A Limited Channel Feedback Method for Intelligent Metasurface-Assisted Wireless Communication

Abstract

The present invention discloses a limited feedback method for intelligent metasurface-assisted wireless communication. The method includes: a user performs channel estimation to obtain the downlink channel path parameters of the intelligent metasurface-assisted wireless communication system; the user determines the specific configuration of the intelligent metasurface that can shape the sparse channel according to the obtained channel path parameters; the user feeds back the sparse channel path parameters related to the specific configuration of the intelligent metasurface through the uplink feedback link; the base station receives the sparse channel path parameters fed back by the user through the uplink feedback link; the base station configures the intelligent metasurface by using the sparse channel path parameters and reconstructs the downlink end-to-end channel matrix. By configuring the intelligent metasurface, the present invention shapes the channel with rich propagation paths into a sparse propagation channel, enabling the channel to be approximately represented by sparse propagation paths, and reducing the amount of user feedback channel parameters required for the base station to reconstruct the downlink channel.

Description

Technical Field

[0001] The present invention provides a limited channel feedback method for intelligent metasurface-assisted wireless communication, belonging to the technical field of wireless communication. Background Art

[0002] In the field of wireless communication, a complete wireless communication system generally includes a transmitter, a channel, and a receiver. Since the channel is generally regarded as an uncontrollable factor, the design of wireless communication systems mainly focuses on the transmitter and the receiver. The uncontrollability of the channel makes the design of wireless communication systems lack the degree of freedom at the channel level. Intelligent metasurface is a technology composed of programmable two-dimensional electromagnetic metamaterials, which can actively control spatial electromagnetic waves in a programmable manner to form an electromagnetic field with controllable amplitude, phase, polarization, and frequency. Introducing intelligent metasurface into a wireless communication system can break the inherent concept of the traditional random and uncontrollable wireless channel, realize active and intelligent control of the wireless propagation environment, fill the gap in the channel level of wireless system design, and provide new ideas for the development of a new generation of wireless communication technologies.

[0003] As a technology capable of effectively regulating channels, the application research of intelligent metasurfaces in various wireless communication scenarios has attracted the attention of numerous researchers. Channel state information is one of the key factors for the successful application of intelligent metasurfaces. In a frequency-division duplex (FDD) system, since the uplink and downlink channels do not have reciprocity, the base station must obtain the downlink channel state information through the channel feedback of users. Although channel feedback has been widely studied in current wireless communication systems, most of these research works are dedicated to feedback of the end-to-end channel matrix between the base station and users, without considering the changes brought by intelligent metasurfaces to the channel. In an intelligent metasurface-assisted wireless communication system, the end-to-end channel between the base station and users includes the intelligent metasurface. Therefore, once the configuration of the intelligent metasurface changes, the end-to-end channel will change accordingly. Generally speaking, designing the specific configuration of the intelligent metasurface requires the use of channel state information, while the end-to-end channel state information is affected by the specific configuration of the intelligent metasurface. Therefore, the research works dedicated to feedback of the end-to-end channel matrix between the base station and users cannot be directly applied to the intelligent metasurface-assisted wireless communication system. As an intermediate node between the base station and users, the intelligent metasurface divides the end-to-end channel between the base station and users into a base station-intelligent metasurface sub-channel and an intelligent metasurface-users sub-channel. Although separately feedbacking the base station-intelligent metasurface sub-channel and the intelligent metasurface-users sub-channel can solve the coupling problem between the intelligent metasurface and the end-to-end channel, this scheme is not feasible in an actual wireless communication system. The reason is that the intelligent metasurface usually has a large number of array elements, resulting in a sharp increase in the matrix dimension of the sub-channel, and the limited feedback ability of users is not sufficient to support the feedback overhead of the ultra-large dimension matrix. A feasible solution is to model the channel into a finite-ray propagation model, and the user feeds back the channel path parameters, and then the base station reconstructs the channel matrix according to the path parameters. Summary of the Invention

[0004] Object of the Invention: Aiming at the phenomenon of limited user feedback ability in the frequency-division duplex system, in order to enable users to feedback as accurate channel state information as possible with limited feedback ability, the present invention provides a limited channel feedback method for intelligent metasurface-assisted wireless communication, which shapes a sparse channel propagation environment through the intelligent metasurface, thereby reducing the feedback overhead required for feedbacking channel state information.

