Massive MIMO-OFDM channel acquisition method based on multiple sets of adjustable phase shift pilots

Abstract

The application proposes a large-scale MIMO-OFDM channel acquisition method based on multiple groups of adjustable phase shift pilots. In the application, users are divided into multiple groups, each group uses the same basic pilot sequence to generate multiple adjustable phase shift pilots, and different groups use different basic pilot matrices; the autocorrelation matrix of the basic pilot matrix is a unit matrix, and the sequence after FFT transformation of the diagonal element of the cross-correlation matrix of different basic pilot matrices is sparse, wherein each non-zero complex element has the same argument. In the channel acquisition method, each user terminal sends known and scheduled multiple groups of adjustable phase shift pilot signals to the base station, the base station pre-processes the received signals, and then completes channel estimation. The method can greatly improve the spectral efficiency and channel information acquisition accuracy of the large-scale MIMO-OFDM system, especially in the communication scene with a large number of users and strong mobility, and has superior performance.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology and proposes a channel acquisition method based on multiple sets of adjustable phase-shift pilots for large-scale MIMO-OFDM systems. Background Technology

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

[0003] Massive MIMO-OFDM systems, by combining the advantages of both, can further improve throughput, spectral efficiency, and communication reliability. These technological trends will not only significantly increase the system's peak rate 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 mobile scenarios, even with the channel reciprocity of Time-Division Duplex (TDD) mode, the overhead of CSI acquisition is still insufficient to 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 Pilot (PSOP) widely used in large-scale MIMO-OFDM systems. Although orthogonal pilots effectively suppress pilot pollution, their high pilot overhead makes them unsuitable for highly dynamic environments. Meanwhile, significant progress has been made in research based on channel sparsity. Compressed sensing algorithms have achieved low-overhead CSI acquisition by utilizing channel sparsity, but this is often limited by computational complexity and the sparsity of the channel matrix. In contrast, pilot multiplexing provides a computationally efficient and low-overhead alternative. However, pilot multiplexing introduces interference at the receiver, affecting CSI estimation accuracy; therefore, pilot scheduling becomes a key technical challenge. By fully utilizing the power concentration characteristics of the channel in the angular delay domain, pilot scheduling can significantly reduce or even eliminate interference. Building on this, Adjustable Phase Shift Pilots (APSP) further improve the performance of non-orthogonal pilot multiplexing by enhancing the utilization efficiency of sparse channels through dynamic phase scheduling.

[0005] While existing research has made some progress in reducing pilot overhead, most methods remain limited to utilizing the inherent sparsity of the channel, neglecting the synergistic effect of pilot structure and channel characteristics. As wireless communication evolves towards ultra-dense networking, high spectral efficiency, and ultra-low latency, related application areas and scenarios are becoming increasingly diverse and complex. Simultaneously, the research value of argument statistics in channel estimation is becoming increasingly prominent. When user terminals are densely packed and signal transmission states switch frequently, the channel argument exhibits a non-uniform distribution. Furthermore, the first-order channel statistics in these emerging wireless scenarios exhibit multimodal distribution characteristics, further increasing the complexity of channel estimation. Therefore, adopting accurate, low-overhead channel estimation methods for the complex propagation environments in emerging applications has become a key issue in the current technological development landscape.

[0006] To address the shortcomings of existing channel acquisition techniques and meet the evolving needs of future large-scale MIMO-OFDM systems, this invention proposes a multi-set adjustable phase-shift pilot method for wireless communication systems, and a large-scale MIMO-OFDM channel acquisition method based on this method. In the multi-set adjustable phase-shift pilot method, the characteristics of the basic pilot cross-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 this method, a large-scale MIMO-OFDM system model in an emerging scenario is first established, and an angle delay domain channel model with non-uniform argument distribution is constructed. A basic pilot sequence set is designed, and a multi-set adjustable phase-shift pilot scheduling algorithm is proposed. The base station preprocesses the received signal using statistical channel information and pilot argument information, and then uses an MMSE estimator to achieve low pilot overhead channel estimation, thereby obtaining the channel information for each user. Compared with existing methods, the proposed channel acquisition method significantly improves the system's pilot capacity and channel information acquisition accuracy, exhibiting superior performance. Compared with existing methods, the proposed channel acquisition method significantly improves the system's spectral efficiency and channel estimation accuracy, demonstrating superior performance in communication scenarios with dense user bases and high mobility. Summary of the Invention

[0007] Purpose of the invention: To address the shortcomings of existing technologies for large-scale MIMO-OFDM systems, the present invention aims to propose a channel acquisition method based on multiple sets of adjustable phase-shift pilots, so as to improve the spectral efficiency and channel estimation accuracy of the system and meet the needs of emerging and complex mobile communication scenarios.

[0008] Technical Solution: To achieve the above-mentioned objectives, the present invention provides the following technical solution:

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

[0010] As a preferred option, the optimal sparsity of the sequence after FFT transformation of the diagonal elements of the cross-correlation matrix of different fundamental pilot matrices is a sequence containing only a single non-zero element. One method to achieve the optimal sparsity is to obtain a group of fundamental pilot sequences by performing different cyclic shifts on Zadoff-Chu sequences with the same root. The fundamental pilot sequences are the diagonal elements of the fundamental pilot matrix.

[0011] Secondly, this invention provides a method for acquiring a large-scale MIMO-OFDM channel based on multiple sets of adjustable phase-shift pilots, comprising: in a large-scale MIMO-OFDM system, the spatial frequency domain channel is composed of multipath propagation between line-of-sight and non-line-of-sight wireless transmissions, which is converted to the angular delay domain by array vector and discrete Fourier transform matrix; each user's pilot signal is a set of adjustable phase-shift pilot signals, and the base station groups each user and schedules their corresponding adjustable phase-shift pilots using statistical channel information and pilot amplitude information of each user in the angular delay domain; all users send known sets of adjustable phase-shift pilots to the base station, the base station preprocesses 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.

