A channel estimation method, device, electronic equipment, storage medium and product

Abstract

The application discloses a channel estimation method and device, electronic equipment, storage medium and product, and relates to the technical field of communication. The channel estimation method comprises the following steps: determining the reflection times of each channel link in a communication system through an intelligent reflecting surface; determining effective receiving signals of each channel link in turn according to the reflection times, and determining channel estimation results of each channel link by using a compressed sensing algorithm based on the effective receiving signals; and determining the channel estimation results of the communication system according to the channel estimation results of each channel link. According to the embodiment of the application, the effective receiving signals of each channel link are determined in turn according to the reflection times, and then the channel estimation results of each channel link are determined by using the compressed sensing algorithm based on the effective receiving signals, so that the channel estimation results of the communication system are determined, the channel estimation of any multi-RIS scene is realized, the modeling mode of the channel estimation does not need to be adjusted, and the efficiency of the channel estimation of the communication system is improved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a channel estimation method, apparatus, electronic device, storage medium, and product. Background Technology

[0002] With the development of sixth-generation mobile communication technology (6G), reconfigurable intelligent surfaces (RIS) can effectively enhance the strength of received signals when the line of sight (LOS) is blocked. Besides providing performance gains to communication systems, RIS also consumes very little power, employing a passive reflection method that aligns with the principles of green communication.

[0003] Millimeter-wave information transmission requires accurate channel state information (CSI), necessitating channel estimation. However, the addition of a cascaded multi-RIS (Risk-Related Irregularities) channel complicates the process, increasing the difficulty of channel estimation. Most existing methods are single-RIS channel estimation approaches, often neglecting multi-hop paths within the RIS and thus unsuitable for arbitrary multi-RIS scenarios. Therefore, how to perform channel estimation for complex multi-RIS cascaded channels has become a pressing issue. Summary of the Invention

[0004] This invention provides a channel estimation method, apparatus, electronic device, storage medium, and product to solve the problem that the prior art cannot perform channel estimation for complex multi-RIS cascaded channels.

[0005] According to one aspect of the present invention, a channel estimation method is provided, wherein the method includes:

[0006] Determine the number of times each channel link in the communication system is reflected by the intelligent reflective surface;

[0007] The effective received signal of each channel link is determined sequentially based on the number of reflections, and the channel estimation result of each channel link is determined based on the effective received signal using a compressed sensing algorithm;

[0008] The channel estimation result of the communication system is determined based on the channel estimation results of each of the aforementioned channel links.

[0009] According to another aspect of the present invention, a channel estimation apparatus is provided, wherein the apparatus comprises:

[0010] The reflection count determination module is used to determine the number of reflections of each channel link in the communication system through the intelligent reflective surface;

[0011] The channel information determination module is used to determine the effective received signal of each channel link in sequence according to the number of reflections, and to determine the channel estimation result of each channel link based on the effective received signal using a compressed sensing algorithm;

[0012] The channel estimation determination module is used to determine the channel estimation result of the communication system based on the channel estimation results of each of the channel links.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the channel estimation method according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the channel estimation method according to any embodiment of the present invention.

[0016] According to another aspect of the present invention, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the channel estimation method of any embodiment of the present invention.

[0017] The technical solution of this invention determines the number of reflections of each channel link in the communication system through the intelligent reflective surface, then determines the effective received signal of each channel link according to the number of reflections, and uses a compressed sensing algorithm to determine the channel estimation result of each channel link based on the effective received signal. The channel estimation result of the communication system is determined according to the channel estimation result of each channel link, thereby realizing channel estimation of any multi-RIS scenario communication system without adjusting the channel estimation modeling method, thus improving the efficiency of communication system channel estimation.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a communication scenario provided by an embodiment of the present invention;

[0021] Figure 2 This is an example diagram of a cascaded channel provided according to an embodiment of the present invention;

[0022] Figure 3 This is an example diagram of a parallel cascaded channel provided according to an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of a scenario of a multi-intelligent reflective surface provided according to an embodiment of the present invention;

[0024] Figure 5 This is a flowchart of a channel estimation method provided according to Embodiment 1 of the present invention;

[0025] Figure 6 This is a flowchart of another channel estimation method provided in Embodiment 2 of the present invention;

[0026] Figure 7 This is a schematic diagram of the structure of a channel estimation device according to Embodiment 3 of the present invention;

[0027] Figure 8 This is a schematic diagram of the structure of an electronic device that implements the channel estimation method of this invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] It should be noted that, Figure 1 This is a communication scenario provided by an embodiment of the present invention. For example... Figure 1 As shown, this technology considers a single-user Multiple-Input Multiple-Output (MIMO) system assisted by multiple RIS (Reference Signal Providers), where these RIS work collaboratively in the communication system. Assuming the transmitted carrier wavelength is λ, and considering the uplink channel from the User Experience (UE) to the Base Station (BS), there are cases where line-of-sight (LOS) is blocked. The RIS can effectively enhance the received signal strength when the LOS is blocked. This includes single-hop paths and multi-hop paths. A single-hop path refers to a path where the UE experiences only one reflection on the RIS on the way to the BS, while a multi-hop path refers to a path where the UE experiences multiple reflections on the RIS on the way to the BS. In uplink channel estimation, the UE needs to transmit pilot signals (i.e., receive signals), and the corresponding calculations are performed at the BS.

