Large-scale MIMO-OFDM physical channel fingerprint and channel map construction and channel acquisition method

Abstract

The invention provides a large-scale MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) physical channel fingerprint and channel map construction and channel acquisition method. The method comprises the following steps: firstly, defining a new physical channel fingerprint, and comprehensively representing physical propagation characteristics of a channel through a group of low-dimensional parameters by virtue of a cluster-based geometric channel model; secondly, proposing a channel map construction method, extracting a physical channel fingerprint from a channel measurement sample by utilizing a relationship between a physical channel parameter and a space-frequency-time domain channel, and constructing a physical channel map of a position index; finally, the invention also provides a channel information acquisition method based on a physical channel map, and beam domain statistical channel information adaptive to the current system configuration can be generated with relatively low complexity so as to assist in instantaneous channel estimation. According to the invention, through physical channel fingerprint characterization, the limitation of the existing channel fingerprint technology is overcome; a traditional online detection method is replaced by the physical channel map, so that the system channel detection overhead is effectively reduced, and the channel information acquisition efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and proposes a physical channel fingerprint (PCF, Physical CF) and channel map construction method for large-scale MIMO-OFDM systems, and proposes a channel acquisition method based on the physical channel map. Background Art

[0002] With the rapid development of 6G mobile communication technology, the system will achieve significant improvements in user terminal (UT) connection density, data transmission rate, and positioning accuracy. As the core technology of 5G networks, massive MIMO-OFDM technology will continue to play a key role in 6G communication systems due to its advantages such as high spectrum efficiency and strong anti-interference capability. However, with the rapid increase in UT connection density and the continuous expansion of antenna array size, the system faces new technical challenges, among which the difficulty and complexity of obtaining channel state information (CSI) are particularly prominent. Statistical CSI (sCSI) is often used as important prior information to assist in obtaining instantaneous CSI due to its slow time-varying characteristics. However, using traditional long-term online detection methods to obtain sCSI will result in huge resource overhead and computational complexity. To address this technical bottleneck, existing research has begun to explore new ways to leverage the technical advantages of 6G systems and assist in sCSI acquisition through location information. Specifically, massive MIMO-OFDM systems can accurately collect high-resolution channel parameters (including path gain, angle, and delay, etc.). At the same time, thanks to the dense deployment of UT nodes, the system can obtain more refined spatial sampling data. Based on this feature, by using base stations and UT devices as fixed and mobile sensors, a high-precision statistical parameter database, namely the channel fingerprint (CF) map, can be constructed. This location-indexed map can directly provide the statistical channel parameters of the current area, effectively avoiding the resource overhead brought by traditional online detection methods. However, the existing CF map still has obvious deficiencies in storage efficiency, parameter completeness, and system adaptability, which seriously restricts its practical application value in massive MIMO-OFDM systems. Summary of the Invention

[0003] Purpose of the Invention: To address the shortcomings of existing technologies, the present invention proposes a massive MIMO-OFDM physical channel fingerprint and channel map construction. By constructing a position-indexed physical channel map, the limitations of existing channel fingerprints in terms of storage efficiency and practicality are overcome. Furthermore, the present invention also proposes a channel acquisition method based on the physical channel map, which directly generates statistical channel information using the physical channel fingerprint. This achieves efficient, low-overhead channel acquisition without online detection, providing reliable technical support for 6G and future mobile communication systems.

[0004] Technical solution: In order to achieve the above-mentioned purpose, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map, comprising the following steps:

[0006] Divide the target communication area into grids and establish location coordinate indexes;

[0007] For each location grid, physical characteristic parameters are extracted based on the acquired channel samples to construct a physical channel fingerprint. The physical parameters of each multipath component in each channel sample are clustered to obtain a physical propagation path cluster between the transmitter and receiver. The physical characteristic parameters of a single physical propagation path cluster include: cluster power, mean and standard deviation of the departure angle of the multipath components within the cluster, mean and standard deviation of the arrival angle of the multipath components within the cluster, and mean and standard deviation of the delay of the multipath components within the cluster. The physical channel fingerprint is the set of physical characteristic parameters corresponding to all physical propagation path clusters between the current user terminal location and the base station location.

[0008] Each location coordinate is associated with its corresponding physical channel fingerprint and stored to construct a location-indexed physical channel map.

[0009] In some embodiments, the method for constructing the physical channel fingerprint includes:

[0010] Obtain multiple channel samples within the current location area;

[0011] Extract physical parameters such as path gain, departure angle, arrival angle and delay of each multipath component in each channel sample;

[0012] Multipath components with similar angle-delay characteristics are clustered into propagation path clusters; the statistical characteristics of the multipath parameters within each propagation path cluster are analyzed, and finally the characteristic parameters of all propagation path clusters are merged to form a complete physical channel fingerprint for that location.

[0013] In some embodiments, the channel sample is obtained by uplink detection, and the specific implementation includes:

[0014] All antenna elements configured in the mobile terminal periodically send two-dimensional time-frequency pilot signals to the base station. After receiving the signal, the base station uses an information geometry algorithm or sparse Bayesian method to estimate the three-dimensional channel in the space-frequency-time domain to obtain channel samples.

[0015] In some embodiments, the physical parameters corresponding to the multipath components are extracted. After the channel is estimated, the physical channel parameters corresponding to the multipath components are used as the object to be solved, and an optimization problem is constructed so that the mean square error between the space-frequency-time domain channel samples reconstructed by different multipath components and the space-frequency-time domain channel samples obtained by detection is minimized, and a spatial alternating generalized expectation maximization algorithm or a compressed sensing algorithm is used to solve the constructed optimization problem.