[0005] Technical Solution: To achieve the above object of the invention, a limited channel feedback method for intelligent metasurface-assisted wireless communication of the present invention adopts the following steps:

[0006] Step 1: The user performs channel estimation to obtain the downlink channel path parameters of the intelligent metasurface-assisted wireless communication system;

[0007] Step 2: The user determines the specific configuration of the intelligent metasurface that can shape a sparse channel according to the obtained channel path parameters;

[0008] Step 3: The user feeds back the sparse channel path parameters related to the specific configuration of the intelligent metasurface through the uplink feedback link;

[0009] Step 4: The base station receives the sparse channel path parameters fed back by the user through the uplink feedback link;

[0010] Step 5: The base station configures the intelligent metasurface using the sparse channel path parameters and reconstructs the downlink end-to-end channel matrix.

[0011] Specifically, Step 1 is as follows: The user sequentially estimates the path parameters cascaded by the intelligent metasurface using a channel parameter estimation algorithm. If the maximum value that the energy of the first cascaded path can reach after the configuration of the intelligent metasurface is greater than or equal to τ times the maximum value that the energy of the currently estimated cascaded path can reach after the configuration of the intelligent metasurface, the user stops estimating the remaining path parameters cascaded by the intelligent metasurface.

[0012] Specifically, Step 2 is as follows: Under the requirement of limited feedback parameter number, the user designs the specific configuration of the intelligent metasurface that can shape the sparse channel with the principle of maximizing the sum of cascaded path energies and determines the sparse channel path parameters required for this configuration.

[0013] Specifically, Step 3 is as follows: Under the requirement of limited total feedback bits, with the principle of minimizing the system performance caused by limited quantization feedback, the user allocates feedback bits to each sparse channel path parameter required for the specific configuration of the intelligent metasurface that can shape the sparse channel, and quantizes it within its value range and then feeds it back to the base station in the form of a transmitted signal through the uplink feedback link.

[0014] Specifically, Step 4 is as follows: The base station receives the signal transmitted by the user through the uplink feedback link and obtains the quantized sparse path parameters fed back by the user.

[0015] Specifically, Step 5 is as follows: The base station configures the intelligent metasurface with the obtained quantized sparse path parameters with the principle of maximizing the sum of cascaded path energies, obtains the intelligent metasurface response matrix, and further reconstructs the downlink end-to-end channel matrix according to the obtained quantized sparse path parameters and the intelligent metasurface response matrix.

[0016] Beneficial effects: By utilizing the ability of the intelligent metasurface to reshape the channel, the present invention feeds back limited path parameter information for configuring the intelligent metasurface after the channel estimation is completed, enabling the base station to reconstruct an accurate downlink channel using the limited path parameter information and reducing the user feedback amount required for the base station to reconstruct the downlink channel. Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the channel path propagation of an embodiment of the method of the present invention.

[0018] Figure 2 It is the flowchart of a method embodiment of the present invention.

[0019] Figure 3 It is the cumulative distribution function graph of the ratio of the sum of the energies of the selected cascade paths to the sum of the total path energies in a method embodiment of the present invention before and after configuring the intelligent metasurface. Detailed implementation manners

[0020] The present invention will be further clarified below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to explain the present invention and are convenient for understanding rather than used to limit the scope of the present invention. After reading the present invention, various equivalent modifications made by those skilled in the art to the present invention all fall within the scope defined by the appended claims of this application.

[0021] To facilitate the understanding of the content of the present invention, the related knowledge of the prior art such as the channel model background involved in the present invention will be introduced below. It should be noted that the method of the present invention is not limited to the specific formula expression form below.

[0022] A limited channel feedback method for intelligent metasurface-assisted wireless communication according to the present invention includes the following steps:

[0023] Step 1: The user performs channel estimation to obtain the downlink channel path parameters of the intelligent metasurface-assisted wireless communication system;

[0024] Step 2: The user determines the specific configuration of the intelligent metasurface that can shape the sparse channel according to the obtained channel path parameters;

[0025] Step 3: The user feeds back the sparse channel path parameters related to the specific configuration of the intelligent metasurface through the uplink feedback link;

[0026] Step 4: The base station receives the sparse channel path parameters fed back by the user through the uplink feedback link;

[0027] Step 5: The base station configures the intelligent metasurface using the sparse channel path parameters and reconstructs the downlink end-to-end channel matrix.

