Large-scale MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) channel acquisition method based on multiple groups of adjustable phase shift pilot frequencies

Abstract

The invention provides a large-scale MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) channel acquisition method based on multiple groups of adjustable phase shift pilot frequencies. According to the multi-group adjustable phase shift pilot frequency method, users are divided into a plurality of groups, each group uses the same basic pilot frequency sequence to generate a plurality of adjustable phase shift pilot frequencies, and different groups use different basic pilot frequency matrixes; wherein the autocorrelation matrix of the basic pilot matrix is a unit matrix, the sequence of diagonal elements of the cross-correlation matrix of different basic pilot matrixes after FFT transformation is sparse, and each non-zero complex element has the same argument. In the channel acquisition method, each user terminal sends a plurality of groups of known and scheduled adjustable phase shift pilot signals to the base station, and the base station performs preprocessing according to the received signals so as to complete channel estimation. The method provided by the invention can greatly improve the spectrum efficiency and the channel information acquisition precision of a large-scale MIMO-OFDM system, and has excellent performance especially in a communication scene with a large number of users and strong mobility.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and proposes a channel acquisition method based on multiple groups of adjustable phase-shift pilots for a large-scale MIMO-OFDM system. Background Art

[0002] In the evolution of wireless communication technology, massive Multiple-Input Multiple-Output (MIMO) has overcome the performance bottlenecks of traditional MIMO technology. This system has increased data rates within the same frequency band, laying a critical foundation for high-spectral-efficiency (SE) communications. At the modulation level, Orthogonal Frequency Division Multiplexing (OFDM), with its excellent resistance to frequency-selective fading and inter-symbol interference, has become a key technology in modern wireless communications.

[0003] By combining the advantages of both, massive MIMO-OFDM systems can further improve throughput, spectral efficiency, and communication reliability. These technological trends will not only significantly increase system peak rates, but also make massive MIMO-OFDM one of the core supporting technologies for 6G networks. However, this system is highly dependent on the accurate acquisition of channel state information (CSI) in spatial multiplexing mode. Especially in high-speed mobility scenarios, even if the channel reciprocity of time-division duplex (TDD) mode is utilized, the overhead of CSI acquisition still cannot meet real-time requirements, becoming a key bottleneck restricting system performance.

[0004] Currently, pilot-assisted channel estimation remains the mainstream method, with Phase Shift Orthogonal Pilots (PSOPs) widely used in massive MIMO-OFDM systems. Although orthogonal pilots effectively mitigate pilot contamination, their high pilot overhead makes them unsuitable for use in highly dynamic environments. Meanwhile, research based on channel sparsity has made significant progress. Compressed sensing algorithms exploit channel sparsity to achieve low-overhead CSI acquisition, but are often limited by computational complexity and the sparsity of the channel matrix. In contrast, pilot multiplexing offers a computationally efficient and low-overhead alternative. However, pilot multiplexing can introduce interference at the receiver, affecting CSI estimation accuracy. Therefore, pilot scheduling becomes a key technical issue. By leveraging the power concentration of the channel in the angular delay domain, pilot scheduling can significantly reduce or even eliminate interference. Building on this, Adjustable Phase Shift Pilots (APSPs) further enhance the effectiveness of non-orthogonal pilot multiplexing, improving the utilization efficiency of sparse channels through dynamic phase scheduling.

[0005] Although existing research has made some progress in reducing pilot overhead, most studies are still limited to exploiting the inherent sparsity of the channel, while ignoring the synergistic effect of pilot structure and channel characteristics. As wireless communications evolve towards ultra-dense networking, high spectral efficiency, and ultra-low latency, related application areas and scenarios are becoming increasingly diverse and complex. At the same time, the research value of angle statistics in channel estimation is becoming increasingly prominent. When user terminals are densely populated and signal transmission states switch frequently, the channel angle will exhibit non-uniform distribution characteristics. In addition, the first-order channel statistics in these emerging wireless scenarios exhibit multimodal distribution characteristics, further increasing the complexity of channel estimation. Therefore, the use of accurate and low-overhead channel estimation methods for the complex propagation environments in emerging applications has become a key issue in the current technological development trend.

[0006] To address the shortcomings of existing channel acquisition technologies and meet the future evolution and development needs of large-scale MIMO-OFDM systems, the present invention proposes a method for wireless communication systems with multiple sets of adjustable phase-shifted pilots, and also proposes a large-scale MIMO-OFDM channel acquisition method based on multiple sets of adjustable phase-shifted pilots. In the method, the characteristics of the basic pilot mutual correlation matrix are utilized to reduce inter-group pilot interference and improve the system's spectral efficiency. In the large-scale MIMO-OFDM channel acquisition method based on multiple sets of adjustable phase-shifted pilots, a large-scale MIMO-OFDM system model for emerging scenarios is first established, and an angular delay domain channel model with non-uniform angle distribution is constructed; a basic pilot sequence group is designed, and a scheduling algorithm for multiple sets of adjustable phase-shifted pilots is proposed; the base station uses statistical channel information and pilot angle information to preprocess the received signal, and then uses an MMSE estimator to achieve low pilot overhead channel estimation, thereby obtaining the channel information of each user. Compared with existing methods, the proposed channel acquisition method significantly improves the system's pilot capacity and channel information acquisition accuracy, and has superior performance. Compared with existing methods, the proposed channel acquisition method significantly improves the system's spectrum efficiency and channel estimation accuracy, and demonstrates superior performance in communication scenarios with dense users and high mobility. Summary of the Invention

[0007] Purpose of the invention: In response to the shortcomings of the existing technology of large-scale MIMO-OFDM systems, the purpose of the present invention is to propose a channel acquisition method based on multiple groups of adjustable phase-shifted pilots to improve the system's spectral efficiency and channel estimation accuracy, and adapt to the needs of emerging complex mobile communication scenarios.

[0008] Technical solution: In order to achieve the above-mentioned purpose, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides a method for multiple groups of adjustable phase-shifted pilots, comprising: in a large-scale MIMO-OFDM system, dividing users into multiple groups, using the same basic pilot matrix to generate multiple adjustable phase-shifted pilots in the same group, and using different basic pilot matrices in different groups; the autocorrelation matrix of the basic pilot matrix is a unit matrix, and the sequence of the diagonal elements of the cross-correlation matrices of different basic pilot matrices after FFT transformation is sparse, and each non-zero complex element of the sequence has the same argument; multiple groups of adjustable phase-shifted pilots are obtained by multiplying the basic pilot matrix with a phase shift factor, and the phase shift factor of each pilot can be repeated.