[0016] In a second aspect, the present invention provides a method for acquiring massive MIMO-OFDM channel information based on a physical channel map, comprising the following steps:

[0017] Construct a physical channel map according to the method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map described in the first aspect;

[0018] The physical channel map is queried based on the location information of the user terminal to obtain the corresponding physical channel fingerprint; based on the physical channel fingerprint and combined with the terminal mobility characteristics, statistical channel information is generated; the statistical channel information is used as prior information to assist instantaneous channel estimation.

[0019] In some embodiments, the statistical channel information is the beam domain channel power distribution, including a one-dimensional spatial beam domain power distribution vector, a two-dimensional spatial-frequency beam domain power distribution matrix, a triple spatial-frequency-time beam domain power distribution tensor, etc.; wherein, the spatial beam domain, the frequency beam domain and the time beam domain can be regarded as quantized sampling of the angle domain, the delay domain and the Doppler domain respectively, and the power distribution in the beam domain can also be regarded as a quantized form of the angle-delay domain continuous power distribution function.

[0020] In some embodiments, the generating of the statistical channel information includes:

[0021] Generates the power distribution function in the angle-delay domain based on the physical channel fingerprint. Taking into account the mobility of the user terminal, calculates the channel covariance matrix in the spatial domain, space-frequency domain, and space-frequency-time domain.

[0022] Based on the mapping relationship between the spatial domain / spatial-frequency domain / spatial-frequency-time domain covariance matrix and the one-dimensional spatial beam domain power distribution vector / two-dimensional spatial-frequency beam domain power distribution matrix / triplet spatial-frequency-time beam domain power distribution tensor, the acquisition of statistical channel information is formulated as an optimization problem to minimize the difference between the spatial domain / spatial-frequency domain / spatial-frequency-time domain channel covariance matrix calculated by the physical channel fingerprint and the spatial domain / spatial-frequency domain / spatial-frequency-time domain covariance matrix calculated by the one-dimensional spatial beam domain power distribution vector / two-dimensional spatial-frequency beam domain power distribution matrix / triplet spatial-frequency-time beam domain power distribution tensor to achieve optimal statistical channel information acquisition.

[0023] Taking the triple-beam based channel model as an example, the generation of triple-beam domain statistical channel information includes:

[0024] The physical channel fingerprint is searched based on the physical channel map, and the angle-delay domain power distribution function of the current location is restored based on the channel fingerprint. A space-frequency-time domain covariance matrix is ​​generated based on the relationship between the space-frequency-time domain channel covariance matrix and the angle-delay domain power distribution.

[0025] The triple-beam domain power distribution tensor to be solved is split into a real tensor and its own dot product form. This real tensor is used as the solution object, and another space-frequency-time domain covariance matrix is ​​generated by using the mapping relationship between the space-frequency-time domain channel and the triple-beam domain channel.

[0026] In order to measure the difference between the two space-frequency-time domain covariance matrices, the two space-frequency-time domain covariance matrices are transformed with the help of the space-frequency-time domain rudder matrix to obtain two tensors of the same dimension, an optimization problem is established, and the Kullback-Leibler divergence between the two tensors of the same dimension is minimized as the optimization goal. The optimization problem is solved using the gradient descent method to obtain the solution of the real tensor;

[0027] A dot product operation is performed on the solution of the real tensor and itself, and the obtained tensor is the required triple beam domain statistical channel information.

[0028] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements the steps of the method described in the first and / or second aspects above.

[0029] In a fourth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect and / or the second aspect.

[0030] Beneficial Effects: Compared with the existing technology, the present invention has the following advantages: the physical channel fingerprint proposed in the present invention overcomes the limitations of existing channel fingerprints in terms of storage efficiency and practicality, can more comprehensively reflect the physical characteristics of the current propagation environment, and occupies relatively small storage space; the method proposed in the present invention for generating statistical CSI with the help of physical channel maps does not rely on the traditional online detection process, effectively avoids the additional overhead and data processing complexity caused by detection, and realizes efficient and low-overhead statistical CSI acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of a cluster-based geometric random channel model in an embodiment of the present invention.

[0032] Figure 2 Schematic diagram of a physical channel spectrum in an embodiment of the present invention.

[0033] Figure 3 This is a flow chart of a method for acquiring massive MIMO-OFDM channel information based on a physical channel map according to an embodiment of the present invention.

[0034] Figure 4 This is a performance diagram of the channel information acquisition method proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0036] The embodiment of the present invention discloses a method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map, comprising: in a massive MIMO-OFDM system, dividing a target communication area into a plurality of grids and establishing a position coordinate index; for each position grid, extracting the physical characteristic parameters of the position based on the acquired channel samples, thereby constructing a physical channel fingerprint; and associating and storing each position coordinate with its corresponding physical channel fingerprint to construct a position-indexed physical channel map.

[0037] In a specific embodiment, the grid division is such that within the same grid area, the physical statistical characteristics of the corresponding channel of each position point remain approximately unchanged, and each grid has its corresponding position coordinates.

