Intelligent metasurface-assisted channel state information acquisition method based on channel reshaping

Abstract

This invention discloses a method for acquiring channel state information using an intelligent metasurface-assisted channel reshaping, belonging to the field of wireless communications. This method combines the channel reshaping capabilities of an intelligent metasurface with the channel estimation process. By leveraging the powerful signal processing capabilities of a base station to extract channel parameters from an unknown channel environment that can be used to configure an intelligent metasurface to achieve channel sparsification, the method then uses these parameters to configure the intelligent metasurface so that a channel with abundant propagation paths can be represented by sparse propagation paths, significantly reducing the pilot overhead and complexity of downlink channel estimation.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method for acquiring channel state information assisted by an intelligent metasurface based on channel reshaping. Background Art

[0002] A complete wireless communication system typically consists of three major components: a transmitter, a wireless channel, and a receiver. Throughout the history of wireless communications, the evolution of wireless communication technology has primarily focused on transmitters and receivers, with only a small amount of research addressing wireless channel-oriented technologies. The joint design of transmitters, wireless channels, and receivers has yet to receive sufficient attention. Smart metasurfaces are a new type of electromagnetic surface composed of programmable two-dimensional electromagnetic metamaterials that can actively control electromagnetic waves in space in a programmable manner, forming electromagnetic fields with controllable amplitude, phase, polarization, and frequency. Introducing smart metasurfaces into wireless communication systems can overcome the inherent concept of random and uncontrollable wireless channels, enabling active and intelligent control of the wireless propagation environment. This addresses the channel gap in wireless system design and provides new insights for the development of a new generation of wireless communication technologies.

[0003] As a technology that can effectively and actively control channels, the application of smart metasurfaces in various wireless communication scenarios has attracted the attention of numerous researchers. Most research on wireless transmission using smart metasurfaces assumes perfect channel state information. However, low-cost smart metasurfaces are typically not equipped with components capable of digital signal processing. This makes channel estimation even more challenging in smart metasurface-assisted wireless communication systems. Smart metasurfaces divide the end-to-end channel between the base station and the user into base station-smart metasurface sub-channels and smart metasurface-user sub-channels. Because smart metasurfaces are typically equipped with a large number of array elements, the matrix dimensions of the sub-channels increase dramatically. Acquiring channel state information in smart metasurface-assisted wireless communication systems requires the user and base station to exchange a large number of pilot signals for channel training. Given that the time required to transmit a large number of pilot signals may exceed the channel coherence time, the channel state information may change before channel training is complete. This approach is difficult to implement in practical wireless communication systems. While previous work has proposed low-overhead smart metasurface-assisted channel state information estimation schemes and utilized the acquired channel state information to design wireless transmission schemes, these studies largely adhere to the design principles of traditional wireless communication systems, first estimating channel state information and then using it to configure the smart metasurface. This approach fails to consider the impact of changes in the smart metasurface configuration on the channel. Jointly designing a smart metasurface configuration scheme and a channel state information acquisition method is expected to significantly reduce the overhead and complexity of smart metasurface-assisted channel state information estimation by leveraging the effect of smart metasurface configuration on wireless channels. Summary of the Invention

[0004] In response to the problems of high complexity and high overhead in channel state information estimation in intelligent metasurface-assisted wireless communication systems, the present invention proposes an intelligent metasurface-assisted channel state information acquisition method based on channel reshaping. By utilizing the relationship between intelligent metasurface configuration and wireless channel characteristics, the intelligent metasurface configuration and channel estimation are jointly designed. By configuring the intelligent metasurface to reshape the wireless channel characteristics, the complexity and overhead of channel state information estimation are reduced.

[0005] An embodiment of the present invention provides a method for acquiring channel state information assisted by an intelligent metasurface based on channel reshaping, comprising the following steps:

[0006] Step S1: The user sends multiple sets of pilot signals to the base station, and performs uplink channel training in combination with the intelligent metasurface reflection coefficients switched at different time scales;

[0007] Step S2, using the base station to perform channel estimation to obtain uplink channel parameters of the intelligent metasurface-assisted wireless communication system;

[0008] Step S3, using the uplink channel parameters obtained by the base station to determine the specific configuration of the smart metasurface for shaping the sparse channel, and using them to configure the smart metasurface;

[0009] Step S4: Send a pilot signal to the user through the sparse channel pre-shaped by the base station, and perform downlink channel training using the intelligent metasurface reflection coefficient switched at different time scales according to the user's signal processing capability;

[0010] Step S5: Using the user to perform channel estimation, obtain downlink channel parameters of the sparse channel reshaped by the intelligent metasurface.