[0012] The matrix elements of the angular delay domain channel have non-uniformly distributed arguments; the probability density function of the arguments is single-peaked or multi-peaked, and the position of the peak is related to the dominant argument value of the line-of-sight, non-line-of-sight wireless transmission states, and the intermediate state during the transition between the two states; when the dominant argument values ​​of the above three states are similar, a wrapped Gaussian distribution is used to describe the single-peaked argument distribution of the angular delay domain channel matrix elements, and the mean of the wrapped Gaussian distribution is the argument value of the line-of-sight transmission state, and the variance is related to the difference in the dominant argument values ​​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 can be used instead.

[0013] The modulus and argument of the channel matrix elements in the angle delay domain are independent, and each element in the matrix is ​​also independent, representing the channel complex gain under the corresponding angle and delay. The angle delay domain statistical channel information includes power distribution and argument distribution information. The power distribution of the channel in the angle delay domain is represented as a sparse matrix. The argument distribution of the channel in the angle delay domain is represented as a matrix composed of the mean argument distributions of the corresponding channel elements. The pilot argument information is the argument 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.

[0014] As a preferred method, the pilot cross-correlation matrix in the angular delay domain can be quickly solved by using the pilot cross-correlation matrix in the spatial frequency domain and the Toeplitz property of the matrix. The DFT / IDFT transformation of the diagonal elements of the pilot cross-correlation matrix in the spatial frequency domain corresponds to the first column / first row elements of the pilot cross-correlation matrix in the angular delay domain, respectively. The pilot cross-correlation matrix in the angular delay domain is a Toeplitz matrix, and the entire matrix can be determined by using the elements of a certain column or a certain row.

[0015] Thirdly, this invention provides a large-scale MIMO-OFDM pilot scheduling method based on multiple sets of adjustable phase-shift pilots. Based on the designed multiple sets of adjustable phase-shift pilots, the base station schedules pilots for each user in the system according to the following method:

[0016] For each unscheduled user, intra-group pilot scheduling is performed in 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 situation of each group, the user is assigned to the group with the lowest overlap.

[0017] The equivalent power distribution in the angle delay domain refers to the channel matrix obtained by simultaneously shifting all elements in the channel power distribution matrix in the angle delay domain to the right in a cyclic manner. The shift length is determined by the phase shift factor of the pilot.

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

[0019] Fourthly, this invention provides a preprocessing method for large-scale MIMO-OFDM received signals based on multiple sets of tunable phase-shift pilots. Based on the designed multiple sets of tunable phase-shift pilots, the base station preprocesses the received signal according to the following method to reduce pilot interference between different sets:

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

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

[0022] The argument zeroing matrix refers to the matrix after the average argument of each element of the channel matrix in the angle delay domain is negative; the pilot interference power matrix refers to the equivalent power distribution of the interfering user in the angle delay domain after cyclic shifting, 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] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the multi-set adjustable phase-shift pilot method, the large-scale MIMO-OFDM channel acquisition method based on the multi-set adjustable phase-shift pilot, the large-scale MIMO-OFDM pilot scheduling method based on the multi-set adjustable phase-shift pilot, and / or the large-scale MIMO-OFDM channel estimation method based on the multi-set adjustable phase-shift pilot.

[0024] In a sixth aspect, the present invention provides a large-scale MIMO-OFDM communication system, including a base station and multiple user terminals. The user terminals are used to send known multiple sets of tunable phase-shift 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, it implements the steps of the method for multiple sets of tunable phase-shift pilots, the method for obtaining large-scale MIMO-OFDM channels based on multiple sets of tunable phase-shift pilots, the method for scheduling large-scale MIMO-OFDM pilots based on multiple sets of tunable phase-shift pilots, and / or the method for estimating large-scale MIMO-OFDM channels based on multiple sets of tunable phase-shift pilots.

[0025] Beneficial Effects: Compared with existing technologies, this invention has the following advantages: 1. This invention proposes a multi-set adjustable phase-shift pilot method, which improves the spectral efficiency of the system without increasing pilot overhead. 2. The pilot scheduling method proposed in this invention schedules corresponding pilot signals for different users in a large-scale MIMO-OFDM system with low complexity, effectively suppressing interference between users and improving channel estimation performance. 3. Based on the established channel model with non-uniform argument distribution, this invention proposes a channel estimation method with received signal preprocessing, which fully utilizes the sparsity of the channel in the angle delay domain and the structural characteristics of the pilot, effectively reducing the complexity of channel estimation in large-scale MIMO-OFDM. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the basic pilot cross-correlation matrix features in an embodiment of the present invention.

[0027] Figure 2 This is a flowchart of a large-scale MIMO-OFDM channel acquisition method based on multiple sets of adjustable phase-shift pilots according to an embodiment of the present invention.

[0028] Figure 3 This is a diagram of the pilot cross-correlation matrix structure in the angle delay domain of this invention.

[0029] Figure 4 This is a comparison chart of the estimation errors between the channel acquisition method proposed in this embodiment and existing channel acquisition methods.