[0038] The physical characteristic parameters in this embodiment include the statistical characteristic parameters of each physical propagation path cluster between the transmitter and receiver in the angle-delay domain. Each physical propagation path cluster is composed of several multipath components with similar angle-delay domain distribution characteristics. The power distribution of the multipath components within the same physical propagation path cluster in the angle and delay domains can be fitted using a Gaussian distribution function. The physical characteristic parameters of a single physical propagation path cluster include: cluster power, the mean and standard deviation of the departure angles of the multipath components within the cluster, the mean and standard deviation of the arrival angles of the multipath components within the cluster, and the mean and standard deviation of the delays of the multipath components within the cluster. The physical channel fingerprint refers to the set of physical characteristic parameters corresponding to all physical propagation path clusters between the current user terminal location and the base station location. This physical channel fingerprint can be used to reconstruct a joint power distribution function in the angle-delay domain to fully characterize the channel statistics. This fingerprint depends solely on the physical propagation environment characteristics at the current location and is independent of antenna configuration, system parameters, terminal mobility, etc.

[0039] The physical channel map in this embodiment is a database consisting of physical channel fingerprints indexed by location. In some embodiments, dynamic updating of the map content can be achieved by adjusting the location grid division accuracy of the propagation area.

[0040] Based on the large-scale MIMO-OFDM physical channel fingerprint and channel map construction method disclosed in the above embodiments, the embodiment of the present invention discloses a large-scale MIMO-OFDM channel information acquisition method based on the physical channel map, including: querying the physical channel map according to the location information of the user terminal to obtain the corresponding physical channel fingerprint; based on the physical channel fingerprint, combined with the terminal mobility characteristics, generating beam domain statistical channel information adapted to the current system configuration; this statistical channel information can be used as prior information to assist instantaneous channel estimation.

[0041] Assisted instantaneous channel estimation uses the beam-domain statistical channel information generated by the physical channel fingerprint as prior information during instantaneous channel estimation to obtain an instantaneous channel estimate with minimum mean square error. Specific approaches to using prior statistical channel information to assist instantaneous channel estimation can be referenced in existing research. The key point of the present invention lies in generating beam-domain statistical channel information based on the physical channel fingerprint.

[0042] Figure 1A schematic diagram of a propagation scenario in an embodiment of the present invention is provided, in which a base station (BS) communicates with multiple user terminals (UTs) on the ground. Uniform linear arrays (ULAs) are configured at both the BS and UTs. Different propagation paths between the BS and UTs are represented by different physical propagation path clusters, each containing multipath components with similar propagation characteristics.

[0043] Figure 2 A schematic diagram of a physical channel map in an embodiment of the present invention is provided. First, the current propagation scenario is divided into several grids. The grid division ensures that the physical statistical characteristics of the channel remain stable within the same grid area. Each grid area has corresponding position coordinates. Each grid area has a corresponding PCF. All grids and their corresponding PCFs together constitute the physical channel map that describes the current scenario.

[0044] Figure 3 A flow chart of the method for acquiring massive MIMO-OFDM channel information based on a physical channel map, as proposed in an embodiment of the present invention, is provided. The target communication area is divided into several grids, and a location coordinate index is established. For each location point, a set of physical characteristic parameters of the channel is acquired to generate a physical channel fingerprint for that location. The coordinates of each location point are associated and stored with their corresponding physical channel fingerprint, constructing a location-indexed physical channel map. The physical channel map is queried based on the real-time location information of the user terminal, and after acquiring the corresponding physical channel fingerprint, it is converted into the required statistical channel information, enabling detection-free acquisition of statistical channel information.

[0045] The following is a detailed description of the specific implementation process of the large-scale MIMO-OFDM physical channel fingerprint and channel map construction and channel acquisition method involved in the present invention, combined with a specific communication system example. It should be noted that the method of the present invention is not only applicable to the specific system model given in the example below, but also to system models with other configurations.

[0046] 1. Physical Channel Fingerprint

[0047] Consider Figure 1In the propagation environment shown in the figure, a base station (BS) communicates with several user terminals (UT) on the ground. Assuming that both the user and base station sides are equipped with uniform linear arrays (ULA), the propagation environment at this time can be mapped into a two-dimensional space. As can be seen from the figure, the propagation environment between the BS and the UT can be described by several physical propagation path clusters, each cluster containing several multipath components (MPC) with similar characteristics. Each cluster corresponds to a propagation path in the environment, and each MPC corresponds to a sub-path in the path. Figure 2 In the example, the current scene is divided into several grids. For the convenience of analysis, this embodiment assumes that the propagation environment in each grid area is the same. In the subsequent description, s BS and s represent the grid locations of BS and UT respectively, Represents the collection of all meshes.

[0048] Based on the above configuration, assuming that the UT's motion area is located in the same grid during the analysis time, the channel impulse response (CIR) between the ath antenna element on the BS side and the bth antenna element on the UT side can be expressed as

[0049]

[0050] Where L(s) represents the number of MPCs between BS and UT, and They represent the complex gain and delay corresponding to the lth MPC, f c represents the carrier frequency. Furthermore, MPCs with similar characteristics are grouped into a cluster, and (1) can be further written as

[0051]

[0052] in and denote the complex gain and delay corresponding to the lth MPC in the Pth cluster, P(s) denotes the number of clusters between BS and s, and L p (s) represents the number of MPCs in the current cluster, For the uplink, the angle of departure (AoD, Angel of Departure) and angle of arrival (AoA, Angel of Arrival) of the current subpath are respectively and Then, under the far-field assumption, the time delay can be approximated as

[0053]

[0054] where d BS and d UT Represents the array element spacing on both sides of BS and UT, is the propagation delay between BS and location s, c represents the speed of light, v move and α move Denote the terminal's moving speed and moving direction respectively. Substituting (3) into (2), the CIR can be further expressed as

[0055]

[0056] The Doppler shift where λ is the carrier wavelength of the system.