[0011] Optionally, in one embodiment of the present invention, step S1 specifically includes:

[0012] The user sends multiple sets of pilot signals to the base station through a wireless channel with a completely unknown scattering environment. Each set of pilot signals contains multiple pilot symbols. For each set of pilot signals sent by the user, the smart metasurface switches a different basic reflection coefficient for each smart metasurface unit and keeps the basic reflection coefficient unchanged on the time scale of the same set of pilot signals; for each pilot symbol sent by the user, the smart metasurface switches the same set of additional reflection coefficients for all smart metasurface units within the pilot symbol time.

[0013] Optionally, in one embodiment of the present invention, step S2 specifically includes:

[0014] Based on the multiple sets of pilot signals received, the base station uses the additional reflection coefficient of the smart metasurface within the pilot symbol time scale to process the received pilot signals, decouple the channel components provided by different smart metasurfaces, and use the parameter extraction algorithm to estimate the channel parameters to complete the uplink channel estimation.

[0015] Optionally, in one embodiment of the present invention, step S3 specifically includes:

[0016] The base station selects the channel paths with the strongest link quality from the smart metasurface to the base station channel and the smart metasurface to the user channel, respectively, and configures the smart metasurface using the parameters of the path with the strongest link quality to enhance the link quality of the channel path with the strongest link quality, approximating the channel to a sparse form dominated by the paths with enhanced link quality.

[0017] Optionally, in one embodiment of the present invention, step S4 specifically includes:

[0018] The base station sends a pilot signal to the user through a pre-shaped sparse channel. When the user has the ability to receive and process multiple signals within the same pilot symbol, the base station sends a group of pilots, each group of pilots contains multiple pilot symbols, and the smart metasurface switches the same group of additional reflection coefficients for all smart metasurface units within the pilot symbol time; when the user does not have the ability to receive and process multiple signals within the same pilot symbol, the base station sends multiple groups of the same pilot signals, and the smart metasurface switches different additional reflection coefficients for all smart metasurface units on the time scale of each group of pilot signals.

[0019] Optionally, in one embodiment of the present invention, step S5 specifically includes:

[0020] Based on the received pilot signal, the user uses the additional reflection coefficients of the smart metasurface at different time scales to process the received pilot signal, decouple the channel components provided by different smart metasurfaces, and then simplify the complex multipath downlink channel estimation into several independent single-path channel parameter detection sub-problems based on the pre-shaped sparse channel. The sparse channel parameters are estimated using a parameter extraction algorithm to complete the downlink channel estimation.

[0021] The intelligent metasurface-assisted channel state information acquisition method based on channel reshaping in an embodiment of the present invention combines the channel reshaping capability of the intelligent metasurface with the channel estimation process, and utilizes the powerful signal processing capability of the base station to extract channel parameters that can be used to configure the intelligent metasurface to sparsify the channel from an unknown channel environment. After using the parameters to configure the intelligent metasurface, the channel with rich propagation paths is sparsely distributed, which can significantly reduce the pilot overhead and channel estimation complexity of the downlink channel estimation.

[0022] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0024] Figure 1 This is a flow chart of a method for acquiring channel state information assisted by an intelligent metasurface based on channel reshaping provided by an embodiment of the present invention;

[0025] Figure 2 Schematic diagram of channel path propagation according to an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of configuring the intelligent metasurface reflection coefficient vector when the user sends a pilot signal in step S1 of an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0028] Figure 1 This is a flowchart of a method for acquiring channel state information assisted by an intelligent metasurface based on channel reshaping provided according to an embodiment of the present invention.

[0029] like Figure 1 As shown, the intelligent metasurface-assisted channel state information acquisition method based on channel reshaping includes the following steps:

[0030] In step S1, the user sends multiple groups of pilot signals to the base station, and performs uplink channel training in combination with the intelligent metasurface reflection coefficients switched at different time scales.

[0031] Specifically, a user sends multiple sets of pilot signals to the base station over a completely unknown wireless channel with a completely unknown scattering environment. Each pilot signal contains multiple pilot symbols. For each pilot signal, the smart metasurface switches to a different base reflection coefficient for each smart metasurface unit, while maintaining the base reflection coefficient unchanged over the time scale of the same pilot signal. For each pilot symbol sent by the user, the smart metasurface switches to the same set of additional reflection coefficients for all smart metasurface units within the pilot symbol time.

