MIMO in a large number of communication systems. It requires a large number of antennas, resulting in a significant increase in device power consumption and hardware cost. A promising solution to the above problems is to apply low-precision analog-to-digital converters in massive MIMO systems.

[0005] There is currently no suitable solution to solve the problem of low rate caused by the influence of multiplicative fading in intelligent metasurface assisted massive MIMO communication systems, so the scheme of active intelligent metasurface combined with low-precision analog-to-digital converters has feasibility and practical significance. SUMMARY

[0006] The purpose of the present application is to overcome the above-mentioned defects of passive intelligent metasurfaces caused by multiplicative fading effects and provide a method for controlling a MIMO communication system assisted by an active intelligent metasurface.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] A method for controlling a MIMO communication system assisted by an active intelligent metasurface, the communication system comprising a base station, an active intelligent metasurface and a plurality of user terminals, the direct link between the base station and the user terminals being blocked by an obstacle, the method comprising:

[0009] establishing a received signal model and a quantization noise model at the base station, and obtaining the uplink rate of each user terminal and the sum rate of all users;

[0010] the base station uses maximum ratio combining and an analog-to-digital converter with a precision lower than a preset threshold to process the signal;

[0011] An active intelligent metasurface phase shift optimization problem is established according to the received signal model and the uplink rate, and an optimal phase shift matrix is obtained by solving the optimization problem, and the active intelligent metasurface is controlled to operate according to the optimal phase shift matrix.

[0012] Further, the base station has M antennas, the active intelligent metasurface has N reflecting elements, the communication system includes K user terminals, and the channels from the user terminals to the active intelligent metasurface and from the active intelligent metasurface to the base station adopt a Rician fading model.

[0013] Further, the signal reflected by the active intelligent metasurface is represented as:

[0014] y R =AΦH1Px+AΦv

[0015] wherein A represents an amplification coefficient matrix of the active intelligent metasurface, η n Φ represents a reflection coefficient matrix of the active intelligent metasurface, θ n represents a phase shift of an element in the active intelligent metasurface; H1 represents a channel matrix from the user to the active intelligent metasurface; P represents a transmission power matrix of the user, p k is the transmission power of the kth user terminal; and v represents thermal noise of the active intelligent metasurface.

[0016] Further, the signal transmitted by the user terminal to the base station through the active intelligent metasurface is represented as:

[0017] y=H2AΦH1Px+H2AΦv+n

[0018] wherein H2 represents a channel matrix from the active intelligent metasurface to the base station, and n represents noise at the base station.

[0019] Further, the signal after adding a quantization noise model to the signal transmitted by the user terminal to the base station through the active intelligent metasurface is represented as:

[0020] y q =αy+n q

[0021] wherein α=1-ρ, and ρ is the reciprocal of the signal-to-quantization noise ratio.

[0022] Further, the signal after maximum ratio combining processing of the signal after adding the quantization noise model is represented as:

[0023] r=G H y q

[0024] wherein G = H 2 A Phi H 1, G H denotes the conjugate transpose matrix of G.

[0025] Further, the signal of the kth user at the base station is represented as:

[0026]

[0027] wherein g k = H 2 A Phi h k , h k denotes the channel vector of the kth user to the active intelligent metasurface.

[0028] Further, the calculation expression of the uplink rate of the kth user terminal is:

[0029]

[0030] wherein gamma k is the signal-to-noise ratio at the base station, R k is the uplink rate of the kth user terminal.

[0031] Further, the uplink rate of the kth user terminal is approximately:

[0032]

[0033] wherein,

[0034] Further, the phase shift optimization problem of the active intelligent metasurface is solved by using a genetic algorithm, and the phase shift optimization problem is represented as:

[0035] .

[0036]

[0037] Compared with the prior art, the present application has the following advantages:

[0038] (1) In order to improve the communication performance of the large-scale MIMO system and reduce the hardware overhead and energy consumption, the active intelligent metasurface is combined with the large-scale MIMO communication system, and a low-precision analog-to-digital converter is introduced at the base station. The intelligent metasurface also has the problem that the communication performance is reduced due to the non-ideal phase shift. By optimizing the phase shift of the intelligent metasurface, the optimal phase shift matrix can be obtained, and the problem of reduced communication performance caused by non-ideal phase shift is further overcome.

[0039] Overall, the application introduces an active intelligent metasurface into a large-scale MIMO communication network, and by utilizing the active intelligent metasurface to assist communication and combining a low-precision analog-to-digital converter at the base station, the application can overcome the multiplicative fading effect of the passive intelligent metasurface, improve the performance of the communication system, and reduce the energy consumption and hardware cost of the system.

[0040] (2) The application establishes a received signal model and an additive quantization noise model, applies MRC reception and a low-precision analog-to-digital converter, and obtains the uplink rate of each user terminal and the sum rate of all users.