[0057] Considering the uplink under the MIMO-OFDM architecture, N c ,N g They represent the number of subcarriers and the length of the cyclic prefix, respectively, and Δf represents the subcarrier spacing. Assume that in a symbol time T sym The channel state information remains unchanged within the symbol, but changes due to the Doppler effect between symbols. Under this assumption, the channel response on the kth subcarrier of the nth OFDM symbol can be obtained as

[0058]

[0059] For any Defining Vectors in The definition of is given in (4), which represents the gain of the current MPC. Then when the number of clusters P(s) and the number of MPCs in the cluster L p (s) and each MPC corresponds to When θ is known, the channel response corresponding to the current position can be reconstructed. α =[θ AoD ,θ AoA ,τ] T represents an arbitrary angle-delay domain vector, and ν = v move cos(α move -θ AoD ) / λ represents the Doppler frequency shift, (5) can be written as

[0060]

[0061] in Represents the channel function in the angle-delay domain.

[0062] make represents the power distribution function of the channel between BS and s in the angle-delay domain. As a type of statistical CSI, this function systematically describes the physical propagation characteristics of the current location, which can also be converted into various types of statistical CSI for channel transmission. Therefore, the set of all location labels And S corresponding to all positions s (Θ α ) can form the channel map corresponding to the current scene, which stores the physical channel information of different locations.

[0063] In practical applications, as a type of multidimensional function, S s (Θ α ) cannot be stored directly. Therefore, this embodiment approximates it and stores S s (Θ α ) is approximately expressed as

[0064]

[0065] in represents the average power of the p-th cluster, Represent the probability distribution functions of AoD, AoA and delay of the MPC in the current cluster, which can usually be fitted using Gaussian distribution. For example, the function can be approximated as

[0066]

[0067] in and Represent the mean and variance of the current cluster AoD respectively. Similarly, It can also be expressed in a similar form, with the characteristic function being and So far, the following vectors are defined:

[0068] Contains the physical statistical characteristics of the p-th cluster at the current position. Let the set but Summarizes all the physical statistical characteristics between the current location s and the BS. When the mobility of UT is known, according to The information provided can be used to infer the distribution characteristics of the channel in the Doppler domain. s (Θ α )use Instead, the physical channel fingerprint (PCF) of the current location is used to effectively compress the storage space of the channel fingerprint.

[0069] The PCF in the embodiment of the present invention is for Figure 2 The scenario shown is specifically defined for this purpose, but the method can be extended to more complex scenarios. For example, in a 3D propagation scenario, the elevation parameter can also be included. Regardless of the scenario, the design philosophy of PCF is to fully characterize the physical characteristics of a complex channel environment through the most streamlined set of parameters.

[0070] 2. PCF Generation Method in Massive MIMO-OFDM Architecture

[0071] consider Figure 2 In the propagation scenario, assume that the antenna dimensions of the BS and UT are A×1 and B×1 respectively. Select K valid subcarriers along the subcarrier domain and let their subcarrier number set Along the time dimension, the time resources are divided into several subframes, each subframe contains N s OFDM symbols, and further divide these subframes into N p time slots, each time slot consists of N b OFDM symbols, then there are N s =N p N b The first OFDM symbol in each time slot is used to send the pilot signal. In the process of channel detection, the pilot part of each frame has a total of N OFDM symbols. p indivual.

[0072] In order to study the generation of PCF, the embodiment of the present invention first gives the relationship between the spatial-frequency-time (SFT) domain channel and the parameters in PCF based on the above frame structure. Analyze frame by frame, assuming that the sequence number of the current time slot is Then the current time slot and its previous (N p -1) time slots together constitute the current frame. Then the OFDM symbol number contained in the pilot segment in this frame is Then we can define A×K×N p ×B-dimensional tensor represents the SFT domain channel along all valid subcarriers in the OFDM symbol of the pilot segment of the current frame between the BS and the UT at position s, where

[0073]

[0074] make Represents the vector form of the SFT domain channel, then

[0075]

[0076] The rudder vector of the SFT domain satisfy

[0077]

[0078] When the spatial broadband effect can be ignored, the above steering vector can be simplified to

[0079]

[0080] The space rudder vector on the AoA side is and The frequency domain rudder vector is and The space rudder vector on the AoD side is and The time rudder vector is and These steering vectors represent the mapping relationship from the angle domain, delay domain to the space-frequency-time domain respectively.

[0081] The above channel model establishes the relationship between the SFT domain channel and the MPC parameters, that is, through the mapping between the angle domain, time domain and SFT domain, the effects of different MPCs in the angle-delay domain are mapped to the SFT domain respectively, and the effects of different MPCs in the SFT domain are accumulated to obtain the final representation of the SFT domain channel. Since PCF is the statistical characteristic of the MPC parameters, it is necessary to obtain sufficient SFT domain channel samples through multiple measurements, separate the effects of each MPC from the channel samples, and then extract the MPC parameters and extract their statistical characteristics. First, an embodiment of the present invention provides a method for obtaining SFT domain channel samples. This embodiment selects a two-dimensional time-frequency joint phase-shifted pilot (TFPSP, Time-Frequency Phase-ShiftedPilot) as the detection pilot signal. When the UT end antenna array simultaneously sends the detection pilot to the BS side, the received signal model can be expressed as

[0082]