[0031] In one embodiment, Figure 2 This is an example diagram of a cascaded channel provided according to an embodiment of the present invention; Figure 3 This is an example diagram of a parallel cascaded channel provided according to an embodiment of the present invention, such as 2 and Figure 3 Taking a cascaded channel consisting of only two RIS as an example. Assuming that there are no loops in the communication system (which can be ignored), the cascaded channel can be considered as a directed graph. Therefore, a cascaded channel can also be considered as a series of cascaded channels and cascaded channels. Figure 4 This is a schematic diagram of a scenario involving a multi-intelligent reflective surface according to an embodiment of the present invention. Figure 4 This example uses an uplink transmission scenario with two RIS (Reference Components). This embodiment of the invention can address scenarios such as... Figure 4 Channel estimation is performed on the complex multi-RIS cascaded channel shown.

[0032] Example 1

[0033] Figure 5 This is a flowchart of a channel estimation method according to Embodiment 1 of the present invention. This embodiment is applicable to channel estimation in complex multi-RIS cascaded channels. The method can be executed by a channel estimation device, which can be implemented in hardware and / or software. The channel estimation device can be configured in network equipment, such as a base station. Figure 5 As shown, the method includes:

[0034] S110. Determine the number of times each channel link in the communication system is reflected by the intelligent reflective surface.

[0035] In this context, a channel link can be understood as the path along which a signal is transmitted in a communication system; it is the transmission path that a communication signal takes from the transmitting end to the receiving end. Generally, the number of smart reflective surfaces determines the number of channel links in the communication system. In one embodiment, such as... Figure 4 The communication system shown has four channel links: F, DC, AE, and ABC. A smart reflective surface is an artificial material that can control the characteristics of electromagnetic waves, reshaping the channel links and improving communication efficiency and security. Smart reflective surfaces can be flexibly deployed in communication systems to reshape the channel links by manipulating the frequency, phase, polarization, and other characteristics of reflected or refracted electromagnetic waves. The number of reflections can be understood as the number of times the communication signal is forwarded in the channel link. Specifically, each time a communication signal passes through a smart reflective surface, it is reflected once; therefore, the number of reflections can also be understood as the number of times the signal passes through a smart reflective surface. The number of reflections is the same as the number of smart reflective surfaces present in the channel link. Figure 4 In the communication system shown, the number of reflections in channel link F is 0; the number of reflections in channel link DC is 1; the number of reflections in channel link AE is 1; and the number of reflections in channel link ABC is 2.

[0036] In this embodiment, the deployment of the communication system and the number of smart reflective surfaces can be predetermined. Paths that can be traversed from the user terminal to the base station are sequentially identified as channel links, and the number of reflections through the smart reflective surfaces in each path is determined. Alternatively, the topology of the communication system can be determined, and the topological paths corresponding to the user terminal to the base station or the base station to the user terminal can be identified as channel links. The number of times the smart reflective surfaces pass through each channel link is taken as the reflection count.

[0037] S120. Determine the effective received signal of each channel link in sequence according to the number of reflections, and use the compressed sensing algorithm to determine the channel estimation result of each channel link based on the effective received signal.

[0038] In this context, the effective received signal can be understood as the signal that arrives at the receiving end after the communication signal has passed through each channel link. Compressed sensing algorithms can be considered as utilizing the sparsity characteristics of the channel in millimeter-wave communication systems, transforming the channel estimation problem into a sparse vector representation reconstruction problem. By using compressed sensing algorithms, channel parameters can be accurately estimated from a small number of observations, thereby improving the accuracy and efficiency of channel estimation. Specifically, compressed sensing algorithms can design a suitable observation matrix to project a high-dimensional channel matrix into a low-dimensional observation space, and then use a sparse reconstruction algorithm to recover the original channel matrix from the observations. The channel estimation result refers to the estimated value of the communication channel characteristics. The channel estimation result is used to describe the relevant parameters of the channel that the signal experiences during transmission, so as to enable accurate signal detection and decoding at the receiving end.

[0039] In this embodiment, after the transmitting end sends a signal, each channel link in the communication system can simultaneously reflect the transmission channel, and the receiving end can receive the sum of the received signals from each channel link in the current communication system. At this time, the intelligent reflective surfaces corresponding to the number of reflections can be activated sequentially from smallest to largest to determine the effective received signal of each channel link. In actual operation, devices such as signal strength testers and spectrum analyzers can be used to detect the effective received signal. For example,... Figure 4 As shown, we can first activate the smart reflective surface with a reflection count of 0, meaning the smart reflective surface is not activated. At this point, we can receive the valid received signal for the channel link corresponding to 0 smart reflective surfaces, i.e., the valid received signal for channel link F. Next, we activate the smart reflective surfaces with a reflection count of 1 sequentially, for example, RIS1 first. Now, there are two channel links in the communication system: channel link F and channel link AE. After receiving the sum of the received signals from the two channel links, we can determine the difference between the sum of the received signals and the valid received signal corresponding to channel link F. This difference is taken as the valid received signal corresponding to channel link AE. Then, we deactivate RIS1 and activate RIS2, again activating the smart reflective surface with a reflection count of 1. The valid received signal corresponding to channel link DC is determined using the same method. Finally, we activate the smart reflective surfaces with a reflection count of 2, i.e., simultaneously activating RIS1 and RIS2. We determine the sum of the received signals in the communication system at this point, and the difference between the sum of the received signals and the valid received signals corresponding to channel link F, channel link AE, and channel link DC is taken as the valid received signal corresponding to channel link ABC.