[0010] Preferably, the diagonal elements of the cross-correlation matrices of different basic pilot matrices, after FFT transformation, are optimally sparse sequences containing only a single non-zero element. One implementation method for the optimal sparse case is to obtain a basic pilot sequence group by performing different cyclic shifts on Zadoff-Chu sequences with the same root, where the basic pilot sequence is the diagonal elements of the basic pilot matrix.

[0011] In the second aspect, the present invention provides a large-scale MIMO-OFDM channel acquisition method based on multiple groups of adjustable phase-shifted pilots, including: in a large-scale MIMO-OFDM system, the spatial frequency domain channel is composed of multipaths of line-of-sight and non-line-of-sight wireless transmission, which are converted into the angle delay domain through array vectors and discrete Fourier transform matrices; each user's pilot signal is a multiple group of adjustable phase-shifted pilot signals, and the base station groups each user and schedules its corresponding adjustable phase-shifted pilot by utilizing the statistical channel information and pilot amplitude and angle information of each user in the angle delay domain; all users send known multiple groups of adjustable phase-shifted pilots to the base station, and the base station performs preprocessing based on the received signal, and then estimates the angle delay domain channel; the estimated angle delay domain channel is mapped to the spatial frequency domain to complete the channel estimation.

[0012] In which, the matrix elements of the angular delay domain channel have non-uniformly distributed angular radiation; the probability density function of the angular radiation is single-peaked or multi-peaked, and the position of the peak is related to the dominant angular radiation value of the line-of-sight and non-line-of-sight wireless transmission states, and the intermediate state when the two states are converted; when the dominant angular radiation values of the above three states are similar, the wrapped Gaussian distribution is used to describe the single-peak angular radiation distribution of the angular delay domain channel matrix elements, and the mean of the wrapped Gaussian distribution is the angular radiation value of the line-of-sight transmission state, and the variance is related to the difference in the dominant angular radiation values in the three states; when the variance of the wrapped Gaussian distribution is less than twice pi, a Gaussian distribution with the same mean and variance can be used instead.

[0013] The modulus and radiation angle of the angle delay domain channel matrix elements are independent, and each element in the matrix is also independent of each other, respectively representing the channel complex gain under the corresponding angle and delay; the angle delay domain statistical channel information includes power distribution and radiation angle distribution information; the power distribution of the angle delay domain channel is represented as a sparse matrix; the radiation angle distribution of the degree delay domain channel is represented as a matrix composed of the radiation angle distribution means of the corresponding channel elements; the pilot radiation angle information is the radiation angle of the non-zero complex elements of the diagonal element sequence of the cross-correlation matrix of different basic pilot matrices after FFT transformation.

[0014] As a preferred method, the angle delay domain pilot correlation matrix is quickly solved through the space frequency domain pilot correlation matrix and the Toeplitz characteristics of the matrix; the DFT / IDFT transform of the diagonal elements of the space frequency domain pilot correlation matrix corresponds to the first column / first row elements of the angle delay domain pilot correlation matrix respectively; the angle delay domain pilot correlation matrix is a Toeplitz matrix, and the entire matrix can be determined by a certain column or a certain row element.

[0015] In a third aspect, the present invention provides a massive MIMO-OFDM pilot scheduling method based on multiple sets of adjustable phase-shifted pilots. Based on the designed multiple sets of adjustable phase-shifted pilots, the base station schedules pilots for each user in the system according to the following method:

[0016] For each unscheduled user, pilot scheduling is performed within different groups to ensure that the angle delay domain equivalent power distributions of users in the same group do not overlap or the overlap is less than a certain threshold. Based on the intra-group scheduling of each group, the user is assigned to the group with the lowest overlap.

[0017] The angle delay domain equivalent power distribution refers to the channel matrix obtained by cyclically shifting all elements in the angle delay domain channel power distribution matrix to the right at the same time, and the shift length is determined by the phase shift factor of the pilot;

[0018] The overlap refers to the linear correlation between the two power distribution matrices in the angular delay domain. When the positions of the non-zero elements in the two power distribution tensors are completely staggered, the overlap is zero.

[0019] In a fourth aspect, the present invention provides a method for preprocessing received signals for massive MIMO-OFDM based on multiple sets of adjustable phase-shifted pilots. Based on the designed multiple sets of adjustable phase-shifted pilots, the base station preprocesses the received signals according to the following method to reduce pilot interference between different groups:

[0020] For interfering users outside the group to be estimated, multiply the pilot interference power matrix of the user to be estimated by the corresponding zero-angle matrix, then multiply it by the pilot angle information between the interfering group and the group to be estimated, and then add all of them together to obtain the equivalent pilot angle information matrix of the user to be estimated;

[0021] The received signal is point-multiplied by the argument zeroing matrix of the user to be estimated, and then the tangent value of the equivalent pilot argument information matrix is divided by the real part minus the imaginary part. The result is then used for channel estimation.

[0022] The angle zeroing matrix refers to the matrix after the angle mean of each element of the angle delay domain channel matrix is negated; the pilot interference power matrix refers to the equivalent power distribution of the interfering user in the angle delay domain after cyclic shift, and its phase shift factor is the difference between the pilot phase shift factors of the interfering user and the user to be estimated.

[0023] In a fifth 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 for obtaining multiple sets of adjustable phase-shifted pilots, the method for acquiring large-scale MIMO-OFDM channels based on multiple sets of adjustable phase-shifted pilots, the method for scheduling large-scale MIMO-OFDM pilots based on multiple sets of adjustable phase-shifted pilots, and / or the method for estimating large-scale MIMO-OFDM channels based on multiple sets of adjustable phase-shifted pilots.

[0024] In a sixth aspect, the present invention provides a massive MIMO-OFDM communication system, comprising a base station and multiple user terminals, the user terminals being used to send known multiple groups of adjustable phase-shifted pilots to the base station, the base station comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the multiple groups of adjustable phase-shifted pilot method, the massive MIMO-OFDM channel acquisition method based on the multiple groups of adjustable phase-shifted pilots, the massive MIMO-OFDM pilot scheduling method based on the multiple groups of adjustable phase-shifted pilots, and / or the steps of the massive MIMO-OFDM channel estimation method based on the multiple groups of adjustable phase-shifted pilots.