[0083] in and Indicates the received signal of the ath antenna element on the BS side at the kth effective subcarrier of the nth pilot segment OFDM symbol in the current frame. Operator × n Represents the n-modular product between a matrix and a tensor. and is a diagonal matrix. Let Then the pilot signal sent by the bth antenna array element on the kth effective subcarrier on the nth pilot segment OFDM symbol is The design of TFPSP is to modulate the Zadoff-Chu sequence using a phase-shifted pilot, that is,

[0084]

[0085] where φ b and Represent the phase shift modulation factors in frequency domain and time domain, γ f and γ t represents the roots of the Zadoff-Chu sequence, respectively with K and N p are relatively prime. Let ν max =2v move / λ、 as well as When B≤K f K t When the phase shift modulation factor can be set to

[0086]

[0087] The operators <> and They represent the modulo operation and floor operation respectively. f K t When the BS receives the signal, the Zadoff-Chu sequence with different roots can be generated as the root sequence to increase the pilot capacity. estimate

[0088] In the estimated Afterwards, it is necessary to extract a sufficient number of MPC parameter samples from the channel data for statistical analysis to obtain the physical statistical characteristics of the channel. Extracting MPC parameters can be solved by constructing an optimization problem. Specifically, the MPC parameters are searched to minimize the mean squared error between the SFT-domain channel obtained by summing the MPC effects in the SFT domain and the SFT-domain channel samples obtained by detection, thereby obtaining the desired MPC parameters.

[0089] make Indicates pending evaluation The optimization problem can be expressed as finding the MPC parameter group such that

[0090]

[0091] The function vector Θ=(α s ,θ AoD ,θ AoA,τ) represents any set of MPC parameters. This problem can be solved using the Space Alternating Generalized Expectation-Maximization (SAGE) algorithm or a compressed sensing algorithm. Since the number of MPCs is unknown in actual parameter estimation, it is usually set based on the channel environment. In scenarios with abundant multipath scattering, this value is often set larger. During the solution, the above algorithm can be used to split the original optimization problem into several sub-problems, thereby converting the parallel multipath component parameter extraction problem into a serial, sequential extraction of multipath components, reducing the dimensionality of the optimization problem.

[0092] Here we take SAGE as an example to illustrate the solution of the optimization problem, converting the original parallel multi-dimensional parameter search problem into a sequential estimation When estimating the first group of MPC parameters, let

[0093]

[0094] The optimization problem is

[0095]

[0096] The SAGE algorithm performs steps (19)-(20) until the estimated After grouping the MPC parameters, the Kernel-Power-Means (KPM) algorithm is used to cluster them, generating MPC parameter samples for each cluster. The number of clusters can be determined using the Davies-Bouldin (DB) criterion. After obtaining the MPC parameter samples for each cluster, a Gaussian distribution function is used to fit the angle and delay parameter distributions to obtain the PCF. Within the current cell's coverage area, the PCFs corresponding to different grid locations are associated with the grid coordinates and stored to generate a PCF map.

[0097] 3. Channel Information Acquisition Method Based on PCF Spectrum

[0098] After obtaining the PCF at different positions and constructing the PCF map, it can be used as reference information to generate the required sCSI for channel acquisition and precoding under a given system configuration and UT mobility. In practical applications, sCSI usually refers to the sCSI under the beam-based channel model (BBCM), including the power distribution vector of the one-dimensional spatial beam domain, the power distribution matrix of the two-dimensional space-frequency beam domain, or the power distribution tensor of the triple space-frequency-time beam domain. This embodiment provides a modeling method for the triple BBCM in the scenario of B=1, and thereby provides a detailed method for generating the triple beam domain power distribution tensor. Specifically, by quantizing the angle cosine, delay, and Doppler domains, the space, frequency, and time beam domains are obtained respectively, and the SFT domain channel between the BS and the UT is represented as an A×K×N p The tensor of

[0099]

[0100] Where V s 、V f and They represent the rudder matrices in the space, frequency and time domains respectively. Under the beam basis model, these rudder matrices can be generated with the help of elementary transformations of the DFT matrix. is the channel tensor in the triple beam (TB) domain, satisfying in

[0101]

[0102] Indicates that in the current The channel gain within the beam range is Indicates the first beam intervals.

[0103] In practical environments, since the number of propagation paths is limited, the channel usually exhibits sparse characteristics in the beam domain.

[0104] Define the power distribution tensor of the TB domain Right now Given its sparse characteristics and the fact that sCSI changes slowly over time, It is an important type of sCSI, which is often used in multiple links such as channel acquisition, precoding design and power allocation. According to the definition of the covariance matrix, the channel covariance matrix in the SFT domain is In a massive MIMO-OFDM system, assuming that the channel gains of different beams satisfy independent cyclic symmetric complex Gaussian distributions, we have

[0105]

[0106] Established, of which It should be noted that the power distribution vector in the spatial beam domain and the two-dimensional power distribution matrix in the space-frequency domain can both be used as channel fingerprints, but the sCSI under triple BBCM, i.e. It cannot be used directly as a channel fingerprint because it also contains the channel characteristics in the Doppler domain, which is determined by the physical channel characteristics of the current location and the mobility of the UT. Therefore, when the PCF at the s position and the UT mobility are known, Perform deduction to avoid online detection The expenses caused.