[0040] After determining the effective received signals for each channel link, the channel estimation results for each channel link can be determined using compressed sensing algorithms. In practical operation, the observation matrix can be determined first. Based on the observation matrix and the effective received signals, a sparse vector representation corresponding to each smart reflector in the channel link can be determined. Then, the sparse vector representation is used to determine the channel estimation results for the channel link corresponding to the effective received signals. In practice, the observation matrix can be set according to user requirements. For example, the preset transmission beam matrix, preset reception beam matrix, the first beam domain dictionary matrix corresponding to the user end, and the second beam domain dictionary matrix corresponding to the base station can be determined. The transpose of the preset transmission beam matrix and the Krone product of the preset reception beam matrix, as well as the conjugate transpose of the first beam domain dictionary matrix and the second beam domain dictionary matrix, can be used as the observation matrix. The channel estimation results for each channel link can then be determined using compressed sensing algorithms.

[0041] S130. Determine the channel estimation results of the communication system based on the channel estimation results of each channel link.

[0042] In this embodiment, after determining the channel estimation result of each channel link, the sum of the channel estimation results of each channel link in the communication system can be determined, and the sum of the channel estimation results of each channel link can be used as the channel estimation result of the communication system.

[0043] In this embodiment of the invention, the number of reflections of each channel link in the communication system through the intelligent reflective surface is determined, and the effective received signal of each channel link is determined sequentially based on the number of reflections. Based on the effective received signal, the channel estimation result of each channel link is determined using a compressed sensing algorithm. The channel estimation result of the communication system is determined according to the channel estimation result of each channel link, thereby realizing channel estimation of any multi-RIS scenario communication system without adjusting the channel estimation modeling method, thus improving the efficiency of channel estimation of the communication system.

[0044] Example 2

[0045] Figure 6 This is a flowchart of another channel estimation method provided according to Embodiment 2 of the present invention. This embodiment is a further explanation of a channel estimation method based on the above embodiments. Figure 6 As shown, the method includes:

[0046] S210. Determine the topology of the communication system.

[0047] In this context, topology refers to the way and layout of interconnections between various nodes in a communication system, namely the connection and layout of nodes such as user terminals, base stations, and intelligent reflective surfaces.

[0048] In the embodiments, the deployment and connection relationships of the user terminal, the base station terminal, and the smart reflective surface can be determined, thereby determining the topology of the communication system.

[0049] S220: Identify the number of times each channel link is reflected by the intelligent reflective surface based on the topology.

[0050] In an embodiment, the channel links in the communication system can be determined in the topology, and then the number of times each channel link passes through the smart reflective surface can be determined, with the number of times being used as the reflection count.

[0051] In one embodiment, identifying the number of reflections of each channel link through the smart reflective surface based on the topology includes:

[0052] Find the topological path from the source node to the target node in the topology structure and use it as the channel link;

[0053] The number of times the surface passes through the intelligent reflective surface in each channel link is taken as the number of reflections.

[0054] In this context, the source node can be understood as the node that transmits signals; it can be either a user terminal or a base station. The target node can be understood as the node that receives signals; it can also be either a user terminal or a base station. In actual operation, when the channel link is an uplink channel, the source node is the user terminal and the target node is the base station; when the channel link is a downlink channel, the source node is the base station and the target node is the user terminal. The topology path refers to the path from the source node to the target node within the topology structure.

[0055] In this embodiment, the source node and target node in the topology can be determined, and the topological path from the source node to the target node can be found. This topological path is then used as the channel link. The number of times each topological path passes through the smart reflective surface is then identified, and this number is taken as the reflection count.

[0056] S230. Obtain the received signal after reflection from the smart reflective surface corresponding to the number of times reflection is activated, and use the received signal as the sum of the received signals of the effective channel links in the current communication system.

[0057] In this context, "effective channel links" can be understood as the channel links currently existing in the communication system. Disabling some intelligent reflective surfaces in the communication system reduces the number of effective channel links. The total received signal can be understood as the sum of the received signals from the effective channel links in the current communication system.

[0058] In this embodiment, the smart reflective surfaces corresponding to the number of reflections can be activated to determine the received signal reflected in the current communication system. Since activating the smart reflective surfaces corresponding to the number of reflections allows for one or more valid channel links in the communication system, the received signal can be taken as the sum of the received signals of the valid channel links in the current communication system.

[0059] S240. Determine the effective received signal of each channel link according to the sum of the received signals of each effective channel link.

[0060] In this embodiment, after determining the sum of the received signals of each valid channel link, the valid received signals of each channel link can be determined sequentially. In actual operation, the sum of the received signals of one valid channel link (i.e., the sum of received signals received without the smart reflective surface activated) can be used as the valid received signal of that channel link. Then, the sum of the received signals obtained after reflecting twice through the smart reflective surface is determined sequentially. The difference between this sum and the valid received signal of the channel link without the smart reflective surface activated is taken as the valid received signal of another channel link with a smart reflective surface. This process is repeated to determine the valid received signal of each channel link sequentially.

[0061] In one embodiment, determining the effective received signal of each channel link based on the sum of the received signals of each effective channel link includes:

[0062] Determine the maximum number of reflections in the effective channel links, and use the channel link corresponding to the maximum number of reflections as the channel link to be determined;

[0063] Within each valid channel link, identify other channel links with fewer reflections than the channel link to be identified;

[0064] The intelligent reflective surfaces on each other channel link are turned on in sequence, the corresponding first received signal of each other channel link is determined, and the sum of the first received signals is determined as the sum of invalid received signals;

[0065] The difference between the sum of received signals and the sum of invalid received signals is taken as the valid received signal of the channel link to be determined.

[0066] Here, the channel link to be determined can be understood as the channel link for which valid received signals need to be determined, and the sum of invalid received signals can be considered as the sum of received signals reflected from all channel links to be determined, excluding the channel link to be determined, in the current communication system.

