A wireless communication method

Abstract

This application proposes a wireless communication method applied to a transmitter and a multi-antenna receiver. At the transmitter, two tensor block terms are encoded based on the original information of a single user terminal. Each tensor block term is an L-column complex matrix. Pilot information is added to each tensor block term to determine the partition of the constellation diagram in which the tensor block term is confined. The constellation diagram is an L-dimensional manifold, where T is the dimension of the space containing the high-dimensional manifold, and L and T are natural numbers greater than 1. Each tensor block term is converted into a quantized set of grid coordinates. The quantized set of grid coordinates is mapped to the partition of the constellation diagram to obtain constellation diagram symbols. A modulation signal is obtained based on the constellation diagram symbols. The modulation signal is transmitted on a time-frequency resource block of the channel determined based on a preamble sequence. The preamble sequence is used to determine the grouping of single user terminals. The time-frequency resource block of the channel is a time-frequency resource block of the channel multiplexed by multiple user terminals, determined based on the grouping of single user terminals.

Description

Technical Field

[0001] This application relates to the field of 5G communication technology, and in particular to a wireless communication method. Background Technology

[0002] Massive random access is a cutting-edge requirement for 5G communication networks in massive machine-type communication (mMTC) scenarios: that is, a large number of transmitters and a single receiver communicate. Given the small volume of single-transaction communication and random user terminal activation behavior in scenarios such as the Internet of Things (IoT) and intelligent transportation, traditional solutions use pre-scheduling of channel time-frequency resources to allocate to user terminals and utilize pilot estimation for coherent demodulation. Such solutions lead to wasted channel resources and additional pilot overhead in massive random access scenarios.

[0003] The key technology supporting mMTC scenarios is solving the passive massive random access problem. Passive massive random access is characterized by a large user terminal population, no pre-allocated subcarriers, and a random and unknown number of user terminals. To improve the average rate and utilize massive user terminal scenarios, the academic community has proposed several new modulation and demodulation schemes, mainly tensor-based modulation (TBM) and sparse regression coding (SRC). The tensor-based TBM scheme was first proposed by decurninge et al. in early 2020. It achieves unlicensed incoherent demodulation of a large number of user terminals through canonical polyadic decomposition (CPD) of tensors and a cube split (CS) constellation diagram design on Grassmann manifolds. For example... Figure 1 As shown, for a multi-order tensor x, with order 3, canonical polyadic (CP) decomposition decomposes this order 3 tensor x into the form of a sum of multiple rank 1 tensors, where the multiple rank 1 tensors are as follows: Define the number K of the smallest sums of matrices with rank 1 in the matrix. a Let be the rank of the matrix. CP decomposition is also called tensor rank decomposition.

[0004] The above scheme can achieve random access for a large number of user terminals on a limited channel time-frequency resource block through orthogonal or approximately orthogonal coding schemes. Mathematically, the tensor decomposition-based scheme guarantees the orthogonality between user terminal data through the sparse structure of tensors and the uniqueness condition of decomposition. Specifically, each user terminal generates a tensor by cross-product of its transmitted symbols. The tensors generated by different user terminals are directly superimposed to produce the actual transmitted composite signal. When the dimension and rank of the tensor satisfy a certain inequality condition, the orthogonality of the signals from different user terminals on this channel can be verified according to the demodulation uniqueness condition.

[0005] The sparse regression coding scheme is an encoding and decoding method designed by Barron and Joseph for AWGN channels based on a continuous channel model. For example... Figure 2 As shown, sparse regression coding generates a codebook by introducing a high-dimensional approximate orthogonal matrix. Each user terminal extracts symbols from the codebook through position modulation. When the codebook dimension is large enough and the number of user terminals is limited, it can be approximated that different user terminals will not collide during codebook extraction.

[0006] Based on the above description, how to achieve [something] when a massive number of user terminals access the network in a massive machine-type communication (mMTC) scenario? Figure 3 The large-scale random access from a large number of single-antenna user terminals to multi-antenna receivers, as shown, is a problem that urgently needs to be solved. Summary of the Invention

[0007] This application provides a wireless communication method that can solve the problem of large-scale random access from a large number of single-antenna user terminals to multi-antenna receivers.

[0008] In a first aspect, embodiments of this application provide a wireless communication method applied to a transmitting end. The method includes: encoding two tensor block terms based on the original information of a single user terminal; each of the two tensor block terms is an L-column complex matrix; adding pilot information to each tensor block term; the pilot information is used to determine that the tensor block term is confined within a decision region of a constellation diagram; the constellation diagram is an L-dimensional Grassmann manifold. Where T is the high-dimensional Grassmann manifold The dimensions of the Euclidean space in which it resides, L and T, are natural numbers greater than 1; each tensor block item is converted into a quantized set of Euclidean space grid coordinates; the quantized set of Euclidean space grid coordinates is mapped to the decision region of the constellation diagram partition to obtain the constellation diagram symbol; a modulation signal is obtained based on the constellation diagram symbol; the modulation signal is transmitted on the time-frequency resource block of the channel determined based on the preamble sequence; the preamble sequence is used to determine the grouping of the single user terminal; the time-frequency resource block of the channel is the time-frequency resource block of the channel multiplexed by multiple user terminals determined based on the grouping of the single user terminal. This improves the tensor coding method by using user codeword decomposition and tensor block term design, combined with pilot information embedding, to efficiently encode tensor block term modulation coupled with high-dimensional Grassmann constellation diagrams. It extends existing large-scale multiple access (mMTC) technologies to multi-dimensional tensor block term coding and high-dimensional Grassmann constellation diagrams, improves existing tensor-based incoherent modulation and demodulation methods, enhances the rate and reliability of large-scale access, and provides greater freedom in channel resource allocation. It can serve as a new wireless communication signal modulation and demodulation standard for 5G networks and is a potential direction for New Radio (NR) standardization.