[0028] As Figure 1 shown, the direct propagation channel between the base station and the user is blocked by obstacles and cannot carry data transmission services. The base station and the user establish a data transmission link through the reflection or transmission of K intelligent metasurfaces. The base station has N T antennas, the user has N R antennas, and the intelligent metasurface k with N S,k units is at a distance of d T,k from the base station and at a distance of d k,R from the user. The channel H cascaded by K intelligent metasurfaces between the base station and the user can be expressed as

[0029]

[0030] Among them, is the array response matrix for the signal to reach the user through all the cascaded paths of the intelligent metasurfaces, and a R (·) represents the antenna array response vector of the user, (k = 1,..., K; l k,R = 1,..., L k,R ) represents the angle-of-arrival (AoA) of the l-th path from the k-th intelligent metasurface to the user at the user's antenna array, ∈ represents the symbol of belonging, k,R represents the complex space of dimension N ×L R ×L R is the number of paths from the k-th intelligent metasurface to the user, k,R is the total number of paths from the K intelligent metasurfaces to the user, is the total number of paths from the K intelligent metasurfaces to the user, denotes the summation operation of the variables inside the parentheses from k = 1 to k = K; is the array response matrix for the signal to leave the base station through all the propagation paths, (·) H denotes the conjugate transpose operation, a T (·) represents the antenna array response vector of the base station, (k = 1,..., K; l T,k = 1,..., L T,k ) represents the angle-of-departure (AoD) of the l-th path from the base station to the k-th intelligent metasurface at the base station antenna array, L T,k is the number of paths from the base station to the k-th intelligent metasurface, T,k is the total number of paths from the base station to the K intelligent metasurfaces; is the total number of paths from the base station to the K intelligent metasurfaces; denotes the block diagonal matrix composed of the matrices {Ξ 1 ,..., Ξ K}, where (k = 1,..., K) is the energy matrix of all the cascaded paths passing through the k-th intelligent metasurface, and the element in its l-th row and j-th column can be expressed as (Ξ k ) l,j = ξ k,l,j , where the absolute value |ξ k,l,j | represents the cascaded path energy from the j-th incident propagation path to the l-th outgoing propagation path on the k-th intelligent metasurface, ρ k represents the large-scale path loss coefficient for the signal to reach the user terminal from the base station through the k-th intelligent metasurface, α k,R,lDenote the small-scale fading coefficient when the \(l\)-th propagation path of the intelligent reflecting surface \(k\) reaches the user, \(\alpha\) T,k,j Denote the small-scale fading coefficient when the \(j\)-th path of the intelligent reflecting surface \(k\) leaves the base station, \(\rho\) k \(\alpha\) k,R,l \(\alpha\) T,k,j Denote the cascaded path gain from the \(j\)-th incident propagation path to the \(l\)-th outgoing propagation path on the intelligent reflecting surface \(k\), \(a\) S,k (·) represents the array response vector of the \(k\)-th intelligent reflecting surface, \(\Gamma\) k Denote the response matrix of the intelligent reflecting surface \(k\) to electromagnetic waves Denote the AoD of the \(l\)-th path from the intelligent reflecting surface \(k\) to the user at the array of the intelligent reflecting surface \(k\) Denote the AoA of the \(j\)-th path from the base station to the intelligent reflecting surface \(k\) at the array of the intelligent reflecting surface \(k\).

[0031] Based on the above technical background description, as Figure 2 shown, a limited feedback method for intelligent reflecting surface-assisted wireless communication disclosed in an embodiment of the present invention shapes a channel with rich propagation paths into a sparse propagation channel through an intelligent reflecting surface, thereby reducing the number of parameters required for channel feedback. The method mainly includes the following steps: First, the user performs channel estimation to obtain the downlink channel path parameters of the intelligent reflecting surface-assisted wireless communication system; then, the user determines the specific configuration of the intelligent reflecting surface that can shape the sparse channel according to the obtained channel path parameters; secondly, the user feeds back the sparse channel path parameters related to the specific configuration of the intelligent reflecting surface through the uplink feedback link; then, the base station receives the sparse channel path parameters fed back by the user through the uplink feedback link; finally, the base station configures the intelligent reflecting surface using the sparse channel path parameters and reconstructs the downlink end-to-end channel matrix. The method specifically includes the following steps:

[0032] Step S1: The user performs channel estimation to obtain the downlink channel path parameters of the intelligent reflecting surface-assisted wireless communication system. The user sequentially estimates the path parameters of the cascaded intelligent reflecting surface using a channel parameter estimation algorithm. If the maximum value that the energy of the first cascaded path can reach after the intelligent reflecting surface configuration is greater than or equal to \(\tau\) times the maximum value that the energy of the currently estimated \(t\)-th cascaded path can reach after the intelligent reflecting surface configuration , the user stops estimating the remaining path parameters of the cascaded intelligent reflecting surface.