[0107] The following is given by using PCF and UT mobility to generate The method mainly includes the following steps: generating an SFT domain covariance matrix by using the relationship between the SFT domain channel covariance matrix and the angle-delay domain power distribution; The algorithm is split into a real tensor and its own dot product form, and the real tensor is used as the solution object. With the help of the mapping relationship between the SFT domain channel and the TB domain channel, another SFT domain covariance matrix is ​​generated. In order to measure the difference between the above two types of SFT domain covariance matrices, the two covariance matrices are transformed with the help of the rudder matrix V to obtain two tensors of the same dimension. An optimization problem is established, and minimizing the Kullback-Leibler (KL) divergence between the above two tensors is used as the optimization goal. The gradient descent method is used to solve the optimization problem, and the corresponding solution when the algorithm converges is obtained. This solution is a real tensor. The dot product operation is performed on the real tensor with itself, and the tensor obtained is the required TB domain sCSI.

[0108] The above method will be described below with reference to specific embodiments. When its corresponding PCF, that is, It is known that different clusters correspond to as well as Can be obtained, and then the power distribution function S in the AAD domain can be obtained s (Θ α ). With the relationship between the SFT domain channel and the AAD domain channel parameters, and the approximate assumption that different paths in the massive MIMO-OFDM system are uncorrelated, the S s (Θ α )and The following relationship is satisfied between

[0109]

[0110] According to (23) and (24), the following steps can be used to obtain the beam domain sCSI with the help of PCF: First, according to the UT location information s, the PCF corresponding to the current location is obtained by searching the PCF map, that is, According to PCF, S is calculated s (Θ α ), and then substitute into (24) to obtain the covariance matrix With the help of beam domain sCSI and The relationship between , looking for a non-negative real tensor Make Established.

[0111] In order to solve the non-negative real tensor that meets the requirements We transform the original problem as follows: According to its non-negativity, It can be split into the dot product form of two real tensors, namely Will solve Convert to solve Satisfy

[0112]

[0113] In order to make the values ​​at both ends of (25) as close as possible, this embodiment sets the values ​​at both ends of (25) to The vector is rearranged into two Tensors, respectively and To measure and In order to obtain the similarity of , this embodiment introduces the Kullback-Leibler (KL) divergence. and The KL divergence between can be expressed as

[0114]

[0115] The smaller the KL divergence above, the more and The higher the similarity, the better. The problem is finally transformed into the following optimization problem

[0116]

[0117] During the implementation process, It can be calculated as follows:

[0118]

[0119] in

[0120] T s=((V s ) H V s )⊙((V s ) H V s ) * (29)

[0121] T f =((V f ) H V f )⊙((V f ) H V f ) * (30)

[0122]

[0123] Problem (27) is a convex problem, so it can be solved by gradient descent method, that is, in the dth iteration, we have

[0124]

[0125] where δ (d) Indicates the iterative step size. In order to prevent divergence caused by too fast convergence speed, it can usually be continuously reduced as the number of iterations increases. The gradient calculation formula is

[0126]

[0127] Tensor satisfy When the iteration converges, a real tensor that satisfies the conditions can be obtained. Then the estimated value of TB domain sCSI is

[0128] The above method can also be extended to other types of sCSI acquisition. Based on the mapping relationship between the spatial domain / spatial-frequency domain covariance matrix and the power distribution vector of the one-dimensional spatial beam domain / the power distribution matrix of the two-dimensional spatial-frequency beam, sCSI acquisition is established as an optimization problem similar to (27) to minimize the difference between the spatial domain / spatial-frequency domain channel covariance matrix calculated by the physical channel fingerprint and the spatial domain / spatial-frequency domain covariance matrix calculated by the power distribution vector of the one-dimensional spatial beam domain / the power distribution matrix of the two-dimensional spatial-frequency beam domain to achieve the acquisition of optimal statistical channel information. The above differences can be measured with the help of Kullback-Leibler divergence, and the optimization problem is solved using the gradient descent method.

[0129] IV. Implementation Effect

[0130] In order to enable those skilled in the art to better understand the solution of the present invention, the performance results of the channel information acquisition method in this embodiment under specific configurations are given below.

[0131] Consider a massive MIMO-OFDM system with the following system parameters: carrier frequency f c =5.8GHz, number of subcarriers N c =2048, cyclic prefix length N g =144, subcarrier spacing Δf = 15kHz, effective subcarrier number K = 360, number of antenna elements at the base station side M = 128, element spacing is half a wavelength, number of users in the system U = 300, number of time slots in a frame along the time domain N p =8, the number of OFDM symbols in a single time slot N b =14, user terminal moving speed 15km / h. Oversampling factor F ν =4. The embodiment of the present invention adopts a 6G universal channel model (6GPCM) generator to generate channel data. The specific implementation steps are as follows: First, based on the PCF generation method, a PCF map corresponding to the current cell is constructed; then, according to the proposed technical solution, the PCF is converted into sCSI under BBCM under a specific system configuration and UT mobility state. In order to verify the accuracy of the generated sCSI, it is compared and analyzed with the sCSI obtained by the traditional online detection method. Both sCSIs are used in the channel estimation process. If their channel estimation performance is equivalent, it is confirmed that the sCSI generated based on PCF can effectively replace the online detection sCSI.

[0132] The specific simulation process includes two key steps: extracting the SFT domain channel from the channel measurement results and generating a PCF; and using the PCF to generate corresponding sCSI based on the UT's current position and mobility to assist in channel acquisition. It is important to note that the PCF is configuration-independent and supports sCSI generation under any antenna configuration. During the channel measurement phase, to improve spatial resolution and ensure angle estimation accuracy, both the BS and UT are equipped with multi-antenna arrays. In this embodiment, a 32×1 uniform linear array is used on the UT during PCF generation. After the PCF database is constructed, this embodiment verifies the effectiveness of PCF-based sCSI generation in a scenario where multiple single-antenna UTs communicate with the BS.