[0009] As one feasible implementation, adding pilot information to each tensor block item includes: assigning values ​​to the p-th and q-th rows of each of the two tensor block items to determine the pilot information, wherein the pilot information confines the tensor block item to the decision region of the (p, q)-th partition of the constellation graph. Thus, pilot information can be used to confine a tensor block item to a specific decision region R of the (p, q)-th cell. pq This allows for the elimination of amplitude and phase variations introduced by channel fading, enabling the original information bits to be recovered via incoherent demodulation.

[0010] As one feasible implementation, converting each tensor block item into a quantized set of Euclidean space grid coordinates includes: performing grid coordinate quantization on the real and imaginary parts of each dimension of each tensor block item, then the quantized set A of the grid coordinates on the j-th real dimension is obtained. j for:

[0011]

[0012] Among them, B j The quantization set A of the grid coordinates in the j-th real dimension j The number of bits that can be represented. This allows for an increase in the number of information bits carried per unit of spectrum resource, enabling more information to be carried on the same size resource block, thus improving spectrum efficiency.

[0013] As one feasible implementation, the step of performing grid coordinate quantization on the real and imaginary parts of each dimension of each tensor block item includes: using Gray code to convert between decimal and gray coordinates, and then performing grid coordinate quantization on the real and imaginary parts of each dimension of each tensor block item. This allows for the grid coordinate quantization of tensor block items.

[0014] As one feasible implementation, mapping the quantized set of the Euclidean space grid coordinates to the decision region of the constellation diagram partition to obtain the constellation diagram symbol includes: determining the quantized set A of the grid coordinates from the j-th dimension. j Image space mapped to constellation symbols

[0015]

[0016] Where V is a Grassmann manifold The number of partitions, [V] = {1, 2, ..., V}, is the set of all partition indexes; the image space It is composed of V different partitions, each partition being a set of points formed around a reference point according to distance; a mapping rule is determined for generating constellation diagram symbols from quantized grid coordinates within each of the V different partitions; according to the mapping rule, the quantized set A of the grid coordinates in the j-th dimension is... j Constellation diagram symbols are obtained within the decision region of the partition of the constellation diagram. This enables efficient modulation and coding of tensor block-coupled high-dimensional Grassmann constellation diagrams. The mapping from information bits to constellation diagram coordinates still has degrees of freedom, allowing for further optimization and providing greater flexibility in channel resource allocation.

[0017] As one feasible implementation, determining the mapping rules for generating constellation diagram symbols from quantized grid coordinates within each of the V different partitions includes: for the Grassmann manifold The system is divided into V partitions; the coordinates of the reference point of each of the V partitions are determined as g. pq According to the coordinates g of the reference point pq The subscript determines the position index of the partition where the constellation symbol is located, which is the (p, q)th partition of the constellation diagram; the decision region of the (p, q)th partition is determined, and the decision region is the distance from the reference point by coordinates g. pq The region containing the nearest set of points. This, combined with the block form of the tensor, is modulated using a coupled high-dimensional Grassmann constellation diagram, enabling more information to be carried on the same size resource block, thus improving spectral efficiency.

[0018] As one feasible implementation, after transmitting the modulated signal on the time-frequency resource block of the channel determined based on the preamble sequence, the method includes: superimposing the modulated signal on the time-frequency resource block of the channel with the signals of multiple user terminals in the same group to form a composite signal. In this way, large-scale access can be supported by utilizing resource partitioning and user terminal grouping.

[0019] As one feasible implementation, before transmitting the modulated signal on the time-frequency resource block of the channel determined based on the preamble sequence, the method includes: transmitting the preamble sequence in response to uplink unlicensed scheduling signaling of the new air interface. This enables real-time scheduling of randomly accessed user terminals using uplink unlicensed access.

[0020] Secondly, this application provides a wireless communication method applied to a multi-antenna receiver. The method includes: receiving a modulated signal on a time-frequency resource block of a channel; the time-frequency resource block of the channel is a time-frequency resource block of a channel multiplexed by multiple user terminals, determined based on a preamble sequence; the preamble sequence is used to determine the grouping of the single user terminal; demodulating the modulated signal to obtain constellation diagram symbols; performing inverse mapping based on the decision regions of the constellation diagram symbols in the partitions of the constellation diagram to determine the quantization set of Euclidean space grid coordinates; the constellation diagram is an L-dimensional Grassmann manifold. Where T is the high-dimensional Grassmann manifold The dimensions of the Euclidean space in which it resides, L and T, are natural numbers greater than 1; the quantization set of the Euclidean space grid coordinates is converted into at least two tensor block terms; each of the at least two tensor block terms is a complex matrix of L columns; pilot information is removed from each tensor block term, the pilot information being used to determine whether the tensor block term is confined to the decision region of the constellation diagram partition; each of the two tensor block terms is decoded to restore the original information of the single user terminal. In this way, the existing massive MIMO (mass machine-type communication) technology is extended to multi-dimensional tensor block term encoding and high-dimensional Grassmann constellation diagrams, improving the existing tensor-based incoherent modulation and demodulation methods, enhancing the rate and reliability of massive MIMO access, and providing greater freedom in channel resource allocation; it can serve as a new wireless communication signal modulation and demodulation standard under 5G communication networks and is a potential direction for New Radio (NR) standardization.

[0021] As one feasible implementation, before receiving the modulated signal on the time-frequency resource block of the channel, the method includes: receiving a composite signal transmitted on the time-frequency resource block of the channel, wherein the composite signal is obtained by superimposing the signals of multiple user terminals in the same group on the time-frequency resource block of the channel; and demodulating the composite signal to obtain the modulated signal of each individual user terminal in the group of multiple user terminals. Thus, by using a group parallel demodulation algorithm, demodulation is performed in parallel on each group resource block using a multi-core processor, achieving a combination of parallel demodulation and group scheduling. This allows the channel time-frequency resources to be allocated with smaller granularity, and by introducing a multi-core parallel processing method in the receiver, the actual computation time is further reduced.