[0032] Step S2: Use the base station to perform channel estimation to obtain uplink channel parameters of the intelligent metasurface-assisted wireless communication system.

[0033] Specifically, the base station processes the received signals based on the multiple sets of pilot signals received and uses the additional reflection coefficient of the smart metasurface within the pilot symbol time scale to decouple the channel components provided by different smart metasurfaces, and uses a parameter extraction algorithm to estimate the channel parameters to complete the uplink channel estimation.

[0034] Step S3: The uplink channel parameters acquired by the base station are used to determine the specific configuration of the intelligent metasurface for shaping the sparse channel, and are used to configure the intelligent metasurface.

[0035] Specifically, the base station selects the channel path with the strongest link quality from the smart metasurface to the base station channel and the smart metasurface to the user channel, respectively, and uses the parameters of the path with the strongest link quality to configure the smart metasurface to further enhance the link quality of the path, approximating the channel to a sparse form dominated by the path with enhanced link quality.

[0036] Step S4: Send a pilot signal to the user through the sparse channel pre-shaped by the base station, and perform downlink channel training using the intelligent metasurface reflection coefficient switched at different time scales according to the user's signal processing capability.

[0037] Specifically, the base station sends a pilot signal to the user through a pre-shaped sparse channel. When the user has the ability to receive and process multiple signals within the same pilot symbol, the base station sends a group of pilots, each group of pilots contains multiple pilot symbols, and the smart metasurface switches the same group of additional reflection coefficients for all smart metasurface units within the pilot symbol time; when the user does not have the ability to receive and process multiple signals within the same pilot symbol, the base station sends multiple groups of the same pilot signals, and the smart metasurface switches different additional reflection coefficients for all smart metasurface units on the time scale of each group of pilot signals.

[0038] Step S5: Using the user to perform channel estimation, obtain downlink channel parameters of the sparse channel reshaped by the intelligent metasurface.

[0039] Specifically, the user processes the received signal based on the received pilot signal using the additional reflection coefficients of the smart metasurface at different time scales, decouples the channel components provided by different smart metasurfaces, and then simplifies the complex multipath downlink channel estimation into several independent single-path channel parameter detection sub-problems based on the pre-shaped sparse channel. The sparse channel parameters are estimated using a parameter extraction algorithm to complete the downlink channel estimation.

[0040] The following describes in detail the method for obtaining channel state information assisted by an intelligent metasurface based on channel reshaping of the present invention in conjunction with the accompanying drawings and specific embodiments.

[0041] like Figure 2As shown in the figure, the line-of-sight transmission link between the base station and the user is blocked by obstacles such as tall buildings, but due to the presence of surrounding scatterers, there is still a weak direct transmission link. In order to improve the quality of wireless transmission, K reflective or transmissive smart metasurfaces are arranged between the base station and the user to establish a reflective transmission link. The base station has N b antennas, the user has N u antennas, the smart metasurface k has M k The channel H between the base station and the user, which contains K smart metasurfaces, can be expressed as:

[0042]

[0043] Where ρ0 is the large-scale fading factor of the direct link including penetration loss, Η0 represents the direct channel between the user and the base station, Indicates the summation operation of the variables in the brackets from k = 1 to k = K, ρ k is the large-scale fading factor of the smart metasurface cascade link, diag(γ k ) represents the vector γ k The resulting diagonal matrix, vector Represents the reflection coefficient vector of the smart metasurface k, where e is a natural constant, j is an imaginary unit, and γ k,m represents the reflection phase of the mth unit in the smart metasurface k, H rb,k is the channel from the base station to the smart metasurface k, H ur,k is the channel from user to smart metasurface k. Under the finite channel model, H rb,k It can be expressed as:

[0044]

[0045] in, Indicates the number of non-line-of-sight paths of the channel, is the path gain of the lth path from the smart metasurface k to the base station, l = 0 represents the line-of-sight path, a b (·) represents the array response vector of the uniform linear array at the base station end, represents the AoA (angle-of-arrival) of the lth path at the base station end, a r,k (·,·) represents the array response vector of the uniform planar array at position k of the smart metasurface, (·) H represents the conjugate transpose operation, and They represent the horizontal AoD (angle-of-departure) and vertical AoD of the lth path at the smart metasurface k. Similarly, under the finite channel model, H ur,k It can be expressed as:

[0046]

[0047] in, Indicates the number of non-line-of-sight paths of the channel, is the path gain of the lth path from the user to the smart metasurface k, l = 0 represents the line-of-sight path, a r,k (·,·) represents the array response vector of the uniform planar array at k on the smart metasurface, and denote the horizontal AoA and vertical AoA of the lth path at the smart metasurface k, respectively. u (·) represents the array response vector of the user-side uniform linear array, Indicates the AoD of the lth path on the user side.