[0025] Beneficial effects: Compared with the prior art, the present invention has the following advantages: 1. The present invention proposes a method of multiple sets of adjustable phase-shifted pilots, which improves the spectrum efficiency of the system without increasing the pilot overhead. 2. The pilot scheduling method proposed in the present invention schedules corresponding pilot signals for different users in a large-scale MIMO-OFDM system with lower complexity, effectively suppresses interference between users, and improves channel estimation performance. 3. Based on the established channel model with non-uniform distribution of angular angles, the present invention proposes a channel estimation method with received signal preprocessing, which makes full use of the sparsity of the angular delay domain channel and the structural characteristics of the pilot, effectively reducing the complexity of large-scale MIMO-OFDM channel estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Schematic diagram of basic pilot mutual correlation matrix characteristics in an embodiment of the present invention.

[0027] Figure 2 This is a flow chart of a method for acquiring a massive MIMO-OFDM channel based on multiple sets of adjustable phase-shifted pilots according to an embodiment of the present invention.

[0028] Figure 3 2 is a structural diagram of the angle delay domain pilot correlation matrix in an embodiment of the present invention.

[0029] Figure 4 This is a comparison chart of the estimation errors of the channel acquisition method proposed in an embodiment of the present invention and the existing channel acquisition method.

[0030] Figure 5 The figure is a comparison of the spectrum efficiency of the channel acquisition method proposed in the embodiment of the present invention and the existing channel acquisition method. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described 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.

[0032] The method for generating multiple groups of adjustable phase-shifted pilots disclosed in an embodiment of the present invention includes: in a large-scale MIMO-OFDM system, dividing users into multiple groups, using the same basic pilot sequence to generate multiple adjustable phase-shifted pilots for the same group, and using different basic pilot matrices for different groups; wherein the autocorrelation matrix of the basic pilot matrix is a unit matrix, the diagonal elements of the cross-correlation matrices of different basic pilot matrices are sparse after FFT transformation, and each non-zero complex element of the sequence has the same argument; and multiple groups of adjustable phase-shifted pilots are obtained by multiplying the basic pilot matrix by a phase shift factor, and the phase shift factor of each pilot is repeatable.

[0033] Based on the multiple-group adjustable phase-shifted pilot method of this embodiment, a transmitting device simultaneously transmits multiple known groups of adjustable phase-shifted pilot signals to a receiving device; the receiving device estimates channel parameters using the received pilot signals. The optimal sparsity of the diagonal elements of the cross-correlation matrix of different basic pilot matrices after FFT transformation is a sequence containing only a single non-zero element. One method for achieving optimal sparsity is to perform different cyclic shifts on Zadoff-Chu sequences with the same root to obtain the basic pilot sequence groups.

[0034] Figure 1 The basic pilot correlation matrix characteristic diagram in the implementation of the present invention is given. When users are divided into Q groups, the kth user in the qth group q For users, the pilot matrix is expressed as where N c is the number of subcarriers in the massive MIMO-OFDM system, is the kth q The phase shift factor of each user, diag{x} represents the matrix with x as the diagonal element. The phase shift factor of each user can be the same, that is, there exists Is of length N c The discrete Fourier transform (DFT) matrix of Column, p xtr is the transmission power of the pilot signal, s q is the basic pilot sequence of the qth group. The basic pilot sequences of each group are different, but the length is N c , is the basic pilot matrix of the qth group and satisfies in(·) H Represents the conjugate operation of the matrix. Each group of users uses the same S q Generate adjustable phase-shifted pilots. The basic pilot matrices of different groups are different, i.e. S q ≠S q′ The correlation operation of the pilot matrix is expressed as Represents the cross-correlation of the basic pilot matrix. Since the basic pilot matrix S q There are many possible structures, space-frequency domain pilot cross-correlation matrix (SFPCM) There are also different structures. When the pilots in the same group are correlated When different groups of pilots are correlated Cannot be simplified.

[0035] For the basic pilot matrix S of multiple groups of adjustable phase-shifted pilots q , the optimal design condition is to satisfy represents the DFT transform of x, Only the nth element is non-zero, which is a sparse sequence used to describe ideal situation. and The more similar and sparse the matrix is, the more suitable it is for use as a basic pilot matrix. The phase angle of a complex number is defined as the argument of the complex number. Ideally, There is only one non-zero element value, which satisfies that each non-zero complex element has the same argument; if in non-ideal conditions, If the argument values of each non-zero complex element of are not equal, the basic pilot sequence is not suitable for generating multiple groups of adjustable phase-shifted pilots in this embodiment. For the mutual correlation matrix of the basic pilots within the group, Channel sparsity can be efficiently utilized. When the cross-correlation value is larger, The larger the number and width of the non-zero segments, the stronger the pilot interference. The Zadoff-Chu (ZC) sequence is expressed as in a> b represents the modulo operation of a on b, r represents the root index, N represents the sequence length, and φ∈[0,1,…,N-2] represents the number of self-cyclic shifts. Figure 1 (a) in the expression In the optimal sparsity case, (b) has poor sparsity, with a large non-sparse interval near the peak. (c) shows the situation when ZC sequences with different root indices are used as basic pilot sequences. Although there is no peak, the sparsity is destroyed, resulting in overall interference.

[0036] Select the ZC sequence with the root sequence of 1 and its own cyclic shift sequence as the basic pilot sequence group. and There is an equation At this time, the optimal condition of the basic pilot matrix is satisfied And a fixed angle constant appears This is also the pilot argument information of the q′th group relative to the qth group.

[0037] The specific effects of the multiple groups of adjustable phase-shifted pilot signals designed in the present invention are described in detail below in conjunction with a specific channel information acquisition method.

[0038] Figure 2 A simplified flowchart of a method for acquiring large-scale MIMO-OFDM channel information based on multiple sets of adjustable phase-shifted pilots disclosed in an embodiment of the present invention is provided. The method for acquiring large-scale MIMO-OFDM channel information based on multiple sets of adjustable phase-shifted pilots specifically includes: establishing a spatial frequency domain multipath channel model between different users and a base station, and converting it into an angular delay domain channel model with a non-uniform distribution of angles, wherein the spatial frequency domain channel is composed of multipaths of line-of-sight and non-line-of-sight wireless transmissions, and is converted into an angular delay domain through an array vector and a discrete Fourier transform matrix, and the elements of the angular delay domain channel matrix all have a characteristic of non-uniform distribution of angles; each user's pilot signal is a multiple set of adjustable phase-shifted pilot signals; the base station utilizes the statistical channel information and pilot amplitude and angle information of each user in the angular delay domain to group each user and schedule its corresponding adjustable phase-shifted pilot; all users send a known multiple set of adjustable phase-shifted pilots to the base station, and the base station performs preprocessing based on the received signals and then estimates the angular delay domain channel; the estimated angular delay domain channel is mapped to the spatial frequency domain to complete the channel estimation.