[0022] As one feasible implementation, the demodulation of the modulated signal to obtain constellation diagram symbols includes: demodulating the modulated signal to obtain at least rotated tensor block terms. The rotation tensor block item The constellation symbol c i，k Obtained through rotation and scaling; where Q is the rotation scaling matrix; k is the user terminal identifier; according to the rotation tensor block item The constellation symbol c is determined by the rotation scaling matrix Q. i，k This decouples the high-dimensional Grassmann constellation diagram from the tensor block modulation, improving the existing tensor-based incoherent demodulation method and enhancing spectral efficiency; it can serve as a potential direction for new radio (NR) projection.

[0023] As one possible implementation, the step based on the rotation tensor block item... The constellation symbol c is determined by the rotation scaling matrix Q. i，k Includes: from the rotation tensor block item Extract the rotating pilot information Recover the position index of the partition; determine the rotation scaling matrix Q, which is a second-order invertible matrix; determine the kernel matrix QQ based on the rotation scaling matrix Q. H And the Frobenius norm transformation relationship of each row; based on the position index of the partition and the kernel matrix QQ H The Frobenius norm transformation relationships for each row eliminate the amplitude variations introduced by rotation effects and channel fading, yielding the constellation diagram symbol c. i，k Therefore, at the demodulation end, the rotation invariance of the Grassmann constellation diagram is utilized for demodulation. By extracting pilot information, the influence of channel fading is eliminated, and the constellation diagram symbol c is obtained. i，k .

[0024] As one feasible implementation, the step of performing inverse mapping of the constellation diagram according to the decision region of the constellation diagram partition to determine the quantization set of Euclidean space grid coordinates includes: determining the quantization set A of grid coordinates from the constellation diagram symbol c to the j-th dimension. j The image space of the inverse mapping

[0025]

[0026] Where V is a Grassmann manifold The number of partitions, [V] = {1, 2, ..., V}, is the set of all partition indexes; the image space It is composed of V different partitions, each partition being a set of points formed around a reference point according to distance; the decision region of the partition where the constellation symbol is located is determined; based on the decision region of the partition, the quantization set A of the grid coordinates of the constellation symbol is determined. j The inverse mapping rule; based on the inverse mapping rule, determine the quantization set A of the Euclidean space grid coordinates in the j-th dimension corresponding to the constellation symbol. j This enables efficient demodulation of tensor block-coupled high-dimensional Grassmann constellation diagrams; the inverse mapping from the grid coordinates of constellation diagram symbols to information bits still has degrees of freedom, allowing for further optimization design; and it provides greater freedom for channel resource allocation.

[0027] As one possible implementation, the constellation diagram symbols determined according to the decision region of the partition are quantized into grid coordinates set A. j The inverse mapping rule is: express the rotation-scaling matrix Q as the product of a diagonal matrix and a unitary matrix: that is... Therefore, at the demodulation end, the rotation scaling matrix Q is used for demodulation to eliminate the effects of rotation scaling distortion and channel fading.

[0028] As one feasible implementation, the quantization set A of the Euclidean space grid coordinates in the j-th dimension corresponding to the constellation diagram symbol is determined according to the inverse mapping rule. j This includes: expressing the rotation-scaling matrix Q as the product of a diagonal matrix and a unitary matrix; and recovering the grid coordinate encoded signal vector t based on the norm normalization matrix and the product of the diagonal matrix and the unitary matrix. i The signal vector t encoded by grid coordinates i It is a tensor block term X i，k The data items in the data; based on the signal vector t i Calculate the inverse transform to recover the quantized set A of the grid coordinates in the j-th dimension. j In this way, the quantized set A of the grid coordinates in the j-th dimension can be recovered.j In order to recover the original transmitted data, the rotation invariance of the constellation diagram and the non-coherent demodulation algorithm are used to cancel the fading of different channels on multiple resource blocks; and the matching problem between user terminals and resource blocks is solved.

[0029] As one feasible implementation, converting the quantization set of the Euclidean space grid coordinates into each of the two tensor block terms includes: according to each tensor block term X... i，k An algorithm for quantizing grid coordinates for the real and imaginary parts of each dimension:

[0030]

[0031] Inverse transformation of the grid coordinate quantization set A in the j-th dimension j Corresponding B j B j The quantization set A of the grid coordinates in the j-th dimension j The number of bits that can be represented; the quantization set A of the Euclidean space grid coordinates. j This is converted into each of the two tensor block terms. This increases the number of information bits carried per unit of spectrum resource, allowing more information to be carried on the same size resource block, improving spectrum efficiency. The combination of block term decomposition and user terminal grouping makes the demodulation process more robust to time-varying and frequency-selective effects of the channel.

[0032] As one feasible implementation, the method further includes: issuing uplink unlicensed scheduling signaling for the new air interface; determining user terminal groups based on the preamble sequence returned by the user terminals; allocating time-frequency resource blocks of the channel according to the user terminal groups; and receiving the modulated signal on the time-frequency resource blocks of the channel. Thus, uplink unlicensed access enables real-time scheduling of randomly accessed user terminals. By employing small resource block grouping, the number of accessing user terminals can be increased, demodulation time complexity can be reduced, and the average demodulation error probability per user terminal can be lowered. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the various embodiments disclosed in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only a few embodiments disclosed in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] The accompanying drawings used in the description of the embodiments or prior art are briefly introduced below.

[0035] Figure 1 A schematic diagram of tensor CP decomposition provided for the background technology;

[0036] Figure 2 A schematic diagram of sparse regression coding provided for the background technology;

[0037] Figure 3 Block diagram of an uplink large-scale random access system;

[0038] Figure 4 A schematic diagram illustrating the information decomposition and tensor symbol formation provided for the first scheme;

[0039] Figure 5 A schematic diagram of the grid coordinates and Grassmann constellation symbols provided for the second scheme;

[0040] Figure 6 This is a schematic diagram of the transmitter-receiver end-to-end model of the method provided in the embodiments of this application;

[0041] Figure 7 This is a block diagram of the functional modules of the transmitting end in the method provided in the embodiments of this application;

[0042] Figure 8 This is a flowchart illustrating the process of the transmitting end in the method provided in the embodiments of this application;

[0043] Figure 9 This is a schematic diagram of tensor block term decomposition in the method provided in the embodiments of this application;

[0044] Figure 10 This is a schematic diagram of user terminal BCH codeword to tensor block term modulation in the method provided in the embodiments of this application;

[0045] Figure 11A The signaling flowchart of uplink unlicensed scheduling mode 1 supported by NR in the method provided in the embodiments of this application is shown below.