[0030] Figure 5 This is a comparison chart of the spectral efficiency of the channel acquisition method proposed in this embodiment of the invention and existing channel acquisition methods. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] The method for generating multiple adjustable phase-shift pilots disclosed in this invention includes: in a large-scale MIMO-OFDM system, users are divided into multiple groups, and the same group uses the same basic pilot sequence to generate multiple adjustable phase-shift pilots, while different groups use different basic pilot matrices; wherein, the autocorrelation matrix of the basic pilot matrix is ​​an identity matrix, and the sequence of diagonal elements of the cross-correlation matrix of different basic pilot matrices after FFT transformation is sparse, and each non-zero complex element of the sequence has the same argument; multiple adjustable phase-shift pilots are obtained by multiplying the basic pilot matrix by a phase shift factor, and the phase shift factor of each pilot can be repeated.

[0033] Based on the multi-set adjustable phase-shift pilot method of this embodiment, the transmitting device simultaneously sends multiple known sets of adjustable phase-shift pilot signals to the receiving device; the receiving device uses the received pilot signals to estimate the channel parameters. The optimal sparseness of the sequence of 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 to achieve the optimal sparseness is to perform different cyclic shifts on Zadoff-Chu sequences with the same root to obtain a set of basic pilot sequences.

[0034] Figure 1 A schematic diagram illustrating the characteristics of the basic pilot cross-correlation matrix in this embodiment of the invention is provided. When users are divided into Q groups, for the k-th group in the q-th group... q For each user, the pilot matrix is ​​represented as... Where N c It is the number of subcarriers in a large-scale MIMO-OFDM system. It is the kth q The phase shift factors for each user are represented by diag{x}, which is a matrix with x as its diagonal element. The phase shift factors for all users can be the same, i.e., there exists a... It is of length N c The first of the Discrete Fourier Transform (DFT) matrices column, p xtr It is the transmission power of the pilot signal, s q This is the fundamental pilot sequence of the qth group. The fundamental pilot sequences of each group are different, but all have a length of N. c , It is the fundamental pilot matrix of the q-th group, and satisfies in(·) H This represents the conjugate operation of a matrix. Each group of users uses the same S. q Generate adjustable phase-shift pilots; different groups have different fundamental pilot matrices, i.e., S q ≠S q′ The correlation operation of the pilot matrix is ​​expressed as follows: This represents the cross-correlation of the fundamental pilot matrix. Since the fundamental pilot matrix S... q There are several possible structures, including the Space-Frequency domain Pilot Cross-correlation Matrix (SFPCM). There are also different structures. In the case of autocorrelation... When pilots in the same group are cross-correlated Cross-correlation of different groups of pilots It cannot be simplified.

[0035] For the fundamental pilot matrix S of multiple sets of adjustable phase-shift pilots q The optimal design condition is to satisfy Represents the DFT transform of x. Only the nth element is non-zero, making it a sparse sequence used to describe The ideal situation. and The greater the similarity and sparsity, the more suitable it is for use as a fundamental pilot matrix. The phase angle of a complex number is defined as its argument; ideally... If there is only one non-zero element value, then it satisfies the condition that each non-zero complex element has the same argument; however, in a non-ideal case... If the argument values ​​of each non-zero complex element are not equal, then the basic pilot sequence is not suitable for generating multiple sets of adjustable phase-shift pilots in this embodiment. For the cross-correlation matrix of the basic pilots within a set, the following conditions must be met: Channel sparsity can be efficiently utilized. The larger the cross-correlation value, the better. The greater the number and width of non-zero segments, the stronger the pilot interference. The Zadoff-Chu (ZC) sequence is represented as... in a> b This represents the modulo operation of a with respect to b, r represents the root index, N represents the sequence length, and φ∈[0,1,…,N-2] represents the number of cyclic shifts. (Appendix) Figure 1 (a) in the text represents The best sparsity case is (b), which has poor sparsity and a large non-sparse interval near the peak. (c) represents the case when ZC sequences with different root indices are used as the basic pilot sequences. Although there is no peak, the sparsity is destroyed, resulting in overall interference.

[0036] The ZC sequence with root sequence 1 and its own cyclic shift sequence are selected as the basic pilot sequence group. and There is an equation At this point, the optimal condition for the fundamental pilot matrix is ​​satisfied. And a fixed argument constant appeared. This is also the pilot argument information for the q′ group relative to the q group.

[0037] The following section, using specific channel information acquisition methods, details the specific effects of the multiple sets of adjustable phase-shift pilots designed in this invention.

[0038] Figure 2 A simplified flowchart of the large-scale MIMO-OFDM channel information acquisition method based on multiple sets of adjustable phase-shift pilots disclosed in this invention is provided. The method specifically includes: establishing a spatial-frequency domain multipath channel model between different users and the base station, and converting it to an angle-delay domain channel model with non-uniform argument distribution. The spatial-frequency domain channel consists of multipath signals from line-of-sight and non-line-of-sight wireless transmissions. This model is converted to the angle-delay domain using array vectors and a discrete Fourier transform matrix. The elements of the angle-delay domain channel matrix all exhibit non-uniform argument distribution characteristics. Each user's pilot signal consists of multiple sets of adjustable phase-shift pilot signals. The base station uses statistical channel information and pilot argument information for each user in the angle-delay domain to group users and schedule their corresponding adjustable phase-shift pilots. All users send known sets of adjustable phase-shift pilots 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.

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

[0040] The following describes in detail the specific implementation process of the large-scale MIMO-OFDM channel acquisition method based on multiple sets of adjustable phase-shift pilots involved in this invention, using a specific communication system example. It should be noted that the method of this invention is not only applicable to the specific system model mentioned in the example below, but also to other system models with different configurations.