[0067] In this embodiment, the number of reflections for each valid channel link can be determined, the maximum value of the number of reflections can be determined, and the channel link corresponding to the maximum value can be used as the channel link to be determined. Figure 4As shown, when both RIS1 and RIS2 are enabled, the communication system has four valid channel links: F, DC, AE, and ABC. The channel link corresponding to the maximum number of reflections is channel link ABC. Channel link ABC is selected as the channel link to be determined. At this time, the valid channel links DC, AE, and F are the other channel links. The intelligent reflective surfaces on each of the other channel links are activated sequentially to determine the corresponding first received signal for each other channel link. Specifically, Figure 4 In the communication system, the intelligent reflective surface on the effective channel link F can be activated first (i.e., if no intelligent reflective surface exists), and the sum of the received signals is taken as the first received signal corresponding to the effective channel link F. Then, RIS1 is activated, and the difference between the sum of the received signals and the first received signal corresponding to the effective channel link F is taken as the first received signal of the effective channel link AE. Similarly, RIS2 is activated to determine the first received signal of the effective channel link DC, and the sum of all first received signals is taken as the sum of invalid received signals. The difference between the sum of the received signals and the sum of the invalid received signals is then taken as the effective received signal of the channel link to be determined.

[0068] In one embodiment, i can be determined * i represents the maximum number of reflections possible when the smart reflective surface is currently in the active state. * = 0, 1, 2, ..., n. i represents the number of reflections in the current active state of the smart reflective surface, and n is the maximum number of reflections in each channel link. i ≤ i * j * This represents the number of channel links corresponding to each reflection count in the current active state of the smart reflective surface, where j * =1,2,...S i S i This represents the maximum number of valid channel links corresponding to reflection count i in the current active state of the smart reflective surface. j represents the j-th channel link corresponding to reflection count i, where j ≤ j * There are i reflections at RIS in the channel link, and the channel link can be denoted as H. (i,j) Calculate the effective received signal (excluding the (i)th) * ,j * (A link) can be represented as:

[0069]

[0070] For the sake of simplicity, noise and time t are omitted. For the preset transmission beam matrix; F t This is a preset receiving beam matrix. In the first equals sign, a valid received signal requires i = i *That is, the effective received signal of the channel link corresponding to the number of reflections under different on-states of the smart reflective surface is determined sequentially. Therefore, only i is turned on in each measurement. * The RIS used in each channel link. The second equality can be derived from this, S i Let S be the set of valid channel links when all n RIS are turned on. i In other words, it includes all channel links in the communication system with all RIS enabled. It only reflects when i is selected to be enabled. * The received signal of the RIS. This leads to the third equation, the effective received signal. is i * Received signal when RIS is turned on Subtract when i < i * and All valid received signals at that time. To enable i * The sum of the received signals obtained by the RIS used in each channel link. Specifically, the direct channel from the UE to the BS (0 reflections on the RIS) can be represented as:

[0071] y (0,1) =y (0,1) ifS0=1 (2)

[0072] At this point, all RIS are turned off, i.e. * When y is 0, the number of channels is 1. (0,1) This refers to the sum of received signals in the communication system after enabling 0 RIS, y (0,1) This is the sum of received signals in the communication system after 0 RIS are enabled, which is directly measured.

[0073] In practical operation, the third equal sign can be used to determine the effective received signal of each channel link. For example, y (i,j) The sum of received signals in the communication system after activating i RIS is directly measured; y (i,j) This is the sum of the received signals in the communication system after i RIS are enabled. For example... Figure 4 As shown, you can first enable i * A smart reflective surface with a quantity of 0, correspondingly, j * The value is 1. When i * When the quantity is 0, i = 0; when j * When j = 1, j = 1. In this case, the channel link in the communication system can be considered to include only the channel link F(H). (0,1) At this point, H can be received. (0,1) The corresponding received signal y (0,1) , because y (0,1) =y(0,1) If S0 = 1, the channel link F(H) can be considered as... (0,1) The corresponding valid received signal is y. (0,1) Then, sequentially activate the smart reflective surfaces with a reflection count of 1. When i * When i = 1, j * The value is 2, where j = 1 or 2. The smart reflective surfaces corresponding to the two channel links can be activated sequentially. Assuming channel link AE is the first channel link corresponding to a reflection count of 1, channel link AE can be considered as H. (1,1) Channel link DC is the second channel link corresponding to reflection count 1, and channel link AE can be considered as H. (1,2) First, enable RIS1. At this point, two channel links can exist in the communication system, channel link F(H). (0,1) ) and channel link AE (H (1,1) The sum of received signals from the two channel links, y (1,1) Then, the total received signal y can be determined. (1,1) With channel link F(H) (0,1) The corresponding valid received signal y (0,1) The difference is used as the channel link AE(H) value. (1,1) The corresponding valid received signal (y) (1,1) Then turn RIS1 off and turn RIS2 on. This also activates the smart reflective surface with a reflection count of 1. The channel link DC(H) is then determined using the method described above. (1,2) The corresponding valid received signal (y) (1,2) Finally, the smart reflective surface with a reflection count of 2 is activated, meaning RIS1 and RIS2 are activated simultaneously. * The value is 2. At this point, there are 4 links in the communication system, and the number of channel links corresponding to a single occurrence of 2 is 1, i.e., j... * The value is 1. Assume the channel link ABC is the sum of the received signals (y) of the communication system at this point. (2,1) ,) determine the sum of received signals and the effective received signal (y) corresponding to channel F of channel link F. (0,1) ), the effective received signal (y) corresponding to channel link AE (1,1) ) and the effective received signal (y) corresponding to the channel link DC (1,2) The difference between the values ​​of y and y is taken as the effective received signal (y) corresponding to channel link ABC. (2,1) ).

[0074] S250. By effectively receiving signals, the sparse vector representation of each smart reflective surface in the corresponding channel link is determined using a compressed sensing algorithm.