[0041] (3) The communication system of the application is a multi-user large-scale MIMO system with a low-precision analog-to-digital converter, and compared with the existing intelligent metasurface-based communication technology, the scenario is closer to real life, and therefore the application scenario of the communication system of the application is wide and the applicability is good.

[0042] (4) The communication system of the application adopts an active intelligent metasurface to assist communication, the active intelligent metasurface can suppress interference signals and amplify useful signals at the user, and at the same time, the active intelligent metasurface has a simple structure and low energy consumption, so the hardware cost is low and the energy efficiency is higher.

[0043] (5) The application receives signals under the conditions of a Rician channel and perfect channel state information, and has a wide application range. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 A transmission schematic diagram of a MIMO communication system assisted by an active intelligent metasurface provided in an embodiment of the application;

[0045] Figure 2 A relationship diagram of uplink achievable rate and the number of antennas when the number of quantization bits is 1 in an embodiment of the application;

[0046] Figure 3 A relationship diagram of uplink achievable rate and user transmit power when the number of quantization bits is 1 in an embodiment of the application. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions and advantages of the embodiments of the application clearer, the technical solutions in the embodiments of the application will be described below in conjunction with the drawings of the embodiments of the application. Obviously, the described embodiments are some but not all of the embodiments of the application. The components of the embodiments of the application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0048] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0049] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0050] Example 1

[0051] like Figure 1 As shown, this embodiment provides a multi-user uplink massive MIMO communication system with a low-precision analog-to-digital converter assisted by an active smart metasurface. The massive MIMO communication system includes a base station, an active smart metasurface, and multiple user terminals. The base station has multiple transmit antennas and multiple receive antennas. The active smart metasurface has multiple reflective elements. The direct link between the base station and the user terminals is blocked by an obstacle.

[0052] In this embodiment, the base station has M antennas, the active smart metasurface has N reflective elements, and the communication system includes K user terminals.

[0053] The channel state of the large-scale MIMO communication system of this invention is quasi-static, and the channel state information can be fully acquired by the base station. The base station calculates the received signal and the optimal phase shift of the active intelligent metasurface in real time, and sends control information to the active intelligent metasurface through a dedicated channel. This invention establishes a received signal model and an additive quantization noise model, and obtains the uplink rate of each user terminal and the sum rate of all users. Maximum ratio combining (MRC) and a low-precision analog-to-digital converter are used at the base station. Based on the received signal model and the uplink rate, an active intelligent metasurface phase shift optimization problem is established, and the optimal phase shift matrix is ​​obtained by solving this optimization problem. The active intelligent metasurface operates according to this matrix.

[0054] The specific steps for implementing the control method of a multi-user uplink massive MIMO communication system with a low-precision analog-to-digital converter assisted by an active intelligent metasurface include:

[0055] Maximum ratio combining (MRC) and low-precision analog-to-digital converters are used at the base station.

[0056] Establish the received signal model and the additive quantization noise model, and obtain the uplink rate of each user terminal and the sum rate of all users;

[0057] An active intelligent metasurface phase shift optimization problem is established according to a received signal model and an uplink rate, and an optimal phase shift matrix is obtained by solving the optimization problem, and the active intelligent metasurface operates according to the matrix;

[0058] Specifically, the base station has M antennas, the intelligent metasurface has N reflecting elements, and the communication system includes K user terminals.

[0059] First, the user-to-active intelligent metasurface and active intelligent metasurface Rician channel is expressed as follows:

[0060] H1=[h1,...,h k ,...,h K ]

[0061]

[0062]

[0063] wherein H1 represents a channel matrix of a user-to-active intelligent metasurface, h k represents a channel vector of the kth user-to-active intelligent metasurface, H2 represents a channel matrix of an active intelligent metasurface-to-base station. α k and β represent large-scale fading coefficients, ε k and δ represent Rician factors. and represent non-line-of-sight components, which are subject to a complex Gaussian distribution (0, 1). and represent line-of-sight components, which are given as follows

[0064]

[0065]

[0066] wherein, and respectively represent the azimuth angle and the elevation angle of the kth user-to-active intelligent metasurface arrival; and respectively represent the azimuth angle and the elevation angle of the active intelligent metasurface-to-base station departure; and respectively represent the azimuth angle and the elevation angle received at the base station. The vector represents

[0067]

[0068] The right upper corner T represents the transpose of the matrix.

[0069] Further, the signal reflected by the active intelligent metasurface can be expressed as

[0070] y R = AΦH1Px + AΦv

[0071] where A denotes the amplification coefficient matrix of the active smart metasurface, η n η.Φ denotes the reflection coefficient matrix of the smart metasurface, θ n denotes the phase shift of the element in the active smart metasurface. H1 denotes the channel matrix from the user to the active smart metasurface. P denotes the transmit power matrix of the user, p k is the transmit power of the kth user. v denotes the thermal noise of the active smart metasurface. The amplification coefficient can be calculated as

[0072]

[0073] p A is the power of the active smart metasurface, is the average power of the dynamic noise of the active smart metasurface.