[0041] I. 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 of M antennas, each separated by half a wavelength λ. The cell accommodates K single-antenna user terminals (UTs). These K UTs are first divided into Q groups, denoted as [equation missing in original text]. in Represents the group index. The q-th UT set is represented as in and Let represent the UT index in the q-th group. Assume that the channels of different UTs are statistically independent.

[0043] Using N cAn OFDM modulation scheme with N subcarriers, which can be achieved through N c This is implemented using the inverse discrete Fourier transform (IDFT). 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 These represent the system sampling durations with and without CP, where T is the sampling duration for the system. s This represents the system sampling period. It is assumed that the CP duration of all user terminals is greater than the maximum channel delay.

[0044] II. Channel Model

[0045] The example assumes the channel remains constant within one OFDM symbol period but varies between different symbols. Based on physical characteristics, a large-scale MIMO-OFDM channel model is constructed, and the problem description is as follows. In the uplink, the k-th group is defined as... q The m-th antenna of the base station is connected between the user terminal and the base station at the... The nth OFDM symbol c The channel response vectors on each subcarrier are:

[0046]

[0047] in N represents the channel response vector. p This represents the total number of paths. Let represent the complex gain, direction cosine, and time delay of the p-th path, respectively. Specifically, the complex gain of the line-of-sight (LoS) path is expressed as:

[0048]

[0049] in Here, d0 represents the real gain, and d0 represents the distance from the user terminal to the base station. In this embodiment, the phase angle of the complex gain is defined as the argument parameter. Taking a 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 contrasts with the line-of-sight path. Due to the significant power differences between the paths... The real gain and phase angle are mainly affected by the line-of-sight path. The spatial-frequency domain channel response matrix can be obtained by aggregating the channel response vectors of different subcarriers. Its expression is as follows

[0050]

[0051] Computing high-dimensional matrices in large-scale MIMO-OFDM systems Significant challenges exist. To simplify the calculation, array vectors and discrete Fourier transform matrices are used to... Channel estimation is performed by switching to the angle-delay domain.

[0052]

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

[0054]

[0055] It is the kth q The user terminal in the first The channel response matrix in the angular delay domain within each symbol. Array response vector. The specific expression is as follows

[0056]

[0057] Equation (4) can be used to transform the estimated channel to Therefore, the focus is on analyzing the characteristics of the channel in the angle delay domain. This channel matrix element... Defined as

[0058]

[0059] If we assume If the elements are independent of each other, then from arrive The transformation can be calculated element-wise in the spatial frequency domain, as shown in equation (7). Considering a high-speed mobile scenario with a large number of user terminals, the frequent movement of terminals will lead to changes in the communication link state, namely, line-of-sight and non-line-of-sight wireless transmission states, as well as the switching of intermediate states when transitioning between the two states. This frequent change will cause the channel argument to exhibit a non-uniform distribution, and the argument values ​​will be concentrated near the dominant argument values ​​of the three states, thus exhibiting 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) exhibits a single-peak characteristic. To approximate this phase distribution, a wrapped Gaussian distribution is adopted. Modeling is performed, where the mean Related to the argument of the viewing distance component, variance This reflects the argument similarity between the non-line-of-sight component and the line-of-sight component. This embodiment uses a single-peak case as an example; the multi-peak case can be viewed as the superposition of multiple single peaks. Rewritten equation (7)

[0060]

[0061] in Represents the real gain vector in the angular delay domain, with argument parameter Specifically, the argument is mainly concentrated around a specific mean, and its variance is less than 2π. Under these conditions, the Gaussian distribution of the package can be sufficiently approximated as a Gaussian distribution. A Gaussian distribution with the same mean and variance can be used instead.

[0062] matrix It is closely related to the received signal delay and the angle of arrival (AOA). Based on the sparsity characteristics of large-scale MIMO-OFDM channels in the angle delay domain, from a statistical perspective... When the number of antennas is large enough, its relationship with the channel power matrix in the angle delay domain can be established. The mapping relationship.

[0063]

[0064] in The time difference between OFDM symbols. Let E0 be the channel time correlation function, which is related to the Doppler frequency parameter v. E{·} denotes the expectation operation, and ⊙ denotes the dot product operation of matrices. Based on the Clarke-Jakes channel power Doppler spectrum model, this time correlation function can be concisely represented as a zeroth-order Bessel function of the first kind, J0(·).

[0065]

[0066] In large-scale MIMO-OFDM channels Each element represents the channel complex gain at the corresponding angle and time delay. They are statistically independent, and their magnitude and argument distributions are independent. Therefore, the corresponding power matrix... It exhibits sparse characteristics in the angular time delay domain. Because Composed of distinct independent elements, an element-by-element estimation method can be employed. The argument distribution of the channel in the angular delay domain is represented as a matrix composed of the mean arguments of the corresponding channel elements. Based on these channel characteristics, this embodiment can achieve high-precision channel estimation. The pilot argument information can be directly calculated when designing the basic pilot sequence, assuming the base station knows the power matrix of all user terminals. and argument distribution parameters.

[0067] III. Channel Estimation

[0068] Assume all user terminals remain fully synchronized. During the uplink phase of each frame, all UTs are in the [missing information - likely a specific phase or stage]. Pilot signals are transmitted simultaneously on each OFDM symbol, and the base station receives all pilot signals. Assume that the UTs are divided into Q groups, each assigned a basic pilot matrix. In this embodiment, each group of UTs uses a different basic pilot matrix to generate APSP. When Q=1, the multi-group adjustable phase-shift pilot (MAPSP) channel acquisition method becomes an APSP-based method.