[0075] In this embodiment, once the valid received signal for each channel link is determined, the sparse vector representation of each smart reflective surface in the corresponding channel link can be determined using a compressed sensing algorithm based on the valid received signal.

[0076] In one embodiment, the sparse vector representation corresponding to each smart reflector in the corresponding channel link is determined using a compressed sensing algorithm by effectively receiving signals, including:

[0077] Determine the reflection vector of each smart reflector in the channel link corresponding to the valid received signal;

[0078] Determine the preset transmission beam matrix, preset reception beam matrix, first beam domain dictionary matrix corresponding to the user end, and second beam domain dictionary matrix corresponding to the base station in the channel link, and determine the observation matrix according to the preset transmission beam matrix, preset reception beam matrix, first beam domain dictionary matrix, and second beam domain dictionary matrix;

[0079] Extract the third beam domain dictionary matrix corresponding to the smart reflective surface, determine the transpose of the third beam domain dictionary matrix as the first matrix, determine the conjugate transpose of the third beam domain dictionary matrix as the second matrix, and take the Krone product of the first matrix and the second matrix as the third matrix.

[0080] The sparse vector representation of each smart reflective surface is determined based on the reflection vector, the observation matrix, the third matrix, and the effective received signal.

[0081] The reflection vector can be considered as the reflection amplitude and phase of the incident signal by each reflecting unit, and can be set according to user requirements. The intelligent reflective surface reflects the received signal based on the reflecting units; the number of reflecting units can be one or more, and each reflecting unit can correspond to multiple transmission pilot blocks. The preset transmission beam matrix can be considered as a pre-set precoding matrix at the base station end, representing the matrix of the transmission beams of the pilot blocks of the reflecting units; the preset receiving beam matrix can be considered as a pre-set precoding matrix at the user end, representing the matrix of the receiving beams of the pilot blocks of the reflecting units; the first beam domain dictionary matrix can be understood as a dictionary matrix at the user end composed of the turning vectors of a pre-fixed grid; the second beam domain dictionary matrix can be understood as a dictionary matrix at the base station end composed of the turning vectors of a pre-fixed grid; and the third beam domain dictionary can be understood as a dictionary matrix at the intelligent reflective surface composed of the turning vectors of a pre-fixed grid. The number of fixed grids can be preset.

[0082] In this embodiment, the reflection vector of each smart reflector in the channel link corresponding to the effective received signal can be determined. Then, the transpose of the preset transmission beam matrix and the conjugate transpose of the first beam domain dictionary matrix are determined. The Krone product of the conjugate transpose of the first beam domain dictionary matrix and the preset received beam matrix, and the Krone product of the conjugate transpose of the first beam domain dictionary matrix and the second beam domain dictionary matrix are determined respectively. The product of the two Krone products is used as the observation matrix. Next, the third beam domain dictionary matrix corresponding to the smart reflector is determined. The transpose and conjugate transpose of the third beam domain dictionary matrix are determined as the first matrix and the second matrix respectively. The Krone product of the first matrix and the second matrix is ​​used as the third matrix. Finally, the sparse vector representation corresponding to each smart reflector is determined using compressed sensing based on the reflection vector, the observation matrix, the third matrix, and the effective received signal.

[0083] Specifically, the user terminal (UE) and the base station (BS) can each be equipped with N t and N r The antenna RIS has M reflection elements. The uplink channel from UE to BS can be represented as:

[0084] H=Gdiag(Ψ)R(3)

[0085] Here, G and R are the channels from RIS to BS and from UE to RIS, respectively. There are K RIS reflection modes during the channel estimation phase. The k-th (k = 1, 2, ..., K) reflection mode is represented by the RIS reflection vector Ψ. k H is a sparse vector, i.e., the channel estimation result.

[0086] The signal received under the t-th (t=1,2,…,T) pilot block of the k-th reflection mode (after power normalization) can be expressed as:

[0087]

[0088] in, Defined as; It is the combiner at the BS end, which is the matrix representation of the transmission beam of the t-th pilot block of the k-th reflection unit, i.e., the preset transmission beam matrix; It is the precoder at the UE end, which is the matrix representation of the received beam of the t-th pilot block of the k-th reflection unit, i.e., the preset received beam matrix; It is the equivalent received noise at the BS end. and These refer to the number of beams at the UE and BS ends, respectively. In compressed sensing, a beamspace dictionary can be selected.

[0089] in and M G These represent the number of grid points (grid size) at the UE, BS, and RIS ends, respectively. This is the dictionary matrix for the first beam domain. This is the dictionary matrix for the second beam domain; This is the dictionary matrix for the third beam domain.

[0090] When M = M G (For a uniform planar array, both dimensions must remain equal) and K = M, for any RIS reflection matrix Ψ, the cascaded channel can be estimated:

[0091]

[0092] in It is the estimated sparse vector representation (corresponding to the channel in the beam domain). For inverse quantization; D = D(1:M) G ,:)yes The former M G Columns, where symbols Represents the Khatri-Rao product.

[0093] At this point, the observation matrix can be represented as

[0094] A concatenated channel is a link from the UE to the BS that reflects off one or more RIS surfaces, where n represents the number of RIS traversed by the reflection, and the reflection vectors are denoted as Ψ. 1 ,Ψ 2 ,…,Ψ n The expression for a serial channel is:

[0095] H = H 1 Ω 1 H 2 Ω 2 H 3 …H n Ω n H n+1 (6)

[0096] Among them, H 1 H 2 ,…,H n+1 This represents each stage of a cascaded series channel. For i = 1, 2, ..., n, Ψ i Let be the reflection vector of the i-th RIS.