[0046] Figure 11B This is a signaling flowchart of the uplink unlicensed scheduling mode 2 supported by NR in the method provided in the embodiments of this application;

[0047] Figure 12 This is a schematic diagram of user terminal grouping and time-frequency resource block allocation in the method provided in the embodiments of this application;

[0048] Figure 13 This is a functional block diagram of the multi-antenna receiver in the method provided in the embodiments of this application;

[0049] Figure 14 A flowchart illustrating the workflow of a multi-antenna receiver in the method provided in this application embodiment;

[0050] Figure 15 A comparison chart of simulation results of the number of iterations of the tensor fast term decomposition algorithm in the method provided in the embodiments of this application;

[0051] Figure 16 This is a comparison chart of tensor block term decomposition combined with iterative detection results in a large-scale uplink multiple access scenario provided in the embodiments of this application;

[0052] Figure 17 This is a comparison chart of detection results combining user terminal grouping and time-frequency resource block segmentation in the large-scale uplink multiple access scenario provided in the embodiments of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings.

[0054] In the description of the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0055] In the description of the embodiments in this application, the term "and / or" is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, and A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple terminals refer to two or more terminals.

[0056] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0057] In the description of the embodiments in this application:

[0058] User terminal or user equipment (UE), including 5G UE, UE or terminal that supports 5G mobile communication standards, such as smartphones, tablets, or handheld devices, or other 5G UE or terminals.

[0059] A base station, including a 5G gNB, is a base station device in a next-generation mobile communication system, or a transmission and reception point (TRP), or other network equipment in a 5G access network (such as a micro base station). In this application, the base station gNB is a multi-antenna receiver.

[0060] A subcarrier is a channel resource that can carry different user terminals. They are generally orthogonal to each other, such as sub-bands, time windows, or spreading sequences. In New Radio (NR), non-orthogonal subcarriers are also introduced to improve channel utilization efficiency.

[0061] Channel: refers to the channel through which information is transmitted.

[0062] Grant-free access is a mechanism that allows user terminals to directly send data without having to send signaling to the base station (gNB) in advance to apply for access to the channel.

[0063] Passive random access refers to unlicensed access, where the base station (gNB) does not pre-allocate subcarriers, but instead allows the user terminal to randomly select subcarriers for access through a pre-designed codebook or other means.

[0064] User terminal grouping scheduling refers to the process by which the base station (gNB) uses downlink control information (DCI) signaling to group a large number of user terminals and match them with channel time-frequency resource blocks (RBs) during uplink or downlink scheduling.

[0065] A constellation diagram is a signal actually transmitted by a user terminal. It is generated by mapping the 0-1 bits in the codeword to coordinates and is generally a set of scattered points on a complex plane.

[0066] The complex plane is a geometric representation of complex numbers constructed using a horizontal real axis and a vertical imaginary axis.

[0067] A tensor is a quantity that transforms according to a specific rule in different reference frames. It is a generalization of the concepts of vector and matrix. For example, a scalar is a zero-order tensor, a vector is a first-order tensor, a matrix (square matrix) is a second-order tensor, and a third-order tensor is like a three-dimensional matrix.

[0068] Tensor decomposition is a method for extracting low-rank information from tensor data. It mainly includes typical multivariate decomposition (CPD), Tucker decomposition, etc., and can be compared with the singular value decomposition (SVD) of a matrix.

[0069] BCH encoding, an abbreviation of Bose, Ray-Chaudhuri, and Hocquenghem, is an encoding method.

[0070] The technical solutions related to this application are described below with reference to the accompanying drawings.

[0071] The first approach is a signal modulation and demodulation scheme based on tensor CP decomposition.

[0072] The mathematical symbols and definitions used in this scheme are referenced in Table 1:

[0073] Table 1

[0074]

[0075] By definition, a tensor The typical form of a multivariate is:

[0076]

[0077] in x i，k This tensor constitutes For each vector, d is the order of the tensor, and K is a natural number greater than 1, where k takes values ​​of 1, 2, ..., K. By performing the Kronecker product operation on them consecutively, the original vectors can be expanded into a tensor of order d. When K such tensors are superimposed, a standard tensor CP form can be obtained. If for a tensor, the maximum value of the parameter K that can be expressed in CP form is Kd. r Then the above expression is called a tensor. The CP rank decomposition has a rank of K. r . A collection of representative constellation diagrams.

[0078] In the first scheme, BCH encoding is first used to add redundancy to the bit data of the original information, such as... Figure 4 As shown, adding redundancy to a payload of length B bits results in a payload of length B+B. BCH The codeword of a bit. This is achieved by using a codeword of length B+B. BCH The codeword is split into multiple segments, and each segment is mapped to a complex vector x according to the constellation diagram. i，k(i = 1, 2, ..., d), i.e., symbol coordinates. Next, this scheme uses the Kronecker product of vectors to construct the CP form of a tensor, thereby forming the constellation symbol of the data transmitted by each user terminal. Then, the constellation symbol s of the data transmitted by user terminal k is... k for:

[0079]

[0080] For a communication system with a single-antenna user terminal and a multi-antenna receiver (N antennas), the data transmitted by each user terminal will be replicated N times at the receiver. This means the data tensor transmitted by user terminal k will have an additional dimension, reflecting the diversity provided by the multiple antennas. The resulting new tensor with channel fading effects is:

[0081]

[0082] Based on the allocated channel time-frequency resource block size, when the number of its resource elements (REs) is not less than the constellation diagram symbols s k Total dimension At this time, user terminal codewords can be transmitted completely on the same time-frequency resource block. Furthermore, due to the unique sparsity and orthogonality of tensors, codewords from a large number of user terminals can be superimposed on the same time-frequency resource block for transmission. The receiver uses a tensor decomposition algorithm to recover the signal.

