Multi-region RIS cascade channel estimation method, device, equipment and medium
By using the Saleh-Valenzuela channel model and sparsification techniques in a RIS-assisted communication system, combined with a sparse compression sampling algorithm, the complexity and accuracy issues of high-dimensional channel estimation are solved, and efficient multi-region channel estimation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUILIN UNIVERSITY OF TECHNOLOGY
- Filing Date
- 2026-01-26
- Publication Date
- 2026-04-21
AI Technical Summary
Existing RIS-assisted communication systems require huge pilot overhead and high computational complexity for high-dimensional channel estimation, and have low channel estimation accuracy and robustness in multi-user scenarios, especially ignoring the specific structural features of the channel in the angular domain.
A pre-built Saleh-Valenzuela channel model is used to model a multi-region uplink communication system. The channel sparsity is processed by using virtual angular domains and overcomplete dictionary matrices. Combined with the structural sparse compressed sampling matching pursuit algorithm, the non-zero element values of the channel matrix are recovered by least squares algorithm through the constraints of common row and column support sets.
It improves the channel estimation accuracy for multi-region user channel structure sparsity, reduces the dimensionality complexity and pilot overhead of channel estimation, and enhances the accuracy and robustness of estimation.
Smart Images
Figure CN121907641A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of wireless communication technology, and particularly relates to a multi-region RIS cascaded channel estimation method, apparatus, device, and medium. Background Technology
[0002] Reconfigurable Intelligent Surfaces (RIS) have become a key candidate technology for 6G communication due to their low cost, low power consumption, and ability to enhance signal coverage. In RIS-assisted multi-user communication systems, obtaining accurate Channel State Information (CSI) is crucial for achieving efficient passive beamforming and optimizing system performance.
[0003] However, existing RIS-assisted communication systems typically consist of a large number of passive reflective elements and lack signal processing capabilities. This leads to a dramatic increase in the cascaded channel dimension between the base station and the user as the number of RIS elements increases. Traditional channel estimation methods often require enormous pilot overhead and have extremely high computational complexity when dealing with such high-dimensional channels.
[0004] Although existing compressed sensing algorithms (such as Orthogonal Matching Pursuit, OMP) utilize the sparsity of the channel to reduce overhead to some extent, these methods usually ignore the specific structural features of the channel in the angular domain in multi-user scenarios, resulting in lower channel estimation accuracy and robustness for multi-region user channel structure sparsity under low pilot overhead. Summary of the Invention
[0005] This invention provides a multi-region RIS cascaded channel estimation method, apparatus, device, and medium, which can improve the channel estimation accuracy of multi-region user channel structure sparsity.
[0006] To achieve the above objectives, this invention provides a multi-region RIS cascaded channel estimation method, comprising: Construct a RIS-assisted multi-region uplink communication system model based on base station entities, RIS entities, and user entities in different regions; The channel of each entity node in the multi-region uplink communication system model is modeled using the pre-built Saleh-Valenzuela channel model to obtain the system signal model; Uplink pilot signals are sent by user entities in different regions and reflected to the base station entity by the RIS entity. An observation signal is constructed based on the uplink pilot signal received by the base station entity. A concatenated channel matrix of base station entity-RIS entity-user entity is established based on the observation signal using the system signal model. The concatenated channel matrix is then mapped to the virtual corner domain to obtain the virtual corner domain channel matrix. The virtual angular domain channel matrix is subjected to angular domain sparsification based on the overcomplete dictionary matrix of the virtual angular domain to obtain the angular domain sparse channel matrix. The structural sparse compressed sampling matching pursuit algorithm is used to estimate the row structure sparsity in the angular domain sparse channel matrix, and the common row support set shared by user entities in different regions is obtained. The common row support set shared by user entities in different regions is used as a prior constraint, and the structural sparse compressed sampling matching pursuit algorithm is used to estimate the column support set of users in different regions in the common non-zero row. The non-zero element values of the angular domain sparse channel matrix are calculated using the least squares algorithm based on the common row support set and column support set. Then, the non-zero element values of the angular domain sparse channel matrix are inversely transformed to the spatial domain based on the overcomplete dictionary matrix to obtain the cascaded channel estimation matrix of the base station entity-RIS entity-user entity.