[0133] In this embodiment, channel estimation is performed in the SFT domain and MPC parameters are extracted according to the method, and PCF is generated with a grid density of 1m×1m. After the PCF generation for all location points is completed, a PCF database based on location index is constructed. To verify the practical value of PCF and the effectiveness of the proposed sCSI generation method, the sCSI generated by PCF is compared with the sCSI obtained by traditional online detection methods. Traditional sCSI acquisition methods based on online detection (such as sparse Bayesian methods based on compressed sensing (CS)) model sCSI as sparse signals and have been proven to have high accuracy. Therefore, this method is used to generate SCF as a comparison benchmark.

[0134] This example considers a massive MIMO-OFDM channel acquisition scenario with U = 180. UTs are initially randomly distributed within the scenario area, moving at a speed of 15 km / h, and with randomly selected directions between 0 and π. Simulations are conducted in two typical scenarios, "3GPP_38.901_UMa_NLOS" and "3GPP_38.901_RMa_NLOS." The feasibility of PCF-based sCSI generation is verified by applying both the online detection method and the PCF-based sCSI generated by the proposed method to the channel acquisition process.

[0135] During the channel acquisition process, both pilot design and channel estimation require sCSI as prior information. Since channel acquisition performance depends on the accuracy of sCSI, it directly reflects the precision of sCSI. Normalized mean square error (NMSE) is used as an evaluation metric. Figure 4 The NMSE performance comparison of different sCSI-assisted channel acquisition in two scenarios, UMa and RMa, is demonstrated, including: SF Probing: space-frequency (SF) beam domain sCSI obtained based on online detection; SFPCF-based: SF beam domain sCSI generated from PCF by the proposed method; TB Probing: TB domain sCSI obtained based on online detection; TBPCF-based: TB domain sCSI generated from PCF by the proposed method. The NMSE comparison of the four sCSIs fully verifies the feasibility of generating multi-dimensional sCSI from PCF. The simulation results show that: whether it is SF beam domain or TB domain sCSI, the sCSI generated based on PCF and the sCSI obtained based on online detection are similar in channel acquisition NMSE performance. In addition, triple BBCM exhibits stronger Doppler domain channel feature capture capability compared to double BBCM. Figure 4 As shown in Figure 3, since TB-domain sCSI can more comprehensively characterize multi-domain channel characteristics, it can achieve better NMSE performance than SF-beam-domain sCSI in channel acquisition.

[0136] In summary, the present invention first proposes a method for defining physical channel fingerprints. With the help of a cluster-based geometric channel model, a set of low-dimensional parameters is used to comprehensively characterize the physical propagation characteristics of the channel. Secondly, a method for constructing physical channel fingerprints for large-scale MIMO-OFDM systems is proposed. The relationship between physical channel parameters and space-frequency-time domain channels is utilized to extract physical channel fingerprints from channel measurement samples in the space-frequency-time domain. The physical channel fingerprints are then associated with location information and stored to construct a location-indexed physical channel map. Finally, a channel information acquisition method based on the physical channel map is proposed. By querying the physical channel fingerprint corresponding to the user terminal location in real time and combining the terminal mobility characteristics, beam-domain statistical channel information adapted to the current system configuration is generated with low complexity and used as prior information to achieve efficient instantaneous channel estimation. Experiments show that the present invention overcomes the limitations of existing channel fingerprint technology through physical channel fingerprint characterization. By replacing traditional online detection methods with physical channel maps, the system channel detection overhead is effectively reduced and the efficiency of channel information acquisition is improved.

[0137] Based on the same inventive concept, an embodiment of the present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the computer device implements the method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map, and / or a method for acquiring massive MIMO-OFDM channel information based on a physical channel map.

[0138] In a specific implementation, the device includes a processor, a communication bus, a memory, and a communication interface. The processor can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. The communication bus can include a path for transmitting information between the above components. The communication interface uses any device such as a transceiver for communicating with other devices or communication networks. The memory can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical storage, a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these. The memory can be independent and connected to the processor via a bus. The memory can also be integrated with the processor.

[0139] The memory is used to store application code for executing the solution of the present invention, and the execution is controlled by the processor. The processor is used to execute the application code stored in the memory, thereby implementing the method provided by the above embodiment. The processor may include one or more CPUs, or may include multiple processors, each of which may be a single-core processor or a multi-core processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0140] An embodiment of the present invention also discloses a computer program product, including a computer program. When the computer program is executed by a processor, it implements the method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map, and / or a method for obtaining massive MIMO-OFDM channel information based on a physical channel map.

[0141] The program code for implementing the inventive method can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that the program code, when executed by the processor or controller, causes the steps of the inventive method to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or completely on a remote machine or server. The present invention is not described in detail herein, and all of these are known techniques to those skilled in the art.

[0142] In the embodiments provided herein, it should be understood that the disclosed methods may be implemented in other ways without departing from the spirit and scope of the present application. The present embodiments are merely illustrative examples and should not be construed as limiting. The specific details provided herein should not limit the purpose of the present application. For example, some features may be omitted or not implemented.