[0039] Specifically, the matrix elements of the angular delay domain channel have non-uniformly distributed radii, and the probability density function of the radii is single-peaked or multi-peaked. The location of the peaks is related to the dominant radii of the line-of-sight and non-line-of-sight wireless transmission states, as well as the intermediate states during the transition between the two states. When the dominant radii of the three states are similar, a wrapped Gaussian distribution is used to describe the single-peak radii of the angular delay domain channel matrix elements. The mean of the wrapped Gaussian distribution is the radii of the line-of-sight transmission state, and the variance is related to the difference in the dominant radii between the three states. When the variance of the wrapped Gaussian distribution is less than twice pi, a Gaussian distribution with the same mean and variance can be used instead. The modulus of the angular delay domain channel matrix elements is independent of the radii, and each element in the matrix is also independent of each other. The angular delay domain channel matrix is a sparse matrix, meaning it has only a few non-zero elements. The angle delay domain statistical channel information includes two types of information: power distribution and angular distribution. The power distribution of the angle delay domain channel is represented as a sparse matrix, and the angular distribution is represented as a matrix composed of the angular distribution means of the corresponding channel elements. The pilot angular information is the angular distribution of the non-zero complex elements after the FFT transformation of the diagonal element sequence of the cross-correlation matrix of different basic pilot matrices. In a specific embodiment, the angle delay domain pilot cross-correlation matrix can be quickly solved by using the spatial frequency domain pilot cross-correlation matrix and the Toeplitz property of the matrix. That is, the angle delay domain pilot cross-correlation matrix is a Toeplitz matrix, and the entire matrix can be determined by a column or row element.

[0040] The following describes in detail the specific implementation process of the large-scale MIMO-OFDM channel acquisition method based on multiple groups of adjustable phase-shifted pilots 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.

[0041] 1. System Configuration

[0042] Consider a single-cell TDD broadband massive MIMO wireless communication system. The central base station (BS) is equipped with a uniform linear array consisting of M antennas, with each antenna separated by half a wavelength λ. The cell accommodates K single-antenna user terminals (UTs). First, the K UTs are divided into Q groups, and the set of groups is represented as in Represents the group index. The qth UT set is represented as in and Denotes the UT index in group q. It is assumed that the channels of different UTs are statistically independent.

[0043] Using N cOFDM modulation scheme with N subcarriers, which can be achieved by c The point inverse discrete Fourier transform (IDFT) is implemented. The length of the cyclic prefix (CP) is N g (≤N c ). Define T sym =(N c +N g )T s and T c =N c T s are the system sampling durations including and excluding CP, respectively, where T s It is assumed that the CP duration of all user terminals is greater than the maximum channel delay.

[0044] 2. Channel Model

[0045] The embodiment assumes that the channel remains unchanged within an OFDM symbol period, but changes between different symbols. Based on the physical characteristics of the massive MIMO-OFDM channel model, the following problem description is constructed. In the uplink, define the kth group of qth q The distance between the user terminal and the mth antenna of the base station is OFDM symbols, nth c The channel response vector on the subcarriers is

[0046]

[0047] in represents the channel response vector, N p is the total number of paths. Denote the complex gain, direction cosine, and delay of the pth path, respectively. In particular, the complex gain of the Line of Sight (LoS) path is expressed as

[0048]

[0049] in is the real gain, d0 represents the distance from the user terminal to the base station. This embodiment defines the phase angle of the complex gain as the argument parameter. Taking the line-of-sight path as an example, its argument is For non-line-of-sight (NLoS) path gain In fact, the gain is relatively small and the amplitude is randomly distributed, which is in contrast to the line-of-sight path. The real gain and amplitude of are mainly affected by the line-of-sight path. By aggregating the channel response vectors of different subcarriers, the spatial frequency domain channel response matrix can be obtained: Its expression is as follows

[0050]

[0051] In massive MIMO-OFDM systems, computing high-dimensional matrices To simplify the calculation, we transform the array vector and the discrete Fourier transform matrix into Convert to the angle-delay domain for channel estimation.

[0052]

[0053] in Is of length N c The first N of the DFT matrix g List,(·) T represents the transpose operation of the matrix,

[0054]

[0055] is the kth q The user terminal is in The angular delay domain channel response matrix within symbols. The array response vector The specific expression is

[0056]

[0057] Using formula (4), the estimated channel can be converted to Therefore, we focus on analyzing the characteristics of the angle delay domain channel. The channel matrix element Defined as

[0058]

[0059] If we assume Each element is independent of each other, so arrive The transformation can be calculated element by element in the spatial frequency domain, as shown in formula (7). Considering a high-speed mobile scenario with a large number of user terminals, the frequent movement of the terminals will cause changes in the communication link state, namely the line-of-sight and non-line-of-sight wireless transmission states, and the switching of the intermediate state when the two states are converted. This frequent change will cause the channel radiation angle to be non-uniformly distributed, and the radiation angle values are concentrated near the dominant radiation angle values of the three states, thereby presenting a single-peak or multi-peak probability density function. Since the line-of-sight component dominates the channel change, it is assumed that the phase characteristics of the non-line-of-sight component are similar to those of the line-of-sight component. Under this assumption, the probability distribution of the received signal phase in the interval [0,2π) presents a single-peak characteristic. In order to approximate this phase distribution, a wrapped Gaussian distribution is used. Modeling is performed where the mean Related to the angle of sight component, variance Reflects the similarity of the angular difference between the non-line-of-sight component and the line-of-sight component. This embodiment takes the case of a single peak as an example, and the case of multiple peaks can be regarded as the superposition of multiple single peaks. Rewrite formula (7)

[0060]

[0061] in represents the real gain vector in the angular delay domain, the argument parameter Specifically, the angle is mainly concentrated around a specific mean, and its variance is less than 2π. Under this condition, the wrapped Gaussian distribution can be fully approximated as a Gaussian distribution. You can use a Gaussian distribution with the same mean and variance instead.

[0062] matrix It is closely related to the received signal delay and angle of arrival (AOA). According to the sparse characteristics of the massive MIMO-OFDM channel in the angular delay domain, from the perspective of statistical characteristics When the number of antennas is large enough, the channel power matrix in the angle delay domain can be established. The mapping relationship.