[0069] Base station at The spatial frequency domain signal received on one OFDM symbol can be represented as:

[0070]

[0071] in, Represents the received signal matrix. The channel response matrix is ​​in the spatial frequency domain. Let be an additive white Gaussian noise matrix, whose elements are independently and identically distributed. During the uplink pilot transmission phase, it is assumed that the noise follows a certain distribution. Distribution, where p ntr Indicates noise power.

[0072] Base station receives signals It contains spatial frequency domain channel and pilot information for all user terminals, among which This represents the received signal of the i-th antenna on the j-th subcarrier. If MMSE estimation is directly used to obtain channel information... Its computational complexity would be extremely high. Therefore, by utilizing the sparsity of the channel, it is first estimated in the angular time delay domain. Then, the result can be indirectly obtained through the unitary equivalence relation in equation (4). Therefore, the base station received signal formula (12) can be restated in the uplink as follows:

[0073]

[0074] Suppose we need to estimate the k'th digit. q′ Channel information for each user terminal. Through... After decorrelation and power normalization, the following can be obtained: The least squares estimate is expressed as follows:

[0075]

[0076] The third term on the right side of the equation Indicates user terminal k' q′ The channel response in the angle delay domain. In equation (14), the first term on the right represents pilot interference from the same group of user terminals, which can be simplified to The circular shift form, i.e.

[0077]

[0078] in

[0079]

[0080] yes The zero-padding extended form. N and I N×L Let N and I represent an identity matrix of length N, respectively. N The first L columns of elements.

[0081]

[0082] It is a cyclic shift matrix. It originates from user terminal k in the same group. q′ The pilot interference term can be understood as... Circular shift to the right After one unit, then truncate the first N units. g The results obtained are listed.

[0083] The second term on the right-hand side of equation (14) represents 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 the angle-delay domain pilot cross-correlation matrix (ADPCM). From other user terminal group k q The pilot interference term can be considered as... After multiplying by ADPCM, truncate the first N. g The results of the column. When satisfied In this case, the pilot interference generated by 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 proven to follow a cyclic symmetric complex Gaussian distribution using the unitary transform property. The specific expression is as follows

[0088]

[0089] in Indicates the first Signal-to-noise ratio (SNR) over each OFDM symbol, N nor This is a normalized additive white Gaussian noise matrix, whose elements are independent and identically distributed.

[0090] The received signal is decorrelated; the decorrelated received pilot signal 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) and simplifying, we get...

[0091]

[0092] in

[0093]

[0094] According to equation (9), since The elements are statistically uncorrelated, and the pilot interference term... and The elements also satisfy the statistically uncorrelated property. The power matrices for pilot interference in the same and different groups are defined as follows:

[0095]

[0096] in

[0097]

[0098] yes The zero-padding extended form.

[0099] The received signal after decorrelation processing in equation (14) The minimum mean square error (MMSE) estimator can be obtained through element-wise processing.

[0100]

[0101] in

[0102]

[0103] definition For user terminal k′ q′ Given the channel estimation error matrix, the following relationship can be established:

[0104]

[0105] Clearly, the total MMSE estimation error for K user terminals can be obtained by summing up the individual error terms:

[0106]

[0107] Channel estimation accuracy is directly affected by pilot interference intensity. Pilot scheduling methods can minimize or eliminate pilot interference, approximating the lower bound of the MMSE channel estimation error under optimal conditions, denoted as:

[0108]

[0109] The above equation can be considered as the ideal case where all pilot interference is eliminated. Specifically, for the k'th... q′ There are user terminals,

[0110] From a mathematical point of view, The sparsity can be specifically stated as follows: the matrix contains only c(≤N) elements. g The elements in column A are non-zero valid values, while the remaining elements are approximately zero. These non-zero column vectors themselves also have only a subset of non-zero elements, intuitively reflecting the sparsity of the channel. When multiple user terminals use the same basic pilot matrix, channel overlap can be reduced by adjusting the phase offset of the pilots during scheduling. If... k q′ ≠k′ q′ ,

[0111]

[0112] At this point, pilot interference can be completely eliminated, thus reaching the lower bound of the error. When the sparsity characteristics in 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 inter-group interference. Intra-group interference characteristics are similar to those of APSP generated by a single-fundamental pilot matrix, while inter-group interference is mainly affected by ADPCM. Now, regarding user terminal k... q and k' q′ The general form of SFPCM makes the following assumptions:

[0114]

[0115] Where r i Representation matrix The value of the i-th diagonal element depends on the underlying pilot matrix used. Referring back to equation (19), the user terminal k... q and k' q′ The ADPCM between them can be summarized as follows:

[0116]

[0117] Due to the matrix It is a Toeplitz matrix, meaning that every element except the elements in the first row and first column is equal to its adjacent top-left element. Due to the properties of Toeplitz matrices, only the elements in its first row need to be calculated. or the first column element That will confirm All elements.

[0118]

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

[0120] This paper explains the construction principle of ADPCM from another perspective and analyzes its mechanism for mitigating pilot interference. ADPCMs with different basic pilot matrices exhibit cyclic shift characteristics with phase differences, as shown below.