[0083] Based on the noise characteristics of the wireless channel, the signal received by the receiver can be expressed as follows:

[0084]

[0085] Here, y represents the tensor of the noisy signal from the multi-user terminal obtained by the receiver. In practice, this is achieved by tiling a high-dimensional tensor (excluding the antenna dimension N) onto a two-dimensional time-frequency resource block. Each antenna receives a copy of this time-frequency resource block with different gains. The additive white Gaussian noise of the wireless channel can be expressed as a noise tensor w. Therefore, by concatenating the data from all the receiver antennas and appropriately adjusting the dimensions, a complete tensor of the noisy signal can be recovered.

[0086] From the receiver's perspective, the noisy signal's tensor y needs to undergo CP decomposition to recover the constellation symbol of the data originally transmitted by user terminal k. For successful demodulation, tensor y must satisfy the following CP decomposition uniqueness condition:

[0087] in:

[0088] in:

[0089]

[0090]

[0091]

[0092] For the decomposition of noiseless tensors, this uniqueness condition is a sufficient condition. Theoretically, when the tensor dimensions (T1, T2, ..., T...) are... d (N) and tensor rank K a Satisfying inequalities At that time, the elements x that constitute each order and each rank of the tensor i，k h k All of these can be uniquely recovered. In practice, due to additive white noise introduced by the channel and the increased number of access user terminals (rank K), the recovery process becomes more complex. a The mutual interference effect caused by the increase and the problem that tensor decomposition algorithms converge to the local optimum in non-convex problems mean that accessing a fixed number of user terminals often requires a more abundant tensor dimension allocation than the uniqueness condition to increase sparsity and improve demodulation reliability.

[0093] The first scheme, based on tensor CP modulation, results in strong signal sparsity, leading to high redundancy in time-frequency resource blocks and low spectral efficiency. Since this scheme employs non-coherent demodulation, given the aforementioned tensor signal sparsity, it requires scheduling a large amount of channel time-frequency resources at once. Therefore, it assumes that the channel gain remains constant over a large time and spectral range, which is quite stringent for real-world channels and limits its application scenarios.

[0094] The second scheme provides a one-dimensional cube-split constellation diagram. To achieve incoherent demodulation by combining the tensor CP decomposition of the first scheme, a special design is required for the constellation diagram of the signal transmitted by the user terminal. The second scheme uses Grassmann manifold theory to construct a rotation-invariant constellation diagram, which can be used to cancel channel fading effects and phase offset during transmission, as detailed below.

[0095] The mathematical symbols and definitions used in the second scheme are referenced in Table 2:

[0096] Table 2

[0097]

[0098] By definition, a one-dimensional Grassmann manifold can be expressed as a quotient set:

[0099]

[0100] The definition of the equivalence relation "~" is:

[0101]

[0102] Therefore, a one-dimensional Grassmann manifold represents a T-dimensional complex space that puts all the vectors that coincide after rotation into one class, and the class space after classification, where Q is the rotation matrix.

[0103] Based on this, a Grassmann sphere was constructed, on which all constellation codebooks are composed of constellation symbols of the unit norm. To facilitate demodulation of information on the Grassmann sphere, the scheme employs a cubic splitting method, dividing the sphere into multiple partitions (cells), each partition mapped from Euclidean coordinates in a grid. Figure 5 As shown.

[0104] In third-order tensor modulation and demodulation based on CP decomposition, the order d occupied by the signal vector is 3, that is:

[0105]

[0106] Grassmann flow pattern The complex dimension is d⁻¹ = 2, that is, 2(d⁻¹) = 4 dimensions. Each order vector x i，k The specific method for generating (i.e., a constellation symbol) is as follows:

[0107] S1, for each dimension, perform grid quantization, where each complex dimension includes a real part and an imaginary part, with a total of 4 coordinate axes. Then, the set of quantized coordinates A on the j-th dimension is... j for:

[0108]

[0109] S2, Define the constellation diagram, which is the image space mapping from Euclidean space grid coordinates to constellation diagram symbols.

[0110]

[0111] In the formula, V is the number of cells in the Grassmann constellation diagram, [V] = {1, 2, ..., V} is the set of all cell indices, and a is the set of quantized coordinates A in the j-th dimension. j A grid coordinate within the entire constellation map. It can be considered as being pieced together from different partitions, that is:

[0112]

[0113] In the formula, each partition S i It is a set formed around each reference point according to distance.

[0114] ζ i It is the i-th partition S i The center point, and the points in space are compared with different ζ. i The distance determines which partition (cell) it belongs to.

[0115] S3, Next, determine the mapping g i : That is, the rule for generating constellation diagram symbols from grid coordinates within each cell. Therefore:

[0116]

[0117] Where t = [t1 t2 ... t d-1 ]=ξ d-1 (a)=[ξ1([a1a2]′)...ξ1([a 2d-3 a 2d-2 The function of ξ1 is to adjust the amplitude of each symbol so that its modulus is always less than 1, in order to avoid mapping g. i (a) The output symbol exceeds the range S of the i-th cell. i ; "′" indicates the transpose operation; the j-th element of t j =ξ1([a 2j-1 a 2j ]′), 1≤j≤d-1 satisfy:

[0118]

[0119]

[0120] in, It is the inverse function of the cumulative distribution function (CDF) of the Gaussian distribution.