[0097] For a cascaded channel with n RIS, we can perform n single-RIS channel estimations, keeping the other reflection coefficients fixed during the process. The specific process is described below:

[0098]

[0099] in, For reverse quantization; To fix the reflection vector corresponding to RIS; This is the dictionary matrix for the first beam domain. Ψ is the dictionary matrix for the second beam domain. n Let be the reflection vector of the i-th RIS; It is the estimated sparse vector representation; D = D(1:M) G ,:)yes The former M G Columns, where symbols Represents the Khatri-Rao product; n fixed RIS reflection vectors are They were all selected to achieve good performance in the single RIS channel estimation. After fixing the other reflection vectors in the cascaded channel link, the effective received signal corresponding to each smart reflector can be determined by the effective received signal. Then, by solving formula (7) using formulas (4) and (5), Λ can be determined. 1 ,Λ 2 ,…,Λ n That is, the sparse vector representation corresponding to each smart reflective surface.

[0100] S260. Use sparse vector representation to determine the channel estimation results of the channel link corresponding to the effective received signal.

[0101] In the embodiment, after determining the sparse vector representation corresponding to each reflective surface, the channel estimation result of the channel link corresponding to the effective received signal can be determined according to the above formulas (5), (6) and (7).

[0102] In one embodiment, the channel estimation result for the channel link corresponding to the valid received signal is determined using sparse vector representation, including:

[0103] Extract the reflection coefficient of each smart reflective surface in the channel link corresponding to the valid received signal;

[0104] Extract the first beam domain dictionary matrix and the second beam domain dictionary matrix; wherein, the first beam domain dictionary matrix is ​​the beam domain dictionary matrix associated with the user end; and the second beam domain dictionary matrix is ​​the beam domain dictionary matrix associated with the base station end;

[0105] The channel estimation results for the channel link corresponding to the effective received signal are determined based on sparse vector representation, reflection coefficient, first beam domain dictionary matrix, and second beam domain dictionary matrix.

[0106] In the embodiment, the sparse vector representation of each smart reflective surface in the channel link corresponding to the effective received signal and the reflection coefficient of the smart reflective surface can be determined. The channel estimation result of the channel link corresponding to the effective received signal is determined according to the sparse vector representation, reflection coefficient, first beam domain dictionary matrix and second beam domain dictionary matrix.

[0107] In actual operation, the calculation method of channel estimation results can be derived from formulas (5), (6) and (7):

[0108]

[0109] Among them, among them, For reverse quantization; To fix the reflection vector corresponding to RIS; This is the dictionary matrix for the first beam domain. Ψ is the dictionary matrix for the second beam domain. n Let be the reflection vector of the i-th RIS; It is the estimated sparse vector representation; D = D(1:M) G ,:)yes The former M G Columns, where symbols ⊙ represents the Khatri-Rao product; division is element-wise division, ⊙ represents element-wise multiplication, and its exponent represents the power of the corresponding element. It should be noted that in formula (8), elements with amplitudes close to 0 in the numerator and denominator will directly make the corresponding position of the calculation result 0, because this indicates that there is no specific channel transmission angle at that grid point position.

[0110] S270. The channel estimation results of each channel link are accumulated to obtain the accumulated result, and the accumulated result is used as the channel estimation result of the communication system.

[0111] In this embodiment, after determining the channel estimation result for each channel link, the channel estimation results can be accumulated to obtain an accumulated result, which is then used as the channel estimation result for the communication system. In actual operation, channel estimation can be described as follows:

[0112]

[0113] in, For i = 1, 2, ..., n, Ψ iS is the reflection vector of the i-th RIS; i Let H be the number of channel links corresponding to the number of reflections i (there are i reflections in RIS, denoted as H). (i,j) ).

[0114] This invention, through determining the topology of the communication system, identifies the number of reflections of each channel link by a smart reflective surface based on the topology, facilitating the determination of the channel link corresponding to each reflection count. By acquiring the received signal reflected after activating the smart reflective surface corresponding to the reflection count, the received signal is used as the sum of the received signals of the current effective channel links in the communication system. The effective received signal of each channel link is determined according to the sum of the received signals of each effective channel link, achieving rapid determination of the effective received signal of each channel link. Using the effective received signal, a compressed sensing algorithm is used to determine the sparse vector representation corresponding to each smart reflective surface in the corresponding channel link. The sparse vector representation is then used to determine the channel estimation result of the channel link corresponding to the effective received signal, transforming the complex multi-RIS cascaded channel into multiple serial channel links, facilitating channel estimation for each channel link. The channel estimation results of each channel link are accumulated to obtain an accumulated result, which is used as the channel estimation result of the communication system, achieving rapid determination of the channel estimation result of the communication system, improving the convenience of determining the channel estimation result of the communication system, and enhancing the user experience.

[0115] In one embodiment, S i The number of different channel links reflecting on the i-th RIS allows us to obtain the complexity of this method and the order of the required number of pilots:

[0116]

[0117] Parallel channel links can be computed in parallel, and the total error is additive. However, considering equation (8), the estimation results with errors are multiplied, leading to error propagation, which affects the accuracy of the channel estimation results. Therefore, algorithms are needed to reduce the impact of error propagation.

[0118] For a cascaded link with n RIS, after obtaining the angle domain estimation result H using formula (3), Λ in formula (7) i It can be stacked to obtain:

[0119]

[0120] in, For reverse quantization, Λ i H is the sparse vector representation corresponding to each smart reflective surface. sumTo obtain the channel estimation result matrix by summing, we can calculate the summation result matrix H. sum A rough estimate can be made, where the sparse grid structure index is selected from the most dominant (highest modulus) rows and columns. Therefore, the region containing non-zero elements in the sparse matrix is ​​reduced, improving overall recovery performance. Compressed sensing or least squares algorithms can be used to fully recover the angular domain sparse channel, mitigating error propagation.