[0121]

[0122] in

[0123]

[0124] It is the ADPCM of the basic pilot matrix. Equation (37) comprehensively describes the pilot interference generated by the MAPSP channel estimation method. The intensity of this interference mainly depends on the degree of channel overlap, the selection of the basic pilot matrix, and the phase shift difference. This means that the inter-group pilot interference matrix and the intra-group pilot interference matrix have similar cyclic shift characteristics. Reviewing equation (18), it can be restated as follows:

[0125]

[0126] The interference between the two groups originates from the ADPCM of their basic pilot matrix and Appendix Figure 3 This is a diagram of the pilot cross-correlation matrix structure in the angle delay domain of this invention, illustrating... Structural details. First observe... The structure, The function is to perform a circular right shift on the multiplicand matrix, which means that... Can be regarded as through The result after a circular shift of the column. Because... No change Structural characteristics, It retains the properties of the Toeplitz matrix. (Appendix) Figure 3 The dashed boxes in the equations correspond to equations (34) and (35) respectively, indicating that in this Toeplitz matrix... or Included All information. In equation (37), In essence, it is interception The first N g The result of the column operation, therefore The first N g Column elements are defined as capturing elements. Matrix By The result is calculated by multiplying by the captured element. Special attention should be paid to this. Finally (N) c -N g Since all columns are zero, The last (N) multiplied by it c -N g Row elements do not affect the calculation result.

[0127] In general, Only the first N g Row element influence The values ​​that can be taken. These elements are called... The valid element. If this If all valid elements are zero, then the pilot interference will also be zero. It is worth noting that... Each column is simply a circular shift of the first column, so the value of the valid element depends only on The first N g The last N g Each element. (By reference) Pilot interference can be calculated quickly. Furthermore, there exists another scenario with no pilot interference, where non-zero pilot interference occurs. With the estimated When there is no overlap, phase scheduling, i.e., adjustment, can be used. Adjustable The position of the element in the middle, thus changing To avoid pilot interference. Specifically, when deploying the same base pilot, At this point, equation (37) will degenerate into intragroup interference (15).

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

[0129] like If it is a zero matrix, then The fewer non-zero elements in it, First and last N g The more phase shift combinations where all elements are zero, the more possible they are.

[0130] like If the matrix is ​​non-zero, then the fewer non-zero elements it contains in its effective elements, the better the matrix is ​​related to the channel to be estimated. The lower the degree of overlap.

[0131] IV. Signal Preprocessing and Pilot Scheduling

[0132] In this embodiment, the ZC sequence and its own cyclic shift sequence are selected as the basic pilot sequence group, and the pilot amplitude information satisfies the following relationship. Substituting the fundamental pilot cross-correlation matrix in the angle-delay domain into equation (37), the pilot interference term can be reformulated as follows:

[0133]

[0134] This shows that interference between different groups has... This characteristic coefficient is an important feature that distinguishes it from interference within the same group. Combined with equation (8), The argument is a random variable. Due to the presence of pilot amplitude information, generated The argument also follows a Gaussian distribution. In other words, This causes the arguments of the two sets of 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 user group to be estimated, the base station multiplies the pilot interference power matrix of the user to be estimated by the corresponding argument zeroing matrix, then multiplies it by the pilot argument information between the interfering group and the user group to be estimated, and then adds them all to obtain the equivalent pilot argument information matrix of the user group to be estimated; the base station multiplies the received signal by the argument zeroing matrix of the user to be estimated, then divides the real part by the imaginary part by the tangent of the equivalent pilot argument information matrix, and the result is used for MMSE channel estimation; where the argument zeroing matrix refers to the matrix after taking the negative mean of the arguments of each element of the channel matrix in the angle delay domain; the pilot interference power matrix refers to the equivalent power distribution of the interfering user in the angle delay domain after cyclic shifting, 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 for each user terminal is defined as follows: Its function is to adjust the mean of the argument distribution to zero. In other words, The mean argument of each channel is zero. Next, the equivalent pilot argument information matrix between the q'-th user terminal channel and other channels is calculated.

[0137]

[0138] Where arg{·} represents the argument operation. User k q For user k' q′ The pilot interference power matrix. After preprocessing, the received signal can be expressed as:

[0139]

[0140] From a statistical perspective, the preprocessed signal It can effectively reduce interference from other groups. For simplicity, assume the argument of all channels is... and For the kth q The user terminal in the first The channel vector over OFDM symbols, the mathematical expectation of its nth column can be expressed as:

[0141]

[0142] because but

[0143]

[0144] Finally, the preprocessed signal The received signal is used as the base station signal, and 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 APSP. However, as the number of groups Q increases, the difficulty of reducing inter-group interference also increases. These errors mainly stem from the incomplete elimination of inter-group interference, while interference residue arises from the limitations of the channel phase angle distribution assumption. When the channel parameters... As the error is reduced, the error introduced by preprocessing is also reduced.

[0145] To address intra-group interference, the channel sparsity characteristics in the angle delay domain can effectively reduce or eliminate channel overlap, thereby significantly reducing pilot interference. Since interference intensity is affected by the channel power distribution of each user terminal, pilot scheduling becomes a crucial and advantageous optimization method. Before scheduling, the pilot argument information between groups needs to be confirmed. When it is greater than the argument variance of the channel matrix in the angle delay domain, the selected sequence meets the conditions for being a basic pilot sequence group, and user scheduling can then be performed.

[0146] The base station schedules pilot signals for each user in the system as follows: For each unscheduled user, intra-group pilot scheduling is performed in different groups to ensure that the equivalent power distributions in the angle delay domain do not overlap or the overlap is less than a certain threshold among users in the same group; based on the intra-group scheduling situation of each group, the user is assigned to the group with the lowest overlap; the equivalent power distribution in the angle delay domain refers to the channel matrix obtained by simultaneously right-circularly shifting all elements in the channel power distribution matrix in the angle delay domain; the overlap refers to the linear correlation between two power distribution matrices in the angle delay domain, and the overlap is zero when the positions of the non-zero elements in the two power distribution tensors are completely offset.

[0147] For example, the pilot scheduling in this embodiment adopts the intra-group MMSE criterion. Since the phase shift of a single channel affects other user terminals within the group, the optimal solution needs to traverse all possible intra-group scheduling scenarios. This is achieved by setting an intra-group threshold Y. tra To balance scheduling effectiveness and computational complexity, the smaller the threshold, the more accurate the scheduling, but the more complex the computation.