[0007] To address the above problems, the present invention also provides a multi-region RIS cascaded channel estimation device, the device comprising: The model building module is used to construct a RIS-assisted multi-region uplink communication system model based on the base station entity, RIS entity, and user entities in different regions; and to model the channel of each entity node in the multi-region uplink communication system model using the pre-built Saleh-Valenzuela channel model to obtain the system signal model. The corner domain conversion module is used to transmit uplink pilot signals from user entities in different regions, and reflect the uplink pilot signals to the base station entity using the RIS entity. Based on the uplink pilot signals received by the base station entity, an observation signal is constructed. Based on the observation signal, a concatenated channel matrix of base station entity-RIS entity-user entity is established using the system signal model. The concatenated channel matrix is then mapped to the virtual corner domain to obtain the virtual corner domain channel matrix. The virtual corner domain channel matrix is then subjected to corner domain sparsification based on the overcomplete dictionary matrix of the virtual corner domain to obtain the corner domain sparse channel matrix. The cascaded channel estimation matrix acquisition module is used to estimate the row structure sparsity in the angular domain sparse channel matrix using the structural sparse compressed sampling matching pursuit algorithm, to obtain the common row support set shared by user entities in different regions, and to estimate the column support set of users in different regions in the common non-zero rows using the structural sparse compressed sampling matching pursuit algorithm as a prior constraint. Based on the common row support set and column support set, the non-zero element values of the angular domain sparse channel matrix are calculated using the least squares algorithm, and the non-zero element values of the angular domain sparse channel matrix are inversely transformed to the spatial domain based on the overcomplete dictionary matrix to obtain the cascaded channel estimation matrix of base station entity-RIS entity-user entity.
[0008] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the multi-region RIS cascaded channel estimation method described above.
[0009] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the multi-region RIS cascaded channel estimation method described above.
[0010] This invention utilizes a pre-constructed Saleh-Valenzuela channel model to model the channel of each entity node in a multi-region uplink communication system model, obtaining a system signal model. The pre-constructed Saleh-Valenzuela channel model can characterize the sparsity and multipath structure of millimeter-wave / terahertz channels, thus preserving the sparsity characteristics of the channel in the system signal model. Furthermore, uplink pilot signals are transmitted by user entities in different regions and reflected to the base station entity using a RIS entity. An observation signal is constructed based on the uplink pilot signal received by the base station entity, and a concatenated channel matrix of base station entity-RIS entity-user entity is established using the system signal model based on the observation signal. This concatenated channel matrix is then mapped to a virtual angular domain to obtain a virtual angular domain channel matrix. This allows the seemingly chaotic channel energy in the high-dimensional spatial domain to be focused into a small number of angular domains, revealing the sparse structural characteristics of the channel in the angular domain more clearly. Moreover, the virtual angular domain channel matrix is sparsified based on the overcomplete dictionary matrix of the virtual angular domain. The process involves processing to obtain a angular domain sparse channel matrix. This matrix can be sparsified using an overcomplete dictionary matrix, resulting in a more compact channel representation, reduced redundant information, and lower dimensionality complexity in channel estimation. Furthermore, a structurally sparse compressed sampling matching pursuit algorithm is used to estimate the row structure sparsity in the angular domain sparse channel matrix. This can be achieved by extracting a common row support set shared by users in different regions, leveraging the structural features among multiple users to improve estimation accuracy. Then, the column support set is estimated under the constraint of the common row support set, further enhancing the accuracy of user-level channel estimation. Finally, the non-zero element values of the angular domain sparse channel matrix are calculated using the least squares algorithm based on the common row and column support sets. The non-zero element values are then inversely transformed to the spatial domain using the overcomplete dictionary matrix, yielding a concatenated channel estimation matrix for the base station entity, RIS entity, and user entity. The non-zero values of the angular domain sparse channel are accurately recovered using the least squares method, and the concatenated channel estimation matrix in the spatial domain is obtained through inverse transformation. This process improves the channel estimation accuracy for the structural sparsity of multi-region user channels. Attached Figure Description
[0011] Figure 1 This is a flowchart illustrating a multi-region RIS cascaded channel estimation method provided in an embodiment of the present invention. Figure 2 This is a flowchart illustrating an example of a multi-region RIS cascaded channel estimation method provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of a multi-region uplink communication system model provided by a multi-region RIS cascaded channel estimation method according to an embodiment of the present invention. Figure 4A schematic diagram comparing the SNR and NMSE relationship curves in a specific embodiment of the multi-region RIS cascaded channel estimation method provided in an embodiment of the present invention; Figure 5 A schematic diagram comparing the pilot overhead and NMSE relationship curves in a specific embodiment of the multi-region RIS cascaded channel estimation method provided in an embodiment of the present invention; Figure 6 A schematic diagram comparing the relationship curves between the number of paths and NMSE in a specific embodiment of the multi-region RIS cascaded channel estimation method provided in an embodiment of the present invention; Figure 7 A functional block diagram of a multi-region RIS cascaded channel estimation device provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of an electronic device that implements the multi-region RIS cascaded channel estimation method according to an embodiment of the present invention.
[0012] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0013] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0014] This application provides a multi-region RIS cascaded channel estimation method. The execution entity of this multi-region RIS cascaded channel estimation method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the multi-region RIS cascaded channel estimation method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.