[0121] Example 3

[0122] Figure 7 This is a schematic diagram of a channel estimation device according to Embodiment 3 of the present invention. Figure 7 As shown, the device includes: a reflection count determination module 71, a channel information determination module 72, and a channel estimation determination module 73.

[0123] Among them, the reflection count determination module 71 is used to determine the number of reflections of each channel link in the communication system through the intelligent reflective surface;

[0124] The channel information determination module 72 is used to determine the effective received signal of each channel link in sequence according to the number of reflections, and to determine the channel estimation result of each channel link based on the effective received signal using a compressed sensing algorithm;

[0125] The channel estimation determination module 73 is used to determine the channel estimation result of the communication system based on the channel estimation results of each channel link.

[0126] In this embodiment of the invention, a reflection count determination module determines the number of reflections of each channel link in the communication system through the intelligent reflective surface. A channel information determination module sequentially determines the effective received signal of each channel link based on the reflection count. A channel estimation determination module uses a compressed sensing algorithm based on the effective received signal to determine the channel estimation result of each channel link. Finally, the channel estimation result of the communication system is determined according to the channel estimation results of each channel link. This enables channel estimation for any multi-RIS scenario communication system without adjusting the channel estimation modeling method, thus improving the efficiency of channel estimation in the communication system.

[0127] In one embodiment, the reflection count determination module 71 includes:

[0128] Topology determination unit, used to determine the topology of the communication system;

[0129] The reflection count determination unit is used to identify the number of reflections of each channel link through the intelligent reflective surface based on the topology.

[0130] In one implementation, the reflection count determination unit is specifically used for:

[0131] Find the topological path from the source node to the target node in the topology structure and use it as the channel link;

[0132] The number of times the surface passes through the intelligent reflective surface in each channel link is taken as the number of reflections.

[0133] In one embodiment, the channel information determination module 72 includes:

[0134] The signal sum determination unit is used to obtain the received signal reflected by the intelligent reflective surface after the number of times the reflection is activated, and to use the received signal as the sum of the received signals of the effective channel link in the current communication system.

[0135] The effective signal determination unit is used to determine the effective received signal of each channel link according to the sum of the received signals of each effective channel link;

[0136] The sparse vector representation determination unit is used to determine the sparse vector representation of each smart reflective surface in the corresponding channel link by using compressed sensing algorithm through effective received signal;

[0137] The channel information determination unit is used to determine the channel estimation result of the channel link corresponding to the valid received signal using sparse vector representation.

[0138] In one embodiment, the valid signal determination unit is specifically used for:

[0139] Determine the maximum number of reflections in the effective channel links, and use the channel link corresponding to the maximum number of reflections as the channel link to be determined;

[0140] Within each valid channel link, identify other channel links with fewer reflections than the channel link to be identified;

[0141] The intelligent reflective surfaces on each other channel link are turned on in sequence, the corresponding first received signal of each other channel link is determined, and the sum of the first received signals is determined as the sum of invalid received signals;

[0142] The difference between the sum of received signals and the sum of invalid received signals is taken as the valid received signal of the channel link to be determined.

[0143] In one embodiment, the sparse vector representation determination unit is specifically used for:

[0144] Determine the reflection vector of each smart reflector in the channel link corresponding to the valid received signal;

[0145] Determine the preset transmission beam matrix, preset reception beam matrix, first beam domain dictionary matrix corresponding to the user end, and second beam domain dictionary matrix corresponding to the base station in the channel link, and determine the observation matrix according to the preset transmission beam matrix, preset reception beam matrix, first beam domain dictionary matrix, and second beam domain dictionary matrix;

[0146] Extract the third beam domain dictionary matrix corresponding to the smart reflective surface, determine the transpose of the third beam domain dictionary matrix as the first matrix, determine the conjugate transpose of the third beam domain dictionary matrix as the second matrix, and take the Krone product of the first matrix and the second matrix as the third matrix.

[0147] The sparse vector representation of each smart reflective surface is determined based on the reflection vector, the observation matrix, the third matrix, and the effective received signal.

[0148] In one embodiment, the channel information determination unit is specifically used for:

[0149] Extract the reflection coefficient of each smart reflective surface in the channel link corresponding to the valid received signal;

[0150] Extract the first beam domain dictionary matrix and the second beam domain dictionary matrix; wherein, the first beam domain dictionary matrix is ​​the beam domain dictionary matrix associated with the user end; and the second beam domain dictionary matrix is ​​the beam domain dictionary matrix associated with the base station end;

[0151] The channel estimation results for the channel link corresponding to the effective received signal are determined based on sparse vector representation, reflection coefficient, first beam domain dictionary matrix, and second beam domain dictionary matrix.

[0152] In one embodiment, the channel estimation and determination module 73 includes:

[0153] The channel estimation determination unit is used to accumulate the channel estimation results of each channel link to obtain an accumulated result, and use the accumulated result as the channel estimation result of the communication system.

[0154] The channel estimation device provided in the embodiments of the present invention can execute the channel estimation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0155] Example 4

[0156] Figure 8 This is a schematic diagram of the structure of an electronic device implementing the channel estimation method of embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0157] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0158] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0159] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as channel estimation methods.

[0160] In some embodiments, the channel estimation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the channel estimation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the channel estimation method by any other suitable means (e.g., by means of firmware).

[0161] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0162] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0163] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0164] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0165] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0166] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0167] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the channel estimation method of any embodiment of the present invention.