[0048] The channel reshaping-based intelligent metasurface-assisted channel state information acquisition method of the embodiment of the present invention utilizes the powerful signal processing capabilities of the base station to complete the acquisition of uplink channel parameters. Then, the intelligent metasurface is used to reshape the channel with rich propagation paths into a sparse propagation channel, thereby reducing the pilot overhead and channel estimation complexity of the downlink channel estimation. Specifically, it includes the following steps:

[0049] Step S1: The user sends multiple sets of pilot signals to the base station through a wireless channel with a completely unknown scattering environment, and trains the reflection coefficient vector of the smart metasurface in the uplink. Figure 3 As shown, during the uplink training process, each smart metasurface switches K s Different basic reflection coefficient vectors. Taking the kth smart metasurface as an example, in each basic reflection coefficient vector (γ k,j ,j=1,...,K S ) duration, the user sends a set of N u pilot signal of pilot symbols When the user sends each pilot symbol in each group, the smart metasurface switches a set of additional reflection coefficients for all smart metasurface units within the pilot symbol time. where K F is the number of additional reflection coefficients.

[0050] Step S2: A preferred solution for this step is that the uplink channel parameters obtained in this embodiment are the angle parameters of the line-of-sight path. Since the positions of the base station and the smart metasurface k are known, the angle parameters of the line-of-sight path between the base station and the smart metasurface k can be calculated using a geometric algorithm. The channel between the smart metasurface k and the user uses a positioning-based parameter extraction algorithm. For the sth basic reflection coefficient vector, the pth pilot symbol, and the vth additional reflection coefficient, the channel can be expressed as:

[0051]

[0052] The received signal of the base station can be expressed as:

[0053]

[0054] Among them, n s,p,v Represents the corresponding noise. F The signals are stacked together and expressed as Define the matrix F = [f0,f1,...,f K ], where f0 is K F A vector of all 1s in row and column. is the additional reflection coefficient vector of the kth smart metasurface. When F is set to the DFT matrix, Multiply right by the conjugate matrix F of F * After that, list its column vectors separately, that is:

[0055]

[0056] at this time, It only contains the pilot signal sent by the user to the base station through the direct transmission link. The system only includes the pilot signal sent by the user to the base station via the kth smart metasurface reflective transmission link, meaning that the signals of different channel components are decoupled. Based on this signal decoupling, the Newton-normal matching pursuit parameter estimation algorithm is used to extract the channel path parameters from the different channel components. Based on this, the line-of-sight path parameters are jointly determined from the extracted path parameters of each channel component using geometric positioning methods.

[0057] Step S3: A preferred solution of this step is that, in the scenario of step 2 of this embodiment, the channel components between the base station and the smart metasurface k (k=1,...,K) and between the smart metasurface k and the user are both strongest in the line-of-sight path, and the reflection coefficient vector of the smart metasurface k is determined using the estimated line-of-sight path parameters as follows: in and are the horizontal and vertical AoD of the base station-smart metasurface k channel line of sight calculated in step 2 according to the positions of the base station and the smart metasurface k, and These are the line-of-sight paths (AoAs) of the k-user channels on the smart metasurface, determined from the extracted path parameters using geometric positioning methods in step 2. In this configuration, the array gain provided by the smart metasurface further enhances the line-of-sight paths, allowing the channels between the base station and users to be approximately represented by these enhanced line-of-sight paths, completing the sparse reconstruction of the channel.

[0058] Step S4: The base station sends a pilot signal to the user through a pre-shaped sparse channel to train the reflection coefficient vector of the smart metasurface in the downlink. When the user has the ability to receive and process multiple signals in the same pilot symbol, the base station sends a group of pilots, each group of pilots contains N u pilot symbols, the smart metasurface switches the same set of additional reflection coefficients for all smart metasurface units multiple times within each pilot symbol time, where a total of K F When the user does not have the ability to receive and process multiple signals in the same pilot symbol, the base station sends K s For a group of identical pilot signals, the smart metasurface switches different additional reflection coefficients for all smart metasurface units on the time scale of each group of pilot signals.