[0033] Step S2: The user determines the specific configuration of the intelligent reflecting surface that can shape the sparse channel according to the obtained channel path parameters. Assume that under the requirement of limited feedback parameter number, the base station can only feedback the parameters of \(L\) F cascaded paths. The user selects \(L\) F cascaded paths from the \(t\) paths with the principle of maximizing the sum of the cascaded path energies, and calculates to obtain the selected \(L\)F The specific configuration of the intelligent metasurface that maximizes the sum of the energies of the L cascaded path and the channel path parameters required for this configuration. After configuring the intelligent metasurface using the obtained specific configuration of the intelligent metasurface, as Figure 3 shown, the ratio of the sum of the energies of the selected L F cascaded paths to the sum of the energies of all paths is almost 1, that is, the channel with L T L R cascaded paths can be approximately sparsely represented by the selected L F cascaded paths.

[0034] Step S3: The user feeds back the sparse channel path parameters related to the specific configuration of the intelligent metasurface through the uplink feedback link. Using the specific configuration of the intelligent metasurface obtained in Step S2, the channel can be approximately sparsely represented by the selected L F cascaded paths. Therefore, the user only needs to feed back the parameters of the selected L F cascaded paths related to the specific configuration of the intelligent metasurface. Compared with feeding back the parameters of all L T L R cascaded paths, the present invention uses the parameters of the selected L F cascaded paths to configure the intelligent metasurface to shape a sparse channel, so that the feedback overhead is reduced from L T L R to L F . Under the requirement of limited total feedback bits for the user, with the principle of minimizing the system performance due to limited feedback, the user allocates feedback bits to each parameter of the selected L F cascaded paths for quantization within its value range and then feeds it back to the base station in the form of a transmitted signal through the uplink feedback link.

[0035] Step S4: The base station receives the signal transmitted by the user through the uplink feedback link and obtains the quantized parameters of the L F cascaded paths fed back by the user.

[0036] Step S5: The base station configures the intelligent metasurface using the parameters of the L F cascaded paths and reconstructs the downlink end-to-end channel matrix. The base station uses the quantized parameters of the L F cascaded paths obtained in Step S4 to configure the intelligent metasurface with the principle of maximizing the sum of the energies of the cascaded paths to obtain the intelligent metasurface response matrix, and further reconstructs the downlink end-to-end channel matrix according to the obtained quantized parameters of the L F cascaded paths and the intelligent metasurface response matrix.

Claims

1. A finite channel feedback method for intelligent metasurface-assisted wireless communication, characterized in that, the feedback method comprises the following steps: Step 1: The user performs channel estimation to obtain the downlink channel path parameters of the intelligent metasurface-assisted wireless communication system; Step 2: The user determines the specific configuration of the intelligent metasurface that can shape the sparse channel according to the obtained channel path parameters; Step 3: The user feeds back the sparse channel path parameters related to the specific configuration of the intelligent metasurface through the uplink feedback link; Step 4: The base station receives the sparse channel path parameters fed back by the user through the uplink feedback link; Step 5: The base station configures the intelligent metasurface using the sparse channel path parameters and reconstructs the downlink end-to-end channel matrix; The specific content of Step 2 is: Under the requirement of limited number of feedback parameters, the user designs the specific configuration of the intelligent metasurface that can shape the sparse channel with the principle of maximizing the sum of cascaded path energies and determines the sparse channel path parameters required for this configuration; The specific content of Step 5 is: The base station configures the intelligent metasurface with the principle of maximizing the sum of cascaded path energies using the obtained quantized sparse path parameters, obtains the intelligent metasurface response matrix, and further reconstructs the downlink end-to-end channel matrix according to the obtained quantized sparse path parameters and the intelligent metasurface response matrix.

2. The finite channel feedback method for intelligent metasurface-assisted wireless communication according to claim 1, characterized in that, the specific content of Step 1 is: The user sequentially estimates the path parameters cascaded by the intelligent metasurface using a channel parameter estimation algorithm. If the maximum value that the energy of the first cascaded path can reach after the intelligent metasurface configuration is greater than or equal to τ times the maximum value that the energy of the currently estimated cascaded path can reach after the intelligent metasurface configuration, the user stops estimating the remaining path parameters cascaded by the intelligent metasurface.

3. The finite channel feedback method for intelligent metasurface-assisted wireless communication according to claim 1, characterized in that, the specific content of Step 3 is: Under the requirement of limited total feedback bits, with the principle of minimizing the system performance caused by finite quantization feedback, the user allocates feedback bits to each sparse channel path parameter required for the specific configuration of the intelligent metasurface that can shape the sparse channel, and quantizes it within the value range and feeds it back to the base station in the form of a transmitted signal through the uplink feedback link.

4. The finite channel feedback method for intelligent metasurface-assisted wireless communication according to claim 1, characterized in that, the specific content of Step 4 is: The base station receives the signal transmitted by the user through the uplink feedback link and obtains the quantized sparse path parameters fed back by the user.

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