[0063]

[0064] in is the time difference between OFDM symbols, is the channel time correlation function, which is related to the Doppler frequency parameter v, E{·} represents the mathematical expectation operation, and ⊙ represents the matrix dot multiplication operation. Based on the Clarke-Jakes channel power Doppler spectrum model, the time correlation function can be concisely expressed as the first-kind zero-order Bessel function J0(·)

[0065]

[0066] In massive MIMO-OFDM channels, Each element represents the channel complex gain under the corresponding angle and delay, which has statistical independence, and the modulus and the angle distribution are independent, so the corresponding power matrix It shows sparse characteristics in the angular delay domain. It is composed of different independent elements, so the element-by-element estimation method can be used. The angle distribution of the angle delay domain channel is represented as a matrix composed of the mean of the angle distribution of the corresponding channel elements. Based on these channel characteristics, this embodiment can achieve high-precision channel estimation. The pilot angle information can be directly calculated when designing the basic pilot sequence. Assuming that the base station knows the power matrix of all user terminals and the angular distribution parameters.

[0067] 3. Channel Estimation

[0068] Assume that all user terminals are fully synchronized. In the uplink phase of each frame, all UTs are Pilot signals are sent simultaneously over multiple OFDM symbols, and the base station receives all pilot signals. Assume that the UTs are divided into Q groups, each of which is assigned a basic pilot matrix. This embodiment uses a different basic pilot matrix to generate APSPs for each group of UTs. When Q = 1, the multi-group APSP (MAPSP) channel acquisition method becomes an APSP-based method.

[0069] Base station The spatial frequency domain signal received on OFDM symbols can be expressed as

[0070]

[0071] in, represents the received signal matrix, is the spatial frequency domain channel response matrix, is an additive white Gaussian noise matrix whose elements satisfy independent and identical distribution. In the uplink pilot transmission phase, it is assumed that the noise obeys distribution, where p ntr Represents the noise power.

[0072] Base station receiving signal Contains the spatial frequency domain channel and pilot information of all user terminals, where Represents the received signal of the i-th antenna on the j-th subcarrier. If MMSE estimation is used directly to obtain channel information The computational complexity will be extremely high. Therefore, using the sparse characteristics of the channel, we first estimate the angular delay domain. Then, we can indirectly obtain the unitary equivalent relation in formula (4): Based on this, the base station receiving signal formula (12) can be restated in the uplink as

[0073]

[0074] Suppose we need to estimate the k'th q′ The channel information of each user terminal. After decorrelation and power normalization, we can obtain The least squares estimate of is expressed as follows:

[0075]

[0076] The third term on the right side of the equation Represents user terminal k' q′ In equation (14), the first term on the right side represents the pilot interference from the same group of user terminals, which can be simplified to The cyclic shift form of

[0077]

[0078] in

[0079]

[0080] yes Zero-filled extended form of I N and I N×L Represents the identity matrix of length N, and I N The first L elements of .

[0081]

[0082] is the cyclic shift matrix. q′ The pilot interference term can be understood as Circular shift right After units, intercept the first N g Column results.

[0083] The second term on the right side of Equation (14) represents the pilot interference from different user terminal groups. The specific expression of this interference term varies with the structure of the basic pilot matrix, and its general mathematical expression is as follows:

[0084]

[0085] in

[0086]

[0087] Defined as Angle-Delay domain Pilot Cross-correlation Matrix (ADPCM). From other user terminal group k q The pilot interference term can be regarded as Multiply with ADPCM and then intercept the first N g Column results. When satisfied When , the pilot interference generated by the user terminals in the same group can be regarded as a special case of Equation (18). In addition, the pilot noise term represented by the fourth term on the right side of Equation (14) can be proved to obey the cyclic symmetric complex Gaussian distribution by using the unitary transformation characteristics. The specific expression is

[0088]

[0089] in Indicates the The signal-to-noise ratio (SNR) on OFDM symbols, N nor is a normalized additive Gaussian white noise matrix whose elements are independent and identically distributed in

[0090] The received signal is decorrelated and the received pilot signal after decorrelation contains not only the channel to be estimated It also includes two types of pilot interference and additive white Gaussian noise. Substituting equations (15), (18) and (20) into equation (14), we can simplify and obtain

[0091]

[0092] in

[0093]

[0094] According to formula (9), since There is statistical irrelevance between the elements, and the pilot interference term and The elements of also satisfy the statistical uncorrelated characteristics. The power matrices of the pilot interference of the same group and different groups are defined as follows:

[0095]

[0096] in

[0097]

[0098] yes The zero-filled extended form of .

[0099] Using the received signal after decorrelation processing in formula (14) The minimum mean square error (MMSE) estimator can be obtained by element-by-element processing

[0100]

[0101] in

[0102]

[0103] definition is the user terminal k′ q′ The channel estimation error matrix can be established as follows:

[0104]

[0105] Obviously, the total MMSE estimation error for K user terminals can be obtained by accumulating the independent error terms:

[0106]

[0107] The accuracy of channel estimation is directly affected by the intensity of pilot interference. Pilot scheduling can minimize or eliminate pilot interference and approximate the lower bound of MMSE channel estimation error under optimal conditions, which is expressed as:

[0108]

[0109] The above formula can be regarded as an ideal case where all pilot interference is eliminated. Specifically, for the k'th q′ User terminals,

[0110] From a mathematical point of view, The sparsity of can be specifically expressed as follows: there are only c(≤N g ) column elements are non-zero valid values, and the rest are approximately zero. These non-zero column vectors themselves have only some non-zero elements, which intuitively reflects the sparse characteristics of the channel. When multiple user terminals use the same basic pilot matrix, the phase offset of the pilot can be adjusted during scheduling to reduce channel overlap. If k q′ ≠k′ q′ ,

[0111]

[0112] At this time, the pilot interference can be completely eliminated, thus achieving the lower bound of the error. When the sparse characteristics of the angle delay domain are fully utilized, the maximum number of user terminals that the system can support is

[0113] When using the MAPSP scheme, pilot interference can be divided into two categories: intra-group interference and extra-group interference. The intra-group interference characteristics are similar to those of the APSP generated by a single basic pilot matrix, while extra-group interference is mainly affected by ADPCM. q and k' q′ The general form of the SFPCM makes the following assumptions:

[0114]

[0115] where r i Representation matrix The value of the i-th diagonal element of depends on the basic pilot matrix used. Looking back at formula (19), the user terminal k q and k' q′ The ADPCM between

[0116]

[0117] Since the matrix It is a Toeplitz matrix, which means that except for the elements in the first row and first column, each element is equal to its adjacent upper left corner element. According to the characteristics of the Toeplitz matrix, we only need to calculate the first row element or the first column element You can confirm All elements of.