[0015] Reference Figure 1 The diagram shown is a flowchart illustrating a multi-region RIS cascaded channel estimation method according to an embodiment of the present invention. In this embodiment, the multi-region RIS cascaded channel estimation method includes: S1. Construct a RIS-assisted multi-region uplink communication system model based on the base station entity, RIS entity, and user entities in different regions.
[0016] Understandably, a base station entity refers to a core node in a communication system, responsible for interacting with users. It is typically equipped with a multi-antenna array for transmitting and receiving signals.
[0017] Understandably, a RIS (Reconfigurable Intelligent Surface) entity refers to a reconfigurable intelligent surface composed of a large number of low-cost passive reflective units, which can realize intelligent control of the wireless signal propagation environment and enhance communication performance.
[0018] Understandably, different regional user entities refer to user terminals in different regions, such as mobile phones and sensors. Each user entity has an independent channel with the base station entity and the RIS entity.
[0019] S2. The channel of each entity node in the multi-region uplink communication system model is modeled using the pre-built Saleh-Valenzuela channel model to obtain the system signal model.
[0020] Understandably, the pre-built Saleh-Valenzuela channel model is a sparse multipath channel model, commonly used in millimeter-wave / terahertz communication. Its principle is based on the assumption that the channel consists of a finite number of paths, each with independent gain, delay, and angle characteristics, making it suitable for describing sparse propagation environments in high-frequency bands.
[0021] Understandably, a system signal model refers to a mathematical expression derived from channel modeling that describes the signal transmission relationship between the base station, RIS, and the user.
[0022] Specifically, the channel of each entity node in the multi-region uplink communication system model is modeled using the pre-built Saleh-Valenzuela channel model to obtain the system signal model. The channel from the base station entity to the RIS entity includes: The channel from the base station entity to the RIS entity is represented by the following formula. : ; in, The number of reflection units on the RIS entity. The number of antennas of the base station entity. This represents the number of paths between the RIS entity and user entities in different regions. For the first Complex gain of the path, The array response vector of the RIS entity. Let be the elevation angle of the incident wave. Let be the azimuth angle of the incident wave. This is the transpose of the array response vectors of user entities in different regions. It is the transpose symbol. This is the normalization factor.
[0023] Understandably, the number of reflective units on the RIS entity refers to the number of antennas on the panel.
[0024] Understandably, complex gain includes path fading and phase information.
[0025] For example, the received signal of the base station entity It can be obtained using the following formula: ; in, For the channel from the base station entity to the RIS entity, For the channel from the RIS entity to the user entity, , , , , Here is the reflection matrix at the RIS entity. This represents the pilot signal transmitted by the base station entity. This represents Gaussian white noise.
[0026] S3. Use user entities in different regions to send uplink pilot signals, and reflect the uplink pilot signals to the base station entity using the RIS entity. Construct observation signals based on the uplink pilot signals received by the base station entity, and establish a concatenated channel matrix of base station entity-RIS entity-user entity using the system signal model based on the observation signals. Map the concatenated channel matrix to the virtual angular domain to obtain the virtual angular domain channel matrix.
[0027] Understandably, a cascaded channel matrix refers to the combination of channels from the base station entity to the RIS entity and channels from the RIS entity to the user entity.
[0028] Understandably, virtual angular domain refers to the physical channel being represented in the angular domain by mathematical transformations based on antenna indexes.
[0029] Understandably, the virtual angular domain channel matrix refers to the matrix obtained by mapping the cascaded channels to the angular domain. The angular domain refers to the distribution of signals at different incident and exit angles, and it is usually sparse.
[0030] S4. Based on the overcomplete dictionary matrix of the virtual angular domain, perform angular domain sparsification on the virtual angular domain channel matrix to obtain the angular domain sparse channel matrix.
[0031] Understandably, an overcomplete dictionary matrix refers to a matrix used for angular domain transformation that contains more basis vectors than the channel dimension. It can more finely characterize the sparse structure of the signal in the angular domain, can more finely divide the angular network, and improve the resolution of angular estimation.
[0032] Understandably, the angular domain sparse channel matrix refers to the matrix after angular domain transformation, in which most of the elements of the matrix are 0, and only a few positions are non-zero values, which represent the actual signal propagation path.
[0033] Specifically, the virtual corner domain channel matrix is subjected to corner domain sparsification based on the overcomplete dictionary matrix of the virtual corner domain, resulting in a corner domain sparse channel matrix, including: The virtual angular domain channel matrix is subjected to angular domain sparsification using the following formula: ; in, For a sparse channel matrix in the angular domain, This is an overcomplete dictionary matrix from RIS entities to user entities. This is the overcomplete dictionary matrix from base station entities to RIS entities. For virtual angular domain channel matrix, , For the first Each regional user entity For the first Individual user entities For dimension The complex field of .