[0121] S4. During demodulation, a CP decomposition algorithm, such as the ALS algorithm, is first used to decompose the noisy signal tensor received at the receiver. Decompose the tensors to obtain the recovered tensors of each order. and

[0122] S5, the recovered tensor terms Since this is a noisy recovery, it may not be in the constellation chart. The demodulation process involves two steps: (1) determining the cell in which the symbol is located; and (2) normalizing using a reference signal to eliminate the phase difference effect introduced by the fading channel. For example, for constellation symbol x...1，k Its generation formula is:

[0123]

[0124] Based on the previous text regarding t j Definition, |t j | < 1 always holds true. Therefore, the cell's index can be obtained by finding the element with the largest vector magnitude. Furthermore, since this largest element in the original symbol is known to be "1", the recovered tensor symbol... In this process, normalization can be performed using this element as a benchmark to eliminate channel-introduced gain and phase shift. The normalized symbol is then inversely mapped. The original quantized grid coordinates 'a' can be obtained.

[0125] In the second scheme, the number of cells on the one-dimensional Grassmann constellation diagram is influenced by the sign vector x. 1，k x 2，k x 3，k The dimensionality is limited; one-dimensional Grassmann constellation diagram symbols carry little information, and spectral resources are not fully utilized.

[0126] The technical solution of this application will be described in detail below with reference to the accompanying drawings and embodiments. The wireless communication method proposed in this application is inspired by future communication networks for Beyond 5G / 6G and improves performance in New Radio (NR) Machine-Type Communication (MTC) scenarios. The wireless communication method proposed in this application is an extension of one-dimensional tensor coding and one-dimensional Grassmann constellation diagrams in the aforementioned massive MIMO (mass machine-type communication) technologies for mMTC scenarios. It can improve the rate and reliability of massive access and provides greater freedom in channel resource allocation.

[0127] The wireless communication method provided in this application is applied to massive machine-type communication (mMTC) scenarios, including wireless sensor networks, distributed power grids, intelligent transportation, etc. In mMTC scenarios, a large number of potential user terminals are simultaneously accessed, and they have the characteristics of a small number of services and random activation.

[0128] The wireless communication method provided in this application is applied to indoor industrial control networks and outdoor mobile user terminal equipment scenarios. In ultra-reliable low-latency communication (URLLC), it is also known as critical MTC (cMTC), which can balance latency and improve reliability. Performance is improved through optimization of resource scheduling, coding, and retransmission mechanisms.

[0129] The wireless communication method proposed in this application, at the transmitting end, performs coding modulation based on block-term tensors (BTTs) coupled with a high-dimensional Grassmann constellation diagram; including: obtaining two block-term tensors based on the original information of a single user terminal; each of the two block-term tensors is an L-column complex matrix; adding pilot information to each block-term tensor; the pilot information is used to determine that the block-term tensor is confined to the decision region of the (p, q)th partition of the constellation diagram; the constellation diagram is an L-dimensional Grassmann manifold. Where T is a high-dimensional Grassmann manifold. The dimensions of the Euclidean space in which it resides, L and T, are natural numbers greater than 1; each tensor block term is converted into a quantized set of Euclidean space grid coordinates; the quantized set of Euclidean space grid coordinates is mapped to the decision region of the constellation diagram partition to obtain the constellation diagram symbol; the modulation signal is obtained based on the constellation diagram symbol; the modulation signal is transmitted on the time-frequency resource block of the channel based on the preamble sequence; the preamble sequence is used to determine the grouping of a single user terminal; the time-frequency resource block of the channel is the time-frequency resource block of the channel multiplexed by multiple user terminals determined according to the grouping of the single user terminal.

[0130] Figure 6 This is a schematic diagram of an end-to-end communication model between the transmitter and receiver in the method provided in the embodiments of this application. Figure 6 As shown, the end-to-end communication model includes a single-antenna transmitter 61 and a multi-antenna receiver 62.

[0131] At the transmitting end, constellation diagram symbols of tensor block terms are generated by performing BCH encoding and high-dimensional Grassmann mapping on the data sequence of a single user terminal, and modulated signals are transmitted through a single-antenna transmitter 61.

[0132] At the receiving end, the multi-antenna receiver 62 receives the composite signal transmitted by the single-antenna transmitter 61, which is a combination of constellation diagram symbols using block-term tensor-based modulation (BTT-BM) and signals from multiple user terminals. It performs block-term decomposition (BTD) and Grassmann inverse mapping on the modulation signal of each user terminal in the composite signal, and finally obtains the recovered original data sequence through BCH decoding.

[0133] As can be seen from the above, the improvement of the method provided in this application lies in changing the encoding / decoding and modulation / demodulation scheme of the signal at the physical layer. The adjustment of the encoding / decoding method is reflected in the transmitter and receiver processes, and the adjustment of the resource allocation method is reflected in the receiver scheduling process.

[0134] Figure 7This is a functional block diagram of the transmitting end in the method provided in the embodiments of this application. For example... Figure 7 As shown, the transmitter functional module block diagram of one of the user terminals includes:

[0135] The channel coding module 71 is used to perform redundant coding on the original information of a single user terminal to obtain BCH codewords.

[0136] The symbol splitting module 72 is used to split the BCH codeword and modulate it into at least two tensor block items; each of the two tensor block items is a complex matrix of L columns, where L is a natural number greater than 1.

[0137] The constellation diagram symbol generation module 73 is used to add pilot information to each tensor block item; the pilot information is used to determine that the tensor block item is confined to the decision region of the (p, q)th partition of the constellation diagram, and to convert each tensor block item into a quantized set of Euclidean space grid coordinates; the quantized set of Euclidean space grid coordinates is subjected to Grassmann constellation diagram mapping to determine the corresponding constellation diagram symbol; and the modulation signal is obtained according to the constellation diagram symbol; wherein the constellation diagram is an L-dimensional Grassmann manifold corresponding to an L-column complex matrix. Where T is an L-dimensional Grassmann manifold. The dimensions of the Euclidean space in which it resides, L and T, are natural numbers greater than 1.

[0138] The leader sequence determination module 74 is used to randomly select a leader sequence and return it to the receiving end.

[0139] The transmitting module 75 is used to transmit modulated signals on the time-frequency resource block; wherein the time-frequency resource block is obtained by the receiving end by allocating resource blocks after grouping multiple user terminals according to the preamble sequence.