[0148] use The normalized dot product is used to characterize the degree of overlap between channels. Channel superposition matrix. This describes the overlap of scheduled channels within the q-th group. Groups and phase shift factors are assigned to each user terminal individually, selecting cases where the overlap is below a threshold. When all phases do not meet the threshold Y...tra When the overlap is minimal, the factor with the least overlap is selected. Each user terminal will be scheduled and tested in all groups in turn, and finally assigned to the group with the least overlap, thus making full use of the inherent sparsity of the channel. The detailed steps of the MAPSP scheduling algorithm are as follows:

[0149] Step 1: Input the UT set Channel power matrix Scheduling threshold Y tra Initialize the set of unscheduled UTs Scheduled UT set Intra-group phase Channel superposition matrix

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

[0151]

[0152] like If the traversal is stopped, the traversal continues and the phase shift factor with the smallest overlap is recorded; this process continues until all Q groups have been 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 Results

[0155] To enable those skilled in the art to better understand the present invention, the performance results of the channel information acquisition method in this embodiment under specific configurations are given below.

[0156] Consider a large-scale 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 on the base station side M = 128, element spacing is half wavelength, single symbol length T sym = 71.4 μs, sampling interval T s =32.6ns, Doppler coefficient vT sym =31.4×10 -3 The antenna downtilt angle is 102°, and the user terminal's moving speed is v. speed=80km / h, number of users in the system K=42, K=84, K=126, scheduling algorithm threshold Y tra =10 -7 Assume that the transmissions of all user terminals remain synchronized. The power is determined by the argument similarity between 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 for the sake of simplified calculation, the power of all user terminal channels is normalized to...

[0157] Appendix Figure 4 The graph depicts the variation of average MMSE with SNR, where 2-APSP and 3-APSP represent the MAPSP method with 2 and 3 groups, respectively. With the same number of user terminals, the MAPSP method in this embodiment exhibits a lower MMSE error, which becomes more significant as the value of K 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 high-speed mobile environments, while the MAPSP method still maintains acceptable estimation accuracy. Therefore, as K increases exponentially, the MAPSP method demonstrates a significant MMSE performance advantage, especially in high-speed mobile scenarios.

[0158] Appendix Figure 5 The graph depicts the spectral efficiency versus SNR. The frame length is 7 OFDM symbols, or 500 μs. Uplink and downlink data transmission each occupy half of the data segment, each containing 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 well-suited for high-speed mobile scenarios, and the phase-shifted pilot method can effectively handle rapid channel changes. Both uplink and downlink data are transmitted using an MMSE receiver and precoder, and it is assumed that their signal-to-noise ratio is 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. (According to Appendix...) Figure 5 Under the same K value, the MAPSP method in this embodiment significantly improves spectral efficiency compared to the APSP method. This improvement mainly stems from two key factors affecting spectral efficiency: the number of user terminals and the channel estimation accuracy. baseThis represents the baseline SE value when K=42. When the SNR is 30dB, the spectral efficiency of 2-APSP is higher than that of SE. base Improved by 0.77 SE base The 3-APSP provides an additional 0.63 SE improvement over the 2-APSP. base Because the 3-APSP suffers from more severe inter-group pilot interference, its spectral efficiency improvement is relatively small. At K=84, the spectral efficiency increment of the APSP is lower than that of the 2-APSP, only 0.51 SE. base This is attributed to the increased estimation error caused by the intensified channel overlap within the group. It is noteworthy that at K=126, as the SNR increases, the impact of channel noise weakens while pilot interference becomes more prominent. At this point, the APSP method can no longer accommodate all user terminals, leading to a surge in intra-group pilot interference and a sharp decline in spectral efficiency. This highlights the significant potential of MAPSP in improving spectral efficiency.

[0163] This invention also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the aforementioned methods.

[0164] The program code used to implement the method of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the steps of the method of the present invention to be performed. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a standalone software package, or entirely on a remote machine or server. All aspects not detailed in this invention are well-known to those skilled in the art.

[0165] This invention also discloses a large-scale MIMO-OFDM communication system, including a base station and multiple user terminals. The user terminals are used to send multiple known sets of tunable phase-shift 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, it implements the steps of any of the aforementioned methods. In this large-scale MIMO-OFDM communication system, the base station generates an angle delay domain channel model with non-uniformly distributed argument. It uses statistical channel information and pilot argument information of each user terminal in the angle delay domain to schedule multiple sets of tunable phase-shift pilots corresponding to each user terminal. In the uplink, based on the received signal, the angle delay domain channel is estimated, and the estimated angle delay domain channel is mapped to the spatial frequency domain to complete channel estimation. The user terminals are in a complex scenario where line-of-sight and non-line-of-sight transmission states frequently switch. In the uplink, the scheduled multiple sets of tunable phase-shift pilots are sent to the base station.

[0166] In the embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways without departing from the spirit and scope of this application. The current embodiments are merely exemplary examples and should not be considered limiting, nor should the specific content given limit the purpose of this application. For example, some features may be omitted or not implemented.

[0167] The technical means disclosed in this invention are not limited to those disclosed in the above embodiments, but also include technical solutions composed of any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications are also considered within the scope of protection of this invention.