[0118]

[0119] in represents the IDFT transform of x. From equations (34) and (35), we can see that The first row and first column elements of the SFPCM matrix are derived from the diagonal elements of N c This method can effectively calculate all elements of ADPCM and help understand the impact of ADPCM on pilot interference.

[0120] From another perspective, we explain the construction principle of ADPCM and analyze its mechanism of mitigating pilot interference. ADPCM with different basic pilot matrices exhibits a cyclic shift characteristic with phase difference, as shown below.

[0121]

[0122] in

[0123]

[0124] is the ADPCM of the basic pilot matrix. Equation (37) fully describes the pilot interference generated by the MAPSP channel estimation method. The interference strength depends mainly on the degree of channel overlap, the choice of the basic pilot matrix, and the phase shift difference. This means that the inter-group pilot interference matrix has similar cyclic shift characteristics to the intra-group pilot interference matrix. Looking back at Equation (18), it can be restated as,

[0125]

[0126] The interference between the two groups comes from the ADPCM and Attachment Figure 3 This is a diagram showing the cross-correlation matrix structure of the angle delay domain pilots in the embodiment of the present invention. First observe the structural details of structure, The role of is to implement a circular right shift of the multiplied matrix, which means Can be regarded as through The result after column circular shift. No change The structural characteristics of The Toeplitz matrix properties are still maintained. Figure 3 The dotted boxes in correspond to equations (34) and (35), respectively, indicating that the Toeplitz matrix or Included All information of . In formula (37), The essence is interception Top N g The result of the operation of the column, so Top N g Column elements are defined as capturing elements. By It is calculated by multiplying the captured elements. Please note that Finally (N c -N g ) column is all zero, so The last (N c -N g ) row elements do not affect the calculation results.

[0127] In general, Only the first N g Row element influence These elements are called If this If all valid elements are zero, the pilot interference will also be zero. It is worth noting that Each column of is just a circular shift of the first column, so the value of the valid elements depends only on The first N g and N g elements. By reference The pilot interference can be calculated quickly. In addition, there is another case without pilot interference. When the pilot interference is non-zero and to be estimated When there is no overlap. Through phase scheduling, that is, adjusting Adjustable The position of the elements in To avoid pilot interference. In particular, when deploying the same basic pilot, At this time, Equation (37) will degenerate into intra-group interference (15).

[0128] Based on the above analysis, the following conclusions are drawn.

[0129] like is a zero matrix, then The fewer non-zero elements in First and last N g The more phase shift combinations there are, the more elements are zero at the same time.

[0130] like is a non-zero matrix, the fewer the non-zero elements in its effective elements, the closer the matrix is to the channel to be estimated. The lower the overlap.

[0131] 4. Signal Preprocessing and Pilot Scheduling

[0132] In this embodiment, the ZC sequence and the self-cyclic shift sequence are selected as the basic pilot sequence group, and the pilot amplitude angle information satisfies the relationship After substituting the angle delay domain basic pilot correlation matrix into equation (37), the pilot interference term can be reformulated as

[0133]

[0134] It can be seen that the interference between different groups has This characteristic coefficient is an important feature that distinguishes it from the interference within the same group. Combined with formula (8), The argument is a random variable Due to the existence of pilot amplitude and angle information, Generated The argument also obeys the Gaussian distribution. In other words, This causes the angles of the two user terminal channels to intersect, with only a small overlap in their concentrated areas. Therefore, intra-group interference and inter-group interference exhibit different statistical characteristics. Based on this characteristic of MAPSP, this embodiment proposes a received signal preprocessing method to mitigate inter-group pilot interference.

[0135] The base station preprocesses the received signal as follows: for interfering users outside the group to be estimated, the pilot interference power matrix of the interfering user to be estimated is multiplied by the corresponding angle zeroing matrix, and then multiplied by the pilot angle information between the interfering group and the group to be estimated. All are then added together to obtain the equivalent pilot angle information matrix of the users in the group to be estimated; the received signal is point-multiplied by the angle zeroing matrix of the interfering user to be estimated, and then the tangent value of the equivalent pilot angle information matrix is divided by its real part minus its imaginary part. The result is then used for MMSE channel estimation; the angle zeroing matrix refers to the matrix with the negative angle mean of each element of the angle delay domain channel matrix; the pilot interference power matrix refers to the equivalent power distribution of the interfering user in the angle delay domain after cyclic shift, and its phase shift factor is the difference between the pilot phase shift factors of the interfering user and the user to be estimated.

[0136] Specifically, the kth q The argument zeroing matrix of user terminals is defined as Its effect is to adjust the mean of the argument distribution to zero. In other words, The average value of the argument is zero. Then calculate the equivalent pilot argument information matrix between the q'th group of user terminal channels and other groups of channels

[0137]

[0138] Where arg{·} represents the angle calculation. is user k q For user k' q′ The pilot interference power matrix. After the received signal is preprocessed, the processed received signal can be expressed as:

[0139]

[0140] From a statistical point of view, the preprocessed signal It can effectively reduce the interference from other groups. To simplify the description, it is assumed that the angles of all channels are and For the kth q The user terminal is in The mathematical expectation of the channel vector on the OFDM symbol can be expressed as

[0141]

[0142] because but

[0143]

[0144] Finally, the preprocessed signal As the base station's received signal, MMSE channel estimation is performed. After preprocessing, inter-group interference is effectively suppressed. Under the premise of accommodating more user terminals, the MAPSP method in this embodiment has an estimation error similar to that of APSP. However, as the number of groups Q increases, the difficulty of reducing inter-group interference also increases. These errors mainly come from the fact that inter-group interference cannot be completely eliminated, and the residual interference comes from the limitations of the channel phase angle distribution assumption. When the channel parameters When decreases, the error introduced by preprocessing also decreases.

[0145] To address intra-group interference, leveraging the channel sparsity characteristics of the angular delay domain can effectively reduce or eliminate channel overlap, significantly reducing pilot interference. Interference intensity is affected by the channel power distribution of each user terminal, making pilot scheduling a key and advantageous optimization method. Before scheduling, the pilot angle information between groups must be confirmed. When this angle variance is greater than the angle variance of the angular delay domain channel matrix, the selected sequence meets the criteria for serving as the base pilot sequence group, and user scheduling can then be performed.