[0140] The mathematical symbols and definitions used in the wireless communication method provided in this application embodiment in the workflow of the transmitting end are shown in Table 3.

[0141] Table 3

[0142]

[0143] Figure 8 This is a flowchart illustrating the process of the transmitter in the method provided in the embodiments of this application, as follows: Figure 8 As shown, for the k-th user terminal, the modulation and coding of the original information can be achieved by performing the following steps S11-S15.

[0144] S11, Redundant encoding is performed on the original information of the single-user terminal to obtain the BCH codeword.

[0145] In one feasible implementation, redundancy can be increased by channel coding of the raw information of user terminal k, which is of length B. For example, Bose–Chaudhuri–Hocquenghem (BCH) coding can be used to add B. BCH 1 redundant bit, forming a length of B+B BCH The BCH codeword of a bit.

[0146] S12, the BCH codeword is segmented and modulated into two tensor blocks; each of the two tensor blocks is a complex matrix of L columns, where L is a natural number greater than 1.

[0147] In one feasible implementation, step S12 can be achieved by the following steps S121-S122.

[0148] S121, split the BCH codeword into two parts with lengths respectively. and The sub-code characters are:

[0149]

[0150] S122, the two lengths are respectively and The subcodewords are modulated into two L-column complex matrices, which can be denoted as tensor block terms X. i，k .

[0151] S13, Add pilot information to each tensor block entry; the pilot information is used to determine the decision region of the tensor block entry within the (p, q)th partition of the constellation graph; this constellation graph is an L-dimensional Grassmann manifold. Where T is the high-dimensional Grassmann manifold The dimensions of the Euclidean space in which it resides, L and T, are natural numbers greater than 1.

[0152] In one feasible implementation, values ​​can be assigned to the p-th and q-th rows of each of the two tensor block entries to determine pilot information; this pilot information is used to limit each tensor block entry to the decision region of the (p, q)-th partition.

[0153] In one feasible implementation, the tensor block item X can be... i，k Add pilot information to confine the block term symbol to a decision region R of a specific (p, q)th partition. pq Inside. The way to add pilot information is to add it to the tensor block items. The p-th row element m p and the element m in the qth row q Assign values ​​respectively:

[0154]

[0155] Where z is a value greater than a fixed constant, in This ensures that the modulus of these two rows is the largest among all rows of the entire tensor block.

[0156] S14 converts each of the two tensor block terms into a quantized set of Euclidean space grid coordinates.

[0157] In one feasible implementation, the transformation can be performed based on the second-order tensor block terms obtained in step S122, and step S13 can be implemented by the following steps S141-S143.

[0158] S141, Define the modulation signal s of a single-user terminal k. k The order of occupancy is d=2, s k =X 1，k X′ 2，k X i，k Let X' be a tensor block term, i = 1, 2, X' 2，k For X 2，k The transpose of .

[0159] S142, as shown Figure 9 As shown, determine K a Expression of composite signal of individual user terminals for:

[0160]

[0161] In the formula, the modulation signal s of user terminal k k This can be based on second-order tensor block term decomposition. It's important to understand that in composite signals... Each addend (Analogous to each rank of CP decomposition) is composed of two tensor block terms X 1，k and X′ 2，k The Kronecker product and a channel fading vector h k The outer product is expanded into a tensor.

[0162] S143, each tensor block item X i，k The complex dimension of the Euclidean space grid coordinates is T⁻¹, and the real dimension is 2⁴ (TL). For the tensor block term X… i，k If the real and imaginary parts of each dimension are quantized into grid coordinates, then the quantized grid coordinate set of the j-th dimension is:

[0163]

[0164] S15, map the quantized set of Euclidean space grid coordinates to the decision region of the constellation diagram partition to obtain the constellation diagram symbol.

[0165] In one feasible implementation, the tensor block term X can be quantized according to the Euclidean space grid coordinate quantization set. i，k If the mapping is represented by a sub-constellation symbol c, then...

[0166] In one feasible implementation, T is defined as a Grassmann manifold. The dimension of the original Euclidean space in which it resides, T>1, is configurable based on parameters such as the physical layer protocol and modulation order. In actual communication, it is a pre-selected integer. A high-dimensional constellation diagram is the image space mapping from Euclidean space grid coordinates to constellation diagram symbols:

[0167]

[0168] In the formula, c represents the constellation diagram symbol; V is the number of partitions (cells), and [V] = {1, 2, ..., V} is the set of all partition indices (cell indices). The entire image space... It is composed of different partitions, namely:

[0169]

[0170] Among them, S i For each partition, S i It is a set of points formed around each quantization point according to their distances, that is:

[0171] In one feasible implementation, a mapping rule for generating constellation diagram symbols from quantized grid coordinates within each of V distinct partitions can be determined; and a quantization set A of the grid coordinates in the j-th dimension can be determined based on the mapping rule. j The corresponding constellation symbol.

[0172] In one feasible implementation, the mapping rule g for generating constellation diagram symbols c from quantized grid coordinates within each of the V distinct partitions can be determined. i : That is, each partition consists of quantized grid coordinates A j The rules for generating sub-constellation diagram symbol c. For the i-th sub-constellation diagram symbol c of the k-th user terminal. i，k Taking the number of columns of tensor block terms L=2 as an example, step S15 is implemented through the following steps S151-S156.

[0173] S151, set the tensor block term X with L = 2 columns. i，k Corresponding Grassmann manifold The manifold is divided into V partitions.

[0174] For example, it can be Cut into One partition.

[0175] S152, determine the mapping rules for generating constellation diagram symbols from quantized grid coordinates within each of the V different partitions. In one feasible implementation, step S152 is achieved through the following steps S1521-1523.

[0176] S1521, determine the coordinates of a reference point for each of the V partitions as g. pq Its coordinate expression is as follows:

[0177]

[0178] S1522, based on the coordinates g of the nearest reference point. pq Determine the constellation symbol c i，k The partition it is located in is the (p, q)th partition.