[0074] Further, the signal transmitted by the user through the active smart metasurface to the base station can be represented as

[0075] y = H2AΦH1Px + H2AΦv + n

[0076] where H2 denotes the channel matrix from the active smart metasurface to the base station, n denotes the noise at the base station, and the average power is

[0077] Further, the signal after quantization of the signal using an additive quantization noise model can be represented as

[0078] y q = αy + n q

[0079] where α = 1 - ρ, and ρ is the inverse of the signal-to-quantization noise ratio.

[0080] Table 1 ρ corresponding to different quantization bits

[0081]

[0082] Further, the signal after MRC reception processing can be represented as

[0083] r = G H y q

[0084] where G = H2AΦH1, and G H denotes the conjugate transpose matrix of G.

[0085] The signal of the kth user at the base station can be expressed as

[0086]

[0087] where g k = H2AΦh k , h k denotes the channel vector of the kth user to the active smart metasurface.

[0088] Further, the signal-to-interference-and-noise ratio γ k at the base station is given by

[0089]

[0090] Thus, the uplink achievable rate of the kth user is given by

[0091]

[0092] Further, the uplink achievable rate can be approximated as

[0093]

[0094] where The computational expression is given by

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] where .

[0101] Further, the phase shift of the active smart metasurface can be optimized using a genetic algorithm, and the optimization problem can be expressed as

[0102]

[0103]

[0104] The genetic algorithm toolbox in matlab can be used to solve it.

[0105] Figure 2 , Figure 3The uplink reachable rate and the change relationship between the base station antenna number and the user transmission power are respectively shown, and the reachable rates of the active intelligent metasurface and the passive intelligent metasurface under the same condition are compared, and the optimization method can further improve the uplink reachable rate of the active intelligent metasurface. It can be seen from the figure that the active intelligent metasurface has more advantages than the passive intelligent metasurface, solves the multiplicative fading effect, and brings several times performance improvement for the system.

[0106] In the embodiment, the active intelligent metasurface is used to assist the large-scale MIMO communication system with low-precision analog-to-digital converters, and it is proved that the active intelligent metasurface can greatly improve the performance of the communication system, and the low-precision analog-to-digital converter reduces the energy consumption of the system. In summary, the energy consumption and hardware cost of the system are low, and the performance of the communication system can be greatly improved. The embodiment assumes that the communication channel is a Rician channel, and the channel has perfect channel state information.

[0107] The above describes the preferred embodiments of the present application in detail. It should be understood that those skilled in the art can make many modifications and changes without creative labor according to the concept of the present application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment on the basis of the prior art according to the concept of the present application shall be within the protection scope determined by the claims.

Claims

1. A control method for an active intelligent metasurface-assisted MIMO communication system, characterized in that, The communication system includes a base station, an active smart metasurface, and multiple user terminals. The direct link between the base station and the user terminals is blocked by an obstacle. The method includes: Establish the received signal model and quantization noise model at the base station, and obtain the uplink rate of each user terminal and the sum rate of all users; The base station uses a maximum ratio combiner and an analog-to-digital converter with an accuracy lower than a preset threshold for signal processing. Based on the received signal model and uplink rate, an active intelligent metasurface phase shift optimization problem is established. The optimal phase shift matrix is ​​obtained by solving the optimization problem, and the active intelligent metasurface is controlled to operate according to the optimal phase shift matrix. The base station has The active smart metasurface has a root antenna. The communication system includes a reflective element. K A user terminal, wherein the channels from the user terminal to the active smart metasurface and from the active smart metasurface to the base station adopt the Ricean fading model; The signal reflected by the active intelligent metasurface is represented as follows: in, This represents the magnification matrix of the active smart metasurface. , ; This represents the reflection coefficient matrix of an active smart metasurface. , This represents the phase shift of elements in an active intelligent metasurface; This represents the channel matrix from the user to the active smart metasurface; Represents the user's transmit power matrix. , For the first Transmit power of each user terminal; This represents the thermal noise of an active intelligent metasurface; The signal transmitted from the user terminal to the base station via the active smart metasurface is represented as follows: in, This represents the channel matrix from the active smart metasurface to the base station. This indicates the noise level at the base station. The signal transmitted from the user terminal to the base station via the active smart metasurface, after adding a quantization noise model, is represented as follows: in, , It is the reciprocal of the signal-to-quantization-noise ratio; The signal after maximum ratio combining is represented as follows: in, , express The conjugate transpose of ; Base station The signal for each user is represented as: in, , Indicates the first Channel vectors from a user to an active smart metasurface; The expression for calculating the uplink rate of the kth user terminal is: In the formula, This represents the signal-to-interference-plus-noise ratio at the base station. Let be the uplink rate of the k-th user terminal; The uplink rate of the kth user terminal is approximately: in, , , , , ; The phase shift optimization problem of the active smart metasurface is solved using a genetic algorithm. The phase shift optimization problem is expressed as: 。

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