[0146] The base station schedules pilots for each user in the system according to the following method: for each unscheduled user, intra-group pilot scheduling is performed in different groups respectively, so that the angle delay domain equivalent power distributions between users in the same group do not overlap or the overlap is less than a certain threshold; based on the intra-group scheduling of each group, the users are assigned to the group with the lowest overlap; the angle delay domain equivalent power distribution refers to the channel matrix obtained by cyclically shifting all elements in the angle delay domain channel power distribution matrix to the right at the same time; the overlap refers to the linear correlation between the two power distribution matrices in the angle delay domain. When the positions of the non-zero elements in the two power distribution tensors are completely staggered, the overlap is zero.

[0147] For example, the pilot scheduling in this embodiment adopts the intra-group MMSE criterion. Since the phase shift of a single channel will affect other user terminals in the group, the optimal solution needs to traverse all possible situations of intra-group scheduling. By setting the intra-group threshold Y tra To balance the scheduling effect and computational complexity, the smaller the threshold, the more accurate the scheduling, and the more complex the corresponding calculation.

[0148] use The normalized dot product of is used to characterize the degree of overlap between channels. Channel superposition matrix It is used to describe the overlap of scheduled channels in group q. Groups and phase shift factors are assigned to each user terminal one by one, and the overlap is lower than the threshold. When all phases do not meet the threshold Ytra When , the factor with the smallest overlap is selected. Each user terminal will be scheduled in all groups in turn and finally assigned to the group with the smallest overlap, thus making full use of the inherent sparseness of the channel. The detailed steps of the MAPSP scheduling algorithm are as follows:

[0149] Step 1: Enter UT Collection Channel power matrix Scheduling threshold Y tra ; Initialize the unscheduled UT set Scheduled UT collection Phase within group Channel superposition matrix

[0150] Step 2: From Randomly select an unscheduled user k; for the qth group, traverse the phases in the group The overlap between the phase-shifted channel power matrix and the channel superposition matrix is calculated as follows:

[0151]

[0152] like Then stop traversing, otherwise continue traversing and record the phase shift factor with the smallest overlap; follow the process until all Q groups are traversed;

[0153] Step 3: Update the UT grouping and phase shift factor, compare the overlap of the UT in the Q group, and assign it the group and phase shift factor with the minimum overlap.

[0154] V. Implementation Effect

[0155] 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.

[0156] Consider a massive MIMO-OFDM system with the following system parameters: carrier frequency f c =6GHz, number of subcarriers N c =2048, cyclic prefix length N g =144, subcarrier spacing Δf = 15kHz, system bandwidth B = 20MHz, number of antenna elements at the base station side M = 128, element spacing is half a wavelength, and single symbol length T sym =71.4μs, sampling interval T s =32.6ns, Doppler coefficient vT sym =31.4×10 -3 , the antenna downtilt angle is 102°, and the user terminal moving speed is v speed=80km / h, the number of users in the system K=42, K=84, K=126, the scheduling algorithm threshold Y tra =10 -7 It is assumed that the transmissions of all user terminals are synchronized. It is determined by the similarity of the angular distribution of the non-line-of-sight component and the line-of-sight component. In the numerical simulation, the large-scale fading characteristics of the channel are not considered, and to simplify the calculation, the power of all user terminal channels is normalized to

[0157] Attachment Figure 4 The graph of the average MMSE changing with SNR is described, where 2-APSP and 3-APSP represent the MAPSP method when the number of groups is 2 and 3. Under the same number of user terminals, the MAPSP method in this embodiment shows a lower MMSE error, and it becomes more significant as the K value increases. When K = 84, the MMSE of 2-APSP is only slightly higher than that of APSP when K = 42. When K = 126, the APSP method almost fails in a high-speed mobile environment, while the MAPSP method can still maintain acceptable estimation accuracy. Therefore, when K increases exponentially, the MAPSP method shows a significant MMSE performance advantage, especially in high-speed mobile scenarios.

[0158] Attachment Figure 5 The curve of spectrum efficiency changing with SNR is described. The frame length is 7 OFDM symbols, i.e. 500μs. Uplink and downlink data transmission each occupies half of the data segment, and each contains 3 OFDM symbols. A single OFDM symbol is used for pilot transmission, and the pilot segment is located between the uplink and downlink data segments. This frame structure is very suitable for high-speed mobile scenarios, and the phase-shifted pilot method can effectively cope with rapid channel changes. Both uplink and downlink data are transmitted using MMSE receivers and precoders, and their signal-to-noise ratio is assumed to be the same as the pilot SNR. The channel prediction of the MAPSP method is expressed as

[0159]

[0160] The MMSE error of channel prediction is

[0161]

[0162] When pilot scheduling is performed based on the MMSE criterion of channel estimation, the MMSE error of channel prediction will be minimized. Figure 5 Under the same K value condition, the MAPSP method in this embodiment significantly improves the spectrum efficiency compared with the APSP method. This improvement is mainly due to two key factors that affect spectrum efficiency: the number of user terminals and the channel estimation accuracy. baseIt represents the benchmark SE value when K=42. When the SNR is 30dB, the spectrum efficiency of 2-APSP is better than SE. base Improved by 0.77SE base 3-APSP has increased the SE by 0.63 on the basis of 2-APSP. base Due to the more serious inter-group pilot interference of 3-APSP, its spectrum efficiency improvement is relatively small. When K=84, the spectrum efficiency increment of APSP is lower than that of 2-APSP, which is only 0.51SE. base This is attributed to the increased estimation error caused by increased intra-group channel overlap. It is worth noting that when K = 126, as the SNR increases, the impact of channel noise decreases while the pilot interference effect becomes more prominent. At this point, the APSP method can no longer accommodate all user terminals, resulting in a sharp increase in intra-group pilot interference and a sharp drop in spectrum efficiency. This highlights the huge potential of MAPSP in improving spectrum efficiency.

[0163] An embodiment of the present invention further discloses a computer program product, including a computer program, which implements the steps of any of the aforementioned methods when executed by a processor.

[0164] 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.

[0165] An embodiment of the present invention also discloses a large-scale MIMO-OFDM communication system, comprising a base station and multiple user terminals, wherein the user terminals are used to send known multiple sets of adjustable phase-shifted pilots to the base station, and the base station comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of any of the aforementioned methods when executed by the processor. In the large-scale MIMO-OFDM communication system, the base station generates an angle delay domain channel model with a non-uniform distribution of radial angles, and schedules the corresponding multiple sets of adjustable phase-shifted pilots for each user terminal using the statistical channel information and pilot radial angle information of each user terminal in the angle delay domain; in the uplink, the angle delay domain channel is estimated based on the received signal, and the estimated angle delay domain channel is mapped to the spatial frequency domain to complete the channel estimation; the user terminal is in a complex scenario where the line-of-sight and non-line-of-sight transmission states are frequently switched; and the scheduled multiple sets of adjustable phase-shifted pilots are sent to the base station in the uplink.