[0034] For example, the overcomplete dictionary matrix from RIS entities of a virtual domain to user entities. It can be defined as: ; Under the virtual corner domain representation, the overcomplete dictionary matrix Each column corresponds to a specific combination of azimuth and elevation angles, i.e., the array's steering vector. This is the response vector of an array consisting of the same number of antennas as the base station entity in a specific direction; in order to construct this dictionary matrix, the angle of arrival space of the RIS entity in the horizontal and vertical directions needs to be uniformly discretized into several quantized angles. Similarly, the overcomplete dictionary matrix from base station entities to RIS entities can be represented as follows: ; Similarly, The response vector of an array consisting of an array of antennas corresponding to the number of reflective elements on a RIS entity in a specific direction.
[0035] S5. The structural sparse compressed sampling matching pursuit algorithm is used to estimate the row structure sparsity in the angular domain sparse channel matrix, so as to obtain the common row support set shared by user entities in different regions. The common row support set shared by user entities in different regions is used as a prior constraint, and the structural sparse compressed sampling matching pursuit algorithm is used to estimate the column support set of users in different regions in the common non-zero row.
[0036] Understandably, the Structured Sparse CoSaMP algorithm is a classic compressed sensing reconstruction algorithm used to recover sparse signals from a small number of observations. Its structural sparsity means that the non-zero elements in the sparse signal are not randomly distributed, but have a certain specific structure.
[0037] Understandably, row sparsity refers to the fact that in a angular domain channel matrix, non-zero elements tend to concentrate in a few rows.
[0038] Understandably, the common row support set refers to the set of indices (i.e., positional information) of the non-zero elements in the matrix. Understandably, a column support set refers to the index of a specific non-zero column in a given non-zero row.
[0039] Specifically, the structural sparse compressed sampling matched pursuit algorithm is used to estimate the row structure sparsity in the angular domain sparse channel matrix, obtaining the common row support set shared by user entities in different regions, including: By leveraging the angular overlap characteristics of user entities in different regions in spatial distribution, the channel power of each user entity is accumulated through an iterative optimization process to form a comprehensive power distribution vector; The top K row indices with the highest cumulative power values are selected from the integrated power distribution vector to form the current estimated common row support set, and the non-zero row positions in the channel matrix are identified through the common row support set.
[0040] Understandably, channel power refers to the energy of a user's signal along a certain channel path.
[0041] Furthermore, the step of using the shared row support set of user entities from different regions as a priori constraint, and estimating the column support set of users from different regions in the shared non-zero rows using the structural sparse compressed sampling matching pursuit algorithm, includes: Based on the location information of common non-zero rows in the common row support set, the correlation between the observation residual and the perception matrix is calculated using the structural sparse compressed sampling matching pursuit algorithm. In each iteration of the structural sparse compressed sampling matching pursuit algorithm to calculate the correlation between the observation residual and the perception matrix, the column index with the strongest correlation is selected and added to the column support set. The residual is then updated by least squares reconstruction until the preset stopping criterion is met, and the iteration stops, thus obtaining the column support set.
[0042] S6. Calculate the non-zero element values of the angular domain sparse channel matrix using the least squares algorithm based on the common row support set and column support set, and inversely transform the non-zero element values of the angular domain sparse channel matrix to the spatial domain based on the overcomplete dictionary matrix to obtain the concatenated channel estimation matrix of the base station entity-RIS entity-user entity.
[0043] Understandably, the least squares algorithm is a mathematical optimization technique. After determining the positional information of non-zero elements, the least squares method is used to calculate the most accurate value for that positional information, minimizing the sum of squared errors.
[0044] Specifically, based on the overcomplete dictionary matrix, the non-zero element values of the angular domain sparse channel matrix are inversely transformed to the spatial domain to obtain the concatenated channel estimation matrix of the base station entity-RIS entity-user entity, including: The concatenated channel estimation matrix of the base station entity, RIS entity, and user entity is obtained using the following formula: ; in, This is the concatenated channel estimation matrix for the base station entity, RIS entity, and user entity. For the first In the region, the first The estimated values of the angular domain sparse channel matrix for each user, where, , For the first The common row support set estimated in the next iteration For the first The column support set estimated in the next iteration For the perception matrix The false rebellion, For the first In the region, the first Predicted vectors for each user This is the first iteration of the algorithm's internal loop.