Claims

1. A method for massive MIMO-OFDM channel acquisition based on multiple sets of adjustable phase-shift pilots, characterized in that: In a large-scale MIMO-OFDM system, a spatial frequency domain channel is composed of line-of-sight and non-line-of-sight wireless transmissions, and is converted into an angle-delay domain by an array vector and a discrete Fourier transform matrix; pilot signals of each user are a plurality of groups of adjustable phase shift pilot signals, and using statistical channel information and pilot argument information of each user in the angle-delay domain, a base station groups and schedules corresponding adjustable phase shift pilots of each user; all users send known adjustable phase shift pilots to the base station, the base station pre-processes received signals, and then estimates an angle-delay domain channel; the angle-delay domain channel is mapped to a spatial frequency domain to complete channel estimation; a generation method of the plurality of groups of adjustable phase shift pilots is that users are divided into a plurality of groups, a same group uses a same basic pilot matrix to generate a plurality of adjustable phase shift pilots, and different groups use different basic pilot matrices; A self-correlation matrix of the basic pilot matrix is a unit matrix, and a sequence of diagonal elements of a cross-correlation matrix of different basic pilot matrices after FFT transformation is sparse, and each non-zero complex number element of the sequence has a same argument; A plurality of groups of adjustable phase shift pilots are obtained by multiplying the basic pilot matrix and a phase shift factor, and the phase shift factor of each pilot is repeatable; Matrix elements of the angle-delay domain channel have non-uniformly distributed arguments; a probability density function of the arguments is unimodal or multimodal, and positions of the peaks are related to line-of-sight, non-line-of-sight wireless transmission states, and dominant argument values in intermediate states during conversion between the two states; when the dominant argument values of the above three states are similar, a wrapped Gaussian distribution is used to describe the unimodal argument distribution of the angle-delay domain channel matrix elements, and a mean value of the wrapped Gaussian distribution is an argument value in a line-of-sight transmission state, and a variance is related to differences between the dominant argument values in the three states; when the variance of the wrapped Gaussian distribution is less than twice pi, a Gaussian distribution with a same mean value and a same variance is used to replace the wrapped Gaussian distribution. 2.The method of claim 1, wherein: An optimal sparse sequence of 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; a basic pilot sequence group is obtained by performing different cyclic shifts on Zadoff-Chu sequences with a same root, and the basic pilot sequence is a diagonal element of the basic pilot matrix. 3.The method of claim 1, wherein: A modulus value and an argument of a matrix element of the angle-delay domain channel are independent, and each element in the matrix is also independent, and respectively represents a channel complex gain under a corresponding angle and time delay; angle-delay domain statistical channel information includes power distribution and argument distribution information, the power distribution is represented as a sparse matrix, and the argument distribution is represented as a matrix composed of argument distribution mean values of corresponding channel elements; pilot argument information is a non-zero complex number element argument after FFT transformation of a sequence of diagonal elements of the cross-correlation matrix of different basic pilot matrices. 4.The method of claim 1, wherein: An angle-delay domain pilot cross-correlation matrix is quickly solved through a spatial frequency domain pilot cross-correlation matrix and a Toeplitz characteristic of the matrix; DFT / IDFT transformation of diagonal elements of the spatial frequency domain pilot cross-correlation matrix corresponds to first column / first row elements of the angle-delay domain pilot cross-correlation matrix, respectively; The pilot cross-correlation matrix in the angular time delay domain is a Toeplitz matrix, and the entire matrix can be determined by the elements of a certain column or row.

5. A massive MIMO-OFDM receive signal pre-processing method based on multiple sets of adjustable phase-shift pilots, characterized in that: The design incorporates multiple sets of adjustable phase-shift pilots, including: in a large-scale MIMO-OFDM system, users are divided into multiple groups; the same group uses the same basic pilot matrix to generate multiple adjustable phase-shift pilots, while different groups use different basic pilot matrices; the autocorrelation matrix of the basic pilot matrix is ​​an identity matrix, and the sequence of diagonal elements of the cross-correlation matrices of different basic pilot matrices after FFT transformation is sparse, with each non-zero complex element of the sequence having the same argument; multiple sets of adjustable phase-shift pilots are obtained by multiplying the basic pilot matrix by a phase-shift factor, and the phase-shift factor of each pilot can be repeated; the base station preprocesses the received signal according to the following method: For interfering users outside the user group to be estimated, multiply the pilot interference power matrix of the user to be estimated by the corresponding argument zeroing matrix, then multiply it by the pilot argument information between the interfering group and the user group to be estimated, and then add them all to obtain the equivalent pilot argument information matrix of the user group to be estimated. The received signal is multiplied by the argument zeroing matrix of the user to be estimated, and then the imaginary part is subtracted from the real part and divided by the tangent of the equivalent pilot argument information matrix. The result is then used for channel estimation. The argument zeroing matrix refers to the matrix after the average argument of each element of the channel matrix in the angle delay domain is negative; the pilot interference power matrix refers to the equivalent power distribution of the interfering user in the angle delay domain after cyclic shifting, and its phase shift factor is the difference between the pilot phase shift factors of the interfering user and the user to be estimated.

6. The method of claim 5, wherein the method is based on multiple sets of adjustable phase-shift pilots. The optimal sparsity of the sequence after FFT transformation of the diagonal elements of the cross-correlation matrix of different fundamental pilot matrices is a sequence containing only a single non-zero element; the fundamental pilot sequence group is obtained by performing different cyclic shifts on the Zadoff-Chu sequence with the same root, and the fundamental pilot sequence is the diagonal element of the fundamental pilot matrix.

7. A computer program product comprising a computer program, characterized in that: When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

8. Massive MIMO-OFDM communication system comprising a base station and a plurality of user terminals, characterized in that: The user terminal is used to send known sets of adjustable phase-shift 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, it implements the steps of the method according to any one of claims 1-6.

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