[0168] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0169] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0170] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of channel estimation, characterized by, The method comprises the steps of: determining the number of reflections of each channel link in a communication system through a smart reflecting surface; determining the effective received signals of each channel link in turn according to the number of reflections, and determining the channel estimation results of each channel link by using a compressed sensing algorithm based on the effective received signals; determining the channel estimation results of the communication system according to the channel estimation results of each channel link; wherein the step of determining the effective received signals of each channel link in turn according to the number of reflections comprises the steps of: obtaining the received signals obtained by the reflection of the smart reflecting surface with the number of reflections turned on, and taking the received signals as the sum of the received signals of the effective channel links in the current communication system; determining the effective received signals of each channel link according to the sum of the received signals of each effective channel link; wherein the step of determining the effective received signals of each channel link according to the sum of the received signals of each effective channel link comprises the steps of: determining the maximum value of the number of reflections in the effective channel links, and taking the channel link corresponding to the maximum value as a to-be-determined channel link; determining other channel links in each effective channel link which have a smaller number of reflections than the to-be-determined channel link; turning on the smart reflecting surface on each of the other channel links in turn, determining the first received signals of each of the other channel links, and determining the sum of the first received signals as the sum of invalid received signals; taking the difference between the sum of the received signals and the sum of the invalid received signals as the effective received signal of the to-be-determined channel link.

2. The method of claim 1, wherein, The method comprises the steps of: determining the topology structure of the communication system; identifying the number of reflections of each channel link through the smart reflecting surface based on the topology structure.

3. The method of claim 2, wherein, The step of identifying the number of reflections of each channel link through the smart reflecting surface based on the topology structure comprises the steps of: finding the topological path corresponding to the source node and the target node in the topology structure as the channel link; taking the number of times of passing through the smart reflecting surface in each channel link as the number of reflections.

4. The method of claim 1, wherein, The step of determining the channel estimation results of each channel link by using a compressed sensing algorithm based on the effective received signals comprises the steps of: determining the sparse vector representation corresponding to each smart reflecting surface in the corresponding channel link by using a compressed sensing algorithm based on the effective received signals; determining the channel estimation results of the channel link corresponding to the effective received signals by using the sparse vector representation.

5. The method of claim 4, wherein, The step of determining the sparse vector representation corresponding to each smart reflecting surface in the corresponding channel link by using a compressed sensing algorithm based on the effective received signals comprises the steps of: determining the reflection vector of each smart reflecting surface in the channel link corresponding to the effective received signals; determining a preset transmission beam matrix, a preset receiving beam matrix, a first beam domain dictionary matrix corresponding to the user end, and a second beam domain dictionary matrix corresponding to the base station end, and determining an observation matrix according to the preset transmission beam matrix, the preset receiving beam matrix, the first beam domain dictionary matrix, and the second beam domain dictionary matrix. extracting a third beam domain dictionary matrix corresponding to the smart reflecting surface, determining a transpose of the third beam domain dictionary matrix as a first matrix, determining a conjugate transpose of the third beam domain dictionary matrix as a second matrix, and taking a Kronecker product of the first matrix and the second matrix as a third matrix; determining the sparse vector representation corresponding to each smart reflecting surface based on the reflection vector, the observation matrix, the third matrix, and the effective received signal.

6. The method of claim 4, wherein, The determination of the channel estimation result of the channel link corresponding to the effective received signal by using the sparse vector representation comprises: extracting a reflection coefficient of each smart reflecting surface in the channel link corresponding to the effective received signal; extracting a first beam domain dictionary matrix and a second beam domain dictionary matrix; wherein the first beam domain dictionary matrix is a beam domain dictionary matrix associated with a user end, and the second beam domain dictionary matrix is a beam domain dictionary matrix associated with a base station end; determining a channel estimation result of the channel link corresponding to the effective received signal according to the sparse vector representation, the reflection coefficient, the first beam domain dictionary matrix, and the second beam domain dictionary matrix.

7. The method of claim 1, wherein, The determination of the channel estimation result of the communication system according to the channel estimation results of the channel links comprises: accumulating the channel estimation results of the channel links to obtain an accumulation result, and taking the accumulation result as the channel estimation result of the communication system.

8. A channel estimation apparatus characterized by comprising: comprise: a reflection number determination module configured to determine a reflection number of each channel link passing through a smart reflecting surface in a communication system; a channel information determination module configured to determine effective received signals of the channel links in sequence according to the reflection numbers, and determine channel estimation results of the channel links by using a compressive sensing algorithm based on the effective received signals; a channel estimation determination module configured to determine a channel estimation result of the communication system according to the channel estimation results of the channel links; The channel information determination module comprises: a signal sum determination unit configured to obtain a received signal reflected by a smart reflecting surface with an open reflection number, and take the received signal as a received signal sum of an effective channel link in the current communication system; an effective signal determination unit configured to determine effective received signals of the channel links according to the received signal sums of the effective channel links; The effective signal determination unit is specifically configured to: determine a maximum value of the reflection numbers in the effective channel links, and take a channel link corresponding to the maximum value as a to-be-determined channel link; determine other channel links with a reflection number smaller than that of the to-be-determined channel link in each of the effective channel links; open smart reflecting surfaces on the other channel links in sequence, determine first received signals corresponding to the other channel links, and determine a sum of the first received signals as an invalid received signal sum; take a difference between the received signal sum and the invalid received signal sum as an effective received signal of the to-be-determined channel link.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the channel estimation method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a processor to implement the channel estimation method in any one of claims 1-7 when executed.

11. A computer program product, characterised in that, The computer program product comprises a computer program which, when executed by a processor, implements the channel estimation method according to any one of claims 1-7.

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