[0045] This invention utilizes a pre-constructed Saleh-Valenzuela channel model to model the channel of each entity node in a multi-region uplink communication system model, obtaining a system signal model. The pre-constructed Saleh-Valenzuela channel model can characterize the sparsity and multipath structure of millimeter-wave / terahertz channels, thus preserving the sparsity characteristics of the channel in the system signal model. Furthermore, uplink pilot signals are transmitted by user entities in different regions and reflected to the base station entity using a RIS entity. An observation signal is constructed based on the uplink pilot signal received by the base station entity, and a concatenated channel matrix of base station entity-RIS entity-user entity is established using the system signal model based on the observation signal. This concatenated channel matrix is then mapped to a virtual angular domain to obtain a virtual angular domain channel matrix. This allows the seemingly chaotic channel energy in the high-dimensional spatial domain to be focused into a small number of angular domains, revealing the sparse structural characteristics of the channel in the angular domain more clearly. Moreover, the virtual angular domain channel matrix is sparsified based on the overcomplete dictionary matrix of the virtual angular domain. The process involves processing to obtain a angular domain sparse channel matrix. This matrix can be sparsified using an overcomplete dictionary matrix, resulting in a more compact channel representation, reduced redundant information, and lower dimensionality complexity in channel estimation. Furthermore, a structurally sparse compressed sampling matching pursuit algorithm is used to estimate the row structure sparsity in the angular domain sparse channel matrix. This can be achieved by extracting a common row support set shared by users in different regions, leveraging the structural features among multiple users to improve estimation accuracy. Then, the column support set is estimated under the constraint of the common row support set, further enhancing the accuracy of user-level channel estimation. Finally, the non-zero element values of the angular domain sparse channel matrix are calculated using the least squares algorithm based on the common row and column support sets. The non-zero element values are then inversely transformed to the spatial domain using the overcomplete dictionary matrix, yielding a concatenated channel estimation matrix for the base station entity, RIS entity, and user entity. The non-zero values of the angular domain sparse channel are accurately recovered using the least squares method, and the concatenated channel estimation matrix in the spatial domain is obtained through inverse transformation. This process improves the channel estimation accuracy for the structural sparsity of multi-region user channels.
[0046] Reference Figure 2 The diagram shown is a flowchart illustrating an example of a multi-region RIS cascaded channel estimation method provided in an embodiment of the present invention.
[0047] Reference Figure 3 The figure shown is a schematic diagram of a multi-region uplink communication system model of a multi-region RIS cascaded channel estimation method provided in an embodiment of the present invention.
[0048] Reference Figure 4 The diagram shown is a comparison of the SNR and NMSE relationship curves in a specific embodiment of the multi-region RIS cascaded channel estimation method provided by an embodiment of the present invention.
[0049] Understandably, this invention proposes a specific embodiment, employing the following four strategies for performance comparison with the proposed SS-CoSaMP strategy. Specifically, the invention is verified in a Python 3.8 and PyTorch 1.6 environment, comparing the SS-CoSaMP algorithm of this invention with traditional binary search, convective learning channel estimation methods, and traditional federated learning channel estimation methods: 1. Traditional Dichotomy (LS) The traditional bisection method is a channel estimation method that uses the least squares criterion.
[0050] 2. Orthogonal Matching Pursuit (OMP) Algorithm This method utilizes the sparsity of signals and approximates the target signal by selecting the optimal subset from an overcomplete base (dictionary) through an iterative process.
[0051] 3. Structure Sparse Orthogonal Matching Pursuit (SS-OMP) algorithm This method incorporates the structural prior of "non-zero tap clustering" into the block selection step based on OMP, adding the entire block of atoms at once.
[0052] 4. Double-Structured Orthogonal Matching Pursuit (DS-OMP) Algorithm This method utilizes the "dual-structure sparsity" priors of both the time domain and the frequency domain (or angular domain) to jointly select blocks across domains and update the support set.
[0053] Figure 4The proposed SS-CoSaMP (Structured Sparse CoSaMP) algorithm significantly outperforms the traditional OMP and DS-OMP algorithms. Specifically, under the same SNR conditions, the NMSE of the SS-CoSaMP algorithm is reduced by approximately 1-2 dB compared to the OMP and DS-OMP algorithms, indicating a clear advantage in signal reconstruction accuracy. Furthermore, compared to the SS-OMP algorithm, the NMSE of the SS-CoSaMP algorithm is also reduced by approximately 0.25 dB, further validating its robustness and accuracy in noisy environments. As SNR increases, the impact of noise on the signal gradually weakens, and the NMSE of all four estimation algorithms shows a decreasing trend. This is because under high SNR conditions, the dominance of the signal increases, and the interference of noise is relatively reduced, thus gradually reducing the estimation error. However, even under high SNR conditions, the SS-CoSaMP algorithm still maintains a low NMSE, indicating that it can provide stable and accurate channel estimation under various SNR environments.
[0054] Reference Figure 5 The figure shown is a schematic diagram comparing the pilot overhead and NMSE relationship curves in a specific embodiment of the multi-region RIS cascaded channel estimation method provided by an embodiment of the present invention.