[0059] Step S5: The user processes the received signal based on the received pilot signal using the additional reflection coefficients of the smart metasurface at different time scales, decouples the channel components provided by different smart metasurfaces, and simplifies the complex multipath downlink channel estimation into several independent single-path channel parameter detection sub-problems based on the sparse channel reshaped in step S3. The sparse channel parameters are estimated using a parameter extraction algorithm to complete the downlink channel estimation.

[0060] According to the intelligent metasurface-assisted channel state information acquisition method based on channel reshaping proposed in an embodiment of the present invention, the ability of the intelligent metasurface to reshape the channel is combined with the channel estimation process. The powerful signal processing capability of the base station is used to extract channel parameters that can be used to configure the intelligent metasurface to sparse the channel from an unknown channel environment. After using the parameters to configure the intelligent metasurface, the channel with rich propagation paths is sparsed, which can significantly reduce the pilot overhead and channel estimation complexity of the downlink channel estimation.

[0061] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0062] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0063] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

Claims

1. A method for acquiring channel state information assisted by an intelligent metasurface based on channel reshaping, characterized in that: The following steps are involved: Step S1: The user sends multiple sets of pilot signals to the base station, and performs uplink channel training in combination with the intelligent metasurface reflection coefficients switched at different time scales; Step S2, using the base station to perform channel estimation to obtain uplink channel parameters of the intelligent metasurface-assisted wireless communication system; Step S3, using the uplink channel parameters obtained by the base station to determine the specific configuration of the smart metasurface for shaping the sparse channel, and using it to configure the smart metasurface; specifically, the base station selects the channel path with the strongest link quality from the smart metasurface to the base station channel and the smart metasurface to the user channel, respectively, and configures the smart metasurface using the parameters of the path with the strongest link quality to enhance the link quality of the channel path with the strongest link quality, thereby completing the sparse reshaping of the channel; Step S4: Send a pilot signal to the user through the sparse channel pre-shaped by the base station, and perform downlink channel training using the intelligent metasurface reflection coefficient switched at different time scales according to the user's signal processing capability; In step S5, the user performs channel estimation to obtain the downlink channel parameters of the sparse channel reshaped by the smart metasurface; specifically, the user processes the received pilot signal based on the received pilot signal using the additional reflection coefficients of the smart metasurface at different time scales, decouples the channel components provided by different smart metasurfaces, and then simplifies the complex multipath downlink channel estimation into an independent single-path channel parameter detection sub-problem based on the pre-shaped sparse channel, and uses a parameter extraction algorithm to estimate the sparse channel parameters to complete the downlink channel estimation.

2. The method according to claim 1, characterized in that Step S1 specifically includes: The user sends multiple sets of pilot signals to the base station through a wireless channel with a completely unknown scattering environment. Each set of pilot signals contains multiple pilot symbols. For each set of pilot signals sent by the user, the smart metasurface switches a different basic reflection coefficient for each smart metasurface unit and keeps the basic reflection coefficient unchanged on the time scale of the same set of pilot signals; for each pilot symbol sent by the user, the smart metasurface switches the same set of additional reflection coefficients for all smart metasurface units within the pilot symbol time.

3. The method according to claim 1, characterized in that Step S2 specifically includes: Based on the multiple sets of pilot signals received, the base station uses the additional reflection coefficient of the smart metasurface within the pilot symbol time scale to process the received pilot signals, decouple the channel components provided by different smart metasurfaces, and use the parameter extraction algorithm to estimate the channel parameters to complete the uplink channel estimation.

4. The method according to claim 1, wherein Step S4 specifically includes: The base station sends a pilot signal to the user through a pre-shaped sparse channel. When the user has the ability to receive and process multiple signals within the same pilot symbol, the base station sends a group of pilots, each group of pilots contains multiple pilot symbols, and the smart metasurface switches the same group of additional reflection coefficients for all smart metasurface units within the pilot symbol time; when the user does not have the ability to receive and process multiple signals within the same pilot symbol, the base station sends multiple groups of the same pilot signals, and the smart metasurface switches different additional reflection coefficients for all smart metasurface units on the time scale of each group of pilot signals.

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