[0166] 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.

[0167] 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 multiple sets of adjustable phase-shifted pilots, characterized by: In a massive MIMO-OFDM system, users are divided into multiple groups. The same group uses the same basic pilot matrix to generate multiple adjustable phase-shifted pilots, while different groups use different basic pilot matrices. The autocorrelation matrix of the basic pilot matrix is a unit matrix, the diagonal elements of the cross-correlation matrices of different basic pilot matrices are sparse after FFT transformation, and each non-zero complex element of the sequence has the same argument; Multiple groups of adjustable phase-shifted pilots are obtained by multiplying the basic pilot matrix by the phase shift factor, and the phase shift factor of each pilot is repeatable.

2. The method of claim 1, wherein: The optimal sparseness of the sequence of the diagonal elements of the cross-correlation matrix of different basic pilot matrices after FFT transformation is a sequence containing only a single non-zero element; the basic pilot sequence group is obtained by performing different cyclic shifts on the Zadoff-Chu sequence with the same root, and the basic pilot sequence is the diagonal element of the basic pilot matrix.

3. A massive MIMO-OFDM channel acquisition method based on multiple sets of adjustable phase-shifted pilots, characterized by: In a massive MIMO-OFDM system, the spatial frequency domain channel is composed of multipaths of line-of-sight and non-line-of-sight wireless transmissions, which are converted to the angular delay domain through array vectors and discrete Fourier transform matrices; each user's pilot signal is a plurality of groups of adjustable phase-shifted pilot signals generated by the method for multiple groups of adjustable phase-shifted pilots according to claim 1 or 2. Using the statistical channel information and pilot amplitude angle information of each user in the angular delay domain, the base station groups each user and schedules the corresponding adjustable phase-shifted pilot signal; All users send multiple known sets of adjustable phase-shifted pilot signals to the base station. The base station preprocesses the received signals and then estimates the angle-delay domain channel. The estimated angle-delay domain channel is mapped to the spatial-frequency domain to complete the channel estimation.

4. The method for acquiring massive MIMO-OFDM channels based on multiple sets of adjustable phase-shifted pilots according to claim 3, wherein: The matrix elements of the angular delay domain channel have non-uniformly distributed angular radii; the probability density function of the angular radii is single-peaked or multi-peaked, and the positions of the peaks are related to the dominant angular radii of the line-of-sight and non-line-of-sight wireless transmission states, as well as the intermediate states during the transition between the two states; when the dominant angular radii of the above three states are similar, a wrapped Gaussian distribution is used to describe the single-peak angular radii distribution of the angular delay domain channel matrix elements, and the mean of the wrapped Gaussian distribution is the angular radii of the line-of-sight transmission state, and the variance is related to the difference in the dominant angular radii of the three states; when the variance of the wrapped Gaussian distribution is less than twice pi, a Gaussian distribution with the same mean and variance is used instead.

5. The method for acquiring massive MIMO-OFDM channels based on multiple sets of adjustable phase-shifted pilots according to claim 3, wherein: The modulus and angle of the channel matrix elements in the angle delay domain are independent, and each element in the matrix is also independent of each other, representing the channel complex gain under the corresponding angle and delay respectively; the angle delay domain statistical channel information, including power distribution and angle distribution information, the power distribution is represented as a sparse matrix, and the angle distribution is represented as a matrix composed of the mean angle distribution of the corresponding channel elements; the pilot angle information is the non-zero complex element angle after the FFT transformation of the diagonal element sequence of the cross-correlation matrix of different basic pilot matrices.

6. The method for acquiring massive MIMO-OFDM channels based on multiple sets of adjustable phase-shifted pilots according to claim 3, wherein: The angle-delay domain pilot correlation matrix is quickly solved by using the spatial-frequency domain pilot correlation matrix and the Toeplitz property of the matrix. The DFT / IDFT transform of the diagonal elements of the spatial-frequency domain pilot correlation matrix corresponds to the first column / first row elements of the angle-delay domain pilot correlation matrix respectively. The angle delay domain pilot cross-correlation matrix is a Toeplitz matrix, and the entire matrix can be determined by a certain column or a certain row element.

7. A massive MIMO-OFDM pilot scheduling method based on multiple sets of adjustable phase-shifted pilots, characterized by: According to the method for multiple sets of adjustable phase-shifted pilots according to claim 1 or 2, multiple sets of adjustable phase-shifted pilots are designed, and the base station schedules pilots for each user in the system according to the following method: For each unscheduled user, pilot scheduling is performed within different groups to ensure that the angle delay domain equivalent power distributions of users in the same group do not overlap or the overlap is less than a certain threshold. Based on the intra-group scheduling of each group, the user is assigned to the group with the lowest overlap. The angle delay domain equivalent power distribution refers to the channel matrix obtained by cyclically shifting all elements in the angle delay domain channel power distribution matrix to the right at the same time, and the shift length is determined by the phase shift factor of the pilot; The overlap refers to the linear correlation between the two power distribution matrices in the angular delay domain. When the positions of the non-zero elements in the two power distribution tensors are completely staggered, the overlap is zero.

8. A massive MIMO-OFDM received signal preprocessing method based on multiple sets of adjustable phase-shifted pilots, characterized by: According to the method for designing multiple sets of adjustable phase-shifted pilots according to claim 1 or 2, the base station preprocesses the received signal according to the following method: For interfering users outside the group to be estimated, multiply the pilot interference power matrix of the user to be estimated by the corresponding zero-angle matrix, then multiply it by the pilot angle information between the interfering group and the group to be estimated, and then add all of them together to obtain the equivalent pilot angle information matrix of the user to be estimated; The received signal is point-multiplied by the argument zeroing matrix of the user to be estimated, and then the tangent value of the equivalent pilot argument information matrix is divided by the real part minus the imaginary part. The result is then used for channel estimation. The angle zeroing matrix refers to the matrix after the angle mean of each element of the angle delay domain channel matrix is negated; the pilot interference power matrix refers to the equivalent power distribution of the interfering user in the angle delay domain after cyclic shift, and its phase shift factor is the difference between the pilot phase shift factors of the interfering user and the user to be estimated.

9. 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.

10. A massive MIMO-OFDM communication system, comprising a base station and multiple user terminals, characterized in that: The user terminal is used to send multiple known groups of adjustable phase-shifted pilots to the base station. The base station includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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