[0055] Understandably, Figure 5 The NMSE performance simulation results of the proposed SS-CoSaMP algorithm were verified for the number of RIS entities (64, 144, and 256). As shown in the figure, the estimation performance of the SS-CoSaMP algorithm decreases with the increase of the number of RIS entities. The increase in the number of RIS entities directly leads to a significant increase in the dimensionality of the channel under test, thus increasing the complexity of signal estimation. In high-dimensional channel environments, the algorithm needs to handle more unknown parameters, which increases the difficulty of signal reconstruction and consequently leads to an increase in NMSE performance. When the number of RIS entities increases from 64 to 256, although the SNR remains unchanged, the NMSE performance of the SS-CoSaMP algorithm increases significantly in the low pilot overhead range. This is because under low pilot overhead conditions, the pilot resources available for channel estimation are limited, while high-dimensional channels require more pilot data to ensure estimation accuracy. Therefore, with the increase of channel dimension, the relative insufficiency of pilot resources limits the estimation performance of the algorithm.
[0056] Reference Figure 6 The figure shown is a schematic diagram comparing the relationship curves between the number of paths and NMSE in a specific embodiment of the multi-region RIS cascaded channel estimation method provided by an embodiment of the present invention.
[0057] Understandably, Figure 6The proposed SS-CoSaMP algorithm is described as being closer to the baseline scheme than the traditional OMP algorithm and SS-OMP, exhibiting a lower channel estimation error. Furthermore, as the number of unknown channel parameters to be estimated increases, the channel estimation performance of all four algorithms decreases. This is because the computational complexity of the algorithm increases with the number of parameters to be estimated, leading to a decrease in accuracy when handling more parameters. Especially in multipath scenarios, the increased channel complexity makes it difficult for the algorithm to accurately estimate more channel parameters with limited observation data. Therefore, the estimation performance of all four algorithms is affected, showing a trend of gradual performance decline with increasing number of parameters.
[0058] like Figure 7 The diagram shown is a functional block diagram of a multi-region RIS cascaded channel estimation device provided in an embodiment of the present invention.
[0059] The multi-region RIS cascaded channel estimation device 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the multi-region RIS cascaded channel estimation device 100 may include a model building module 101, an angular domain conversion module 102, and a cascaded channel estimation matrix acquisition module 103.
[0060] The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.
[0061] In this embodiment, the functions of each module / unit are as follows: The model building module 101 is used to build a RIS-assisted multi-region uplink communication system model based on the base station entity, RIS entity, and user entities in different regions; and to model the channel of each entity node in the multi-region uplink communication system model using the pre-built Saleh-Valenzuela channel model to obtain the system signal model.
[0062] The corner domain conversion module 102 is used to send uplink pilot signals using user entities in different regions, and reflect the uplink pilot signals to the base station entity using the RIS entity. It constructs an observation signal based on the uplink pilot signals received by the base station entity, and establishes a concatenated channel matrix of base station entity-RIS entity-user entity using the system signal model based on the observation signal. It then maps the concatenated channel matrix to a virtual corner domain to obtain a virtual corner domain channel matrix. Finally, it performs corner domain sparsification processing on the virtual corner domain channel matrix based on the overcomplete dictionary matrix of the virtual corner domain to obtain a corner domain sparse channel matrix.
[0063] The cascaded channel estimation matrix acquisition module 103 is used to estimate the row structure sparsity in the angular domain sparse channel matrix using the structural sparse compressed sampling matching pursuit algorithm, to obtain the common row support set shared by user entities in different regions, and to estimate the column support set of users in different regions in the common non-zero rows using the structural sparse compressed sampling matching pursuit algorithm, taking the common row support set shared by user entities in different regions as a prior constraint; to calculate the non-zero element values of the angular domain sparse channel matrix using the least squares algorithm based on the common row support set and column support set, and to inversely transform the non-zero element values of the angular domain sparse channel matrix to the spatial domain based on the overcomplete dictionary matrix, to obtain the cascaded channel estimation matrix of base station entity-RIS entity-user entity.
[0064] like Figure 8 The diagram shown is a schematic representation of an electronic device that implements a multi-region RIS cascaded channel estimation method according to an embodiment of the present invention.
[0065] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a multi-region RIS cascaded channel estimation method program.
[0066] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a multi-region RIS cascaded channel estimation method program) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.
[0067] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of a multi-region RIS cascaded channel estimation method program, but also to temporarily store data that has been output or will be output.
[0068] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.
[0069] The communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.
[0070] Figure 8 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 8The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.
[0071] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.
[0072] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.