[0143] The technical means disclosed in the solutions of the present invention are not limited to those disclosed in the above-mentioned embodiments, but also include technical solutions composed of any combination of the above-mentioned technical features. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map, characterized in that: The steps include: Divide the target communication area into grids and establish location coordinate indexes; For each location grid, physical characteristic parameters are extracted based on the acquired channel samples to construct a physical channel fingerprint. The physical parameters of each multipath component in each channel sample are clustered to obtain a physical propagation path cluster between the transmitter and receiver. The physical characteristic parameters of a single physical propagation path cluster include: cluster power, mean and standard deviation of the departure angle of the multipath components within the cluster, mean and standard deviation of the arrival angle of the multipath components within the cluster, and mean and standard deviation of the delay of the multipath components within the cluster. The physical channel fingerprint is the set of physical characteristic parameters corresponding to all physical propagation path clusters between the current user terminal location and the base station location. Each location coordinate is associated with its corresponding physical channel fingerprint and stored to construct a location-indexed physical channel map.

2. The method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map according to claim 1, wherein: The construction of the physical channel fingerprint includes: Obtain multiple channel samples within the current location area; Extract the path gain, departure angle, arrival angle, and delay of each multipath component in each channel sample; cluster multipath components with similar angle-delay characteristics into propagation path clusters; The statistical characteristics of the multipath parameters in each propagation path cluster are analyzed, and the characteristic parameters of all propagation path clusters are combined to form a complete physical channel fingerprint for the location.

3. The method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map according to claim 1, wherein: The channel samples are obtained through uplink detection, including: all antenna array elements configured in the mobile terminal periodically send time-frequency two-dimensional pilot signals to the base station, and after the base station receives the signal, an estimation algorithm based on information geometry or a sparse Bayesian method is used to estimate the three-dimensional channel in the space-frequency-time domain, thereby obtaining channel samples.

4. The method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map according to claim 1, wherein: The physical parameters corresponding to the multipath components are extracted. After estimating the channel, the physical channel parameters corresponding to the multipath components are used as the object to be solved, and an optimization problem is constructed so that the mean square error between the space-frequency-time domain channel samples reconstructed by different multipath components and the space-frequency-time domain channel samples obtained by detection is minimized. The constructed optimization problem is solved using a spatial alternating generalized expectation maximization algorithm or a compressed sensing algorithm.

5. A method for acquiring massive MIMO-OFDM channel information based on a physical channel map, characterized in that: The steps include: The method for constructing a massive MIMO-OFDM physical channel fingerprint and channel map according to any one of claims 1 to 4, constructing a physical channel map; Query the physical channel map based on the location information of the user terminal to obtain the corresponding physical channel fingerprint; Based on the physical channel fingerprint and in combination with the terminal mobility characteristics, statistical channel information is generated; the statistical channel information is used as prior information to assist instantaneous channel estimation.

6. The method for acquiring massive MIMO-OFDM channel information based on physical channel maps according to claim 5, characterized in that: The statistical channel information is the beam domain channel power distribution, including a one-dimensional spatial beam domain power distribution vector, a two-dimensional spatial-frequency beam domain power distribution matrix, or a triple space-frequency-time beam domain power distribution tensor; wherein the spatial beam domain, the frequency beam domain, and the time beam domain are respectively regarded as quantized samplings of the angle domain, the delay domain, and the Doppler domain, and the power distribution in the beam domain is regarded as a quantized form of the angle-delay domain continuous power distribution function.

7. The method for acquiring massive MIMO-OFDM channel information based on physical channel maps according to claim 5, characterized in that: The generating of the statistical channel information includes: Generates the power distribution function in the angle-delay domain based on the physical channel fingerprint. Taking into account the mobility of the user terminal, calculates the channel covariance matrix in the spatial domain, space-frequency domain, and space-frequency-time domain. Based on the mapping relationship between the spatial domain / spatial-frequency domain / spatial-frequency-time domain covariance matrix and the one-dimensional spatial beam domain power distribution vector / two-dimensional spatial-frequency beam domain power distribution matrix / triplet spatial-frequency-time beam domain power distribution tensor, the acquisition of statistical channel information is formulated as an optimization problem to minimize the difference between the spatial domain / spatial-frequency domain / spatial-frequency-time domain channel covariance matrix calculated by the physical channel fingerprint and the spatial domain / spatial-frequency domain / spatial-frequency-time domain covariance matrix calculated by the one-dimensional spatial beam domain power distribution vector / two-dimensional spatial-frequency beam domain power distribution matrix / triplet spatial-frequency-time beam domain power distribution tensor to achieve optimal statistical channel information acquisition.

8. The method for acquiring massive MIMO-OFDM channel information based on physical channel map according to claim 7, characterized in that: Generation of triple beam domain statistical channel information, including: The physical channel fingerprint is searched based on the physical channel map, and the angle-delay domain power distribution function of the current location is restored based on the channel fingerprint. A space-frequency-time domain covariance matrix is ​​generated based on the relationship between the space-frequency-time domain channel covariance matrix and the angle-delay domain power distribution. The triple-beam domain power distribution tensor to be solved is split into a real tensor and its own dot product form. This real tensor is used as the solution object, and another space-frequency-time domain covariance matrix is ​​generated by using the mapping relationship between the space-frequency-time domain channel and the triple-beam domain channel. In order to measure the difference between the two space-frequency-time domain covariance matrices, the two space-frequency-time domain covariance matrices are transformed with the help of the space-frequency-time domain rudder matrix to obtain two tensors of the same dimension, an optimization problem is established, and the Kullback-Leibler divergence between the two tensors of the same dimension is minimized as the optimization goal. The optimization problem is solved using the gradient descent method to obtain the solution of the real tensor; A dot product operation is performed on the solution of the real tensor and itself, and the obtained tensor is the required triple beam domain statistical channel information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is loaded into a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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