[0073] The multi-region RIS cascaded channel estimation method program stored in the memory 11 of the electronic device is a combination of multiple instructions, which, when run in the processor 10, can achieve the following: Construct a RIS-assisted multi-region uplink communication system model based on base station entities, RIS entities, and user entities in different regions; The channel of each entity node in the multi-region uplink communication system model is modeled using the pre-built Saleh-Valenzuela channel model to obtain the system signal model; Uplink pilot signals are sent by user entities in different regions and reflected to the base station entity by the RIS entity. An observation signal is constructed based on the uplink pilot signal received by the base station entity. A concatenated channel matrix of base station entity-RIS entity-user entity is established based on the observation signal using the system signal model. The concatenated channel matrix is then mapped to the virtual corner domain to obtain the virtual corner domain channel matrix. The virtual angular domain channel matrix is subjected to angular domain sparsification based on the overcomplete dictionary matrix of the virtual angular domain to obtain the angular domain sparse channel matrix. The structural sparse compressed sampling matching pursuit algorithm is used to estimate the row structure sparsity in the angular domain sparse channel matrix, and the common row support set shared by user entities in different regions is obtained. The common row support set shared by user entities in different regions is used as a prior constraint, and the structural sparse compressed sampling matching pursuit algorithm is used to estimate the column support set of users in different regions in the common non-zero row. The non-zero element values of the angular domain sparse channel matrix are calculated using the least squares algorithm based on the common row support set and column support set. Then, the non-zero element values of the angular domain sparse channel matrix are inversely transformed to the spatial domain based on the overcomplete dictionary matrix to obtain the cascaded channel estimation matrix of the base station entity-RIS entity-user entity.
[0074] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.
[0075] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).
[0076] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: Construct a RIS-assisted multi-region uplink communication system model based on base station entities, RIS entities, and user entities in different regions; The channel of each entity node in the multi-region uplink communication system model is modeled using the pre-built Saleh-Valenzuela channel model to obtain the system signal model; Uplink pilot signals are sent by user entities in different regions and reflected to the base station entity by the RIS entity. An observation signal is constructed based on the uplink pilot signal received by the base station entity. A concatenated channel matrix of base station entity-RIS entity-user entity is established based on the observation signal using the system signal model. The concatenated channel matrix is then mapped to the virtual corner domain to obtain the virtual corner domain channel matrix. The virtual angular domain channel matrix is subjected to angular domain sparsification based on the overcomplete dictionary matrix of the virtual angular domain to obtain the angular domain sparse channel matrix. The structural sparse compressed sampling matching pursuit algorithm is used to estimate the row structure sparsity in the angular domain sparse channel matrix, and the common row support set shared by user entities in different regions is obtained. The common row support set shared by user entities in different regions is used as a prior constraint, and the structural sparse compressed sampling matching pursuit algorithm is used to estimate the column support set of users in different regions in the common non-zero row. The non-zero element values of the angular domain sparse channel matrix are calculated using the least squares algorithm based on the common row support set and column support set. Then, the non-zero element values of the angular domain sparse channel matrix are inversely transformed to the spatial domain based on the overcomplete dictionary matrix to obtain the cascaded channel estimation matrix of the base station entity-RIS entity-user entity.
[0077] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.
[0078] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0079] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.
[0080] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0081] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.
[0082] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.
[0083] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.
[0084] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.
[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A multi-region RIS cascaded channel estimation method, characterized in that, The method includes: Construct a RIS-assisted multi-region uplink communication system model based on base station entities, RIS entities, and user entities in different regions; The channel of each entity node in the multi-region uplink communication system model is modeled using the pre-built Saleh-Valenzuela channel model to obtain the system signal model; Uplink pilot signals are sent by user entities in different regions and reflected to the base station entity by the RIS entity. An observation signal is constructed based on the uplink pilot signal received by the base station entity. A concatenated channel matrix of base station entity-RIS entity-user entity is established based on the observation signal using the system signal model. The concatenated channel matrix is then mapped to the virtual corner domain to obtain the virtual corner domain channel matrix. The virtual angular domain channel matrix is subjected to angular domain sparsification based on the overcomplete dictionary matrix of the virtual angular domain to obtain the angular domain sparse channel matrix. The structural sparse compressed sampling matching pursuit algorithm is used to estimate the row structure sparsity in the angular domain sparse channel matrix, and the common row support set shared by user entities in different regions is obtained. The common row support set shared by user entities in different regions is used as a prior constraint, and the structural sparse compressed sampling matching pursuit algorithm is used to estimate the column support set of users in different regions in the common non-zero row. The non-zero element values of the angular domain sparse channel matrix are calculated using the least squares algorithm based on the common row support set and column support set. Then, the non-zero element values of the angular domain sparse channel matrix are inversely transformed to the spatial domain based on the overcomplete dictionary matrix to obtain the cascaded channel estimation matrix of the base station entity-RIS entity-user entity.
2. The multi-region RIS cascaded channel estimation method as described in claim 1, characterized in that, The pre-built Saleh-Valenzuela channel model is used to model the channel of each entity node in the multi-region uplink communication system model to obtain the system signal model. The channel from the base station entity to the RIS entity includes: The channel from the base station entity to the RIS entity is represented by the following formula. : ； in, The number of reflection units on the RIS entity. The number of antennas of the base station entity. This represents the number of paths between the RIS entity and user entities in different regions. For the first Complex gain of the path, The array response vector of the RIS entity. Let be the elevation angle of the incident wave. Let be the azimuth angle of the incident wave. This is the transpose of the array response vectors of user entities in different regions. It is the transpose symbol. This is the normalization factor.
3. The multi-region RIS cascaded channel estimation method as described in claim 1, characterized in that, The step of performing corner-domain sparsification on the virtual corner-domain channel matrix based on the overcomplete dictionary matrix of the virtual corner domain to obtain a corner-domain sparse channel matrix includes: The virtual angular domain channel matrix is subjected to angular domain sparsification using the following formula: ； in, For a sparse channel matrix in the angular domain, This is an overcomplete dictionary matrix from RIS entities to user entities. This is the overcomplete dictionary matrix from base station entities to RIS entities. For virtual angular domain channel matrix, , For the first Each regional user entity For the first Individual user entities For dimension The complex field of .
4. The multi-region RIS cascaded channel estimation method as described in claim 1, characterized in that, The method of using a structurally sparse compressed sampling matched pursuit algorithm to estimate the row structure sparsity in the angular domain sparse channel matrix yields a common row support set shared by user entities in different regions, including: By leveraging the angular overlap characteristics of user entities in different regions in spatial distribution, the channel power of each user entity is accumulated through an iterative optimization process to form a comprehensive power distribution vector; The top K row indices with the highest cumulative power values are selected from the integrated power distribution vector to form the current estimated common row support set, and the non-zero row positions in the channel matrix are identified through the common row support set.
5. The multi-region RIS cascaded channel estimation method as described in claim 1, characterized in that, The step of using the shared row support set of users from different regions as a prior constraint and estimating the column support set of users from different regions in the shared non-zero rows using the structural sparse compressed sampling matching pursuit algorithm includes: Based on the location information of common non-zero rows in the common row support set, the correlation between the observation residual and the perception matrix is calculated using the structural sparse compressed sampling matching pursuit algorithm. In each iteration of the structural sparse compressed sampling matching pursuit algorithm to calculate the correlation between the observation residual and the perception matrix, the column index with the strongest correlation is selected and added to the column support set. The residual is then updated by least squares reconstruction until the preset stopping criterion is met, and the iteration stops, thus obtaining the column support set.
6. The multi-region RIS cascaded channel estimation method as described in claim 1, characterized in that, The step of inversely transforming the non-zero element values of the angular domain sparse channel matrix to the spatial domain based on the overcomplete dictionary matrix to obtain the concatenated channel estimation matrix of the base station entity-RIS entity-user entity includes: The concatenated channel estimation matrix of the base station entity, RIS entity, and user entity is obtained using the following formula: ； in, This is the concatenated channel estimation matrix for the base station entity, RIS entity, and user entity. For the first In the region, the first The estimated values of the angular domain sparse channel matrix for each user, where, , For the first The common row support set estimated in the next iteration For the first The column support set estimated in the next iteration For the perception matrix The false rebellion, For the first In the region, the first Predicted vectors for each user This is the first iteration of the algorithm's internal loop.
7. A multi-region RIS cascaded channel estimation device, characterized in that, The apparatus can implement the multi-region RIS cascaded channel estimation method as described in any one of claims 1 to 6, and the apparatus includes: The model building module is used to construct a RIS-assisted multi-region uplink communication system model based on the base station entity, RIS entity, and user entities in different regions; and to model the channel of each entity node in the multi-region uplink communication system model using the pre-built Saleh-Valenzuela channel model to obtain the system signal model. The corner domain conversion module is used to transmit uplink pilot signals from user entities in different regions, and reflect the uplink pilot signals to the base station entity using the RIS entity. Based on the uplink pilot signals received by the base station entity, an observation signal is constructed. Based on the observation signal, a concatenated channel matrix of base station entity-RIS entity-user entity is established using the system signal model. The concatenated channel matrix is then mapped to the virtual corner domain to obtain the virtual corner domain channel matrix. The virtual corner domain channel matrix is then subjected to corner domain sparsification based on the overcomplete dictionary matrix of the virtual corner domain to obtain the corner domain sparse channel matrix. The cascaded channel estimation matrix acquisition module is used to estimate the row structure sparsity in the angular domain sparse channel matrix using the structural sparse compressed sampling matching pursuit algorithm, to obtain the common row support set shared by user entities in different regions, and to estimate the column support set of users in different regions in the common non-zero rows using the structural sparse compressed sampling matching pursuit algorithm as a prior constraint. Based on the common row support set and column support set, the non-zero element values of the angular domain sparse channel matrix are calculated using the least squares algorithm, and the non-zero element values of the angular domain sparse channel matrix are inversely transformed to the spatial domain based on the overcomplete dictionary matrix to obtain the cascaded channel estimation matrix of base station entity-RIS entity-user entity.
8. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the multi-region RIS cascaded channel estimation method as described in any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-region RIS cascaded channel estimation method as described in any one of claims 1 to 6.