[0179] For example, for Each partition can be represented by an ordered pair (p, q) as its index.

[0180] In one feasible implementation, the coordinates g of the reference point can be used. pq The subscript of the constellation diagram symbol c is determined to be the (p, q)th partition.

[0181] S1523, determine the decision region of the (p, q)th partition, which is the distance from the reference point coordinate q. pq The region containing the nearest set of points.

[0182] For example, for the (p, q)th partition, the reference point g is given by the formula above. pq Its decision region R pq It can be represented as:

[0183]

[0184] S153, quantize the grid coordinates A in the j-th dimension according to the mapping rule. j The constellation symbol is obtained by mapping it to the decision region of the partition of the constellation diagram.

[0185] In one feasible implementation, then in the decision region R pqWithin the (p, q)th partition, the tensor block item X 1，k The symbol c of the mapped constellation diagram 1，k The following is true:

[0186]

[0187] Where (p, q) is the tensor block term X 1，k The index of the partition where the coordinates of the mapped reference point g1(a) are located.

[0188] For example, the single-user terminal codewords are arranged according to... Figure 10 The code is split as shown, resulting in two sub-codewords. Refer to steps S11-S12 above; they will not be repeated here. The two split sub-codewords are then used to form two tensor block data structures. Using the mapping rules described in step S14 above, two constellation diagram symbols, namely R, are generated. pq Two elements in the array. For a length of... The first codeword of the bit, take the first part of the first codeword. Each bit stores the index (p, q) of the constellation diagram partition, then the following... Each bit length is divided into 2L (TL) parts.

[0189] For example, we define T = 3, i.e., Grassmann manifold The original Euclidean space has a dimension of 3, L=2, so the first 2 bits of the first subcodeword store the constellation map partition index (p, q). The following... Each bit can be mapped to a decimal coordinate using binary Gray code, with B... j For example, with 3 bits, the mapping rules are shown in Table 4:

[0190] Table 4

[0191]

[0192] Combine these 2L(TL) dimensional coordinates pairwise into a single L(TL) dimensional complex coordinate. Then the (j, l)th element of t It is generated by the following formula:

[0193]

[0194] Where parameters t l For the actual data elements in the constellation symbol, each From grid coordinates Generated through transformation.

[0195] S16, obtain the modulation signal according to the constellation diagram symbol; transmit the modulation signal on the time-frequency resource block of the channel based on the preamble sequence; the preamble sequence is used to determine the grouping of a single user terminal; the time-frequency resource block of the channel is the time-frequency resource block of the channel multiplexed by multiple user terminals determined according to the grouping of the single user terminal.

[0196] After the modulated signal is transmitted on the time-frequency resource block of the channel based on the preamble sequence, the modulated signals of multiple user terminals in the same group are superimposed to form a composite signal, which is transmitted on the nth time-frequency resource block, where n is a natural number.

[0197] For example, the modulated signals of multiple user terminals can be superimposed and coupled with the channel fading signal. Then, the composite information of the multiple user terminals actually transmitted on the channel is:

[0198]

[0199] In uplink access with a large number of user terminals, due to the sheer number of terminals and the randomness of their transmission activity, traditional license-based uplink scheduling would generate a large amount of uplink signaling from user terminals requesting access. This significant signaling overhead greatly impacts communication latency, spectral efficiency, and the base station's processing capacity. To better suit practical wireless communication systems and achieve efficient and reliable access for a large number of user terminals, this application provides a wireless communication method that transmits signals based on the New Radio (NR) uplink unlicensed scheduling mechanism and the user terminal grouping mechanism.

[0200] Figure 11A This is a signaling flowchart for uplink unlicensed scheduling mode 1 supported by NR in the method provided in this application embodiment. Figure 11A As shown, the base station configures parameters by broadcasting RRC signaling, including parameters such as uplink scheduling period, time domain resource allocation, and frequency domain resource allocation. The user terminal (UE) receives the RRC signaling broadcast by the base station and transmits it periodically according to its parameters until the base station (gNB) sends new DCI signaling on the downlink control channel (PDCCH) to activate the user terminal or adjust the parameters.

[0201] Figure 11B The signaling flowchart for uplink unlicensed scheduling mode 2 supported by NR in the method provided in the embodiments of this application is shown below. Figure 11B As shown, common parameters and transmission parameters are configured separately. The base station first broadcasts RRC signaling to assign values ​​to common parameters such as the uplink scheduling period. At this time, the user terminal (UE) does not start transmitting immediately, but waits for the base station to send DCI signaling on the downlink control channel (PDCCH) to instruct the uplink UE to activate and configure transmission parameters for time and frequency resource allocation. Then, it starts the periodic transmission process on the allocated time and frequency resources.

[0202] In modes 1 and 2 above, the base station can send new DCI signaling at any time on the downlink control channel (PDCCH) to reconfigure parameters, including uplink scheduling period, time domain resource allocation, frequency domain resource allocation and other parameters.

[0203] Unlike schemes based on tensor CP decomposition, the wireless communication method provided in this application uses tensor block term decomposition at the transmitting end to modulate the transmission signal, resulting in a slower increase in the number of user terminals allowed to access the time-frequency resource block (RB) size over time. Therefore, the wireless communication method provided in this application splits the time-frequency resource block into multiple groups according to the number of user terminals at the base station end (multi-antenna receiver), and evenly distributes the activated user terminals into each group.

[0204] Figure 12 A schematic diagram illustrating user terminal grouping and time-frequency resource block allocation. (Example) Figure 12 As shown, by dividing the time-frequency resource blocks in different time and frequency domains, the base station can not only obtain the performance improvement of spectrum efficiency and other aspects brought about by the block item decomposition structure, but also avoid its disadvantage in terms of the number of access user terminals, and can continuously support large-scale multi-user terminal access.

[0205] In wireless communication systems, since the base station cannot obtain the ID information of user terminals in passive random access, it is necessary to select other user terminal grouping criteria.

[0206] In one feasible implementation, the base station uses a preamble sequence as the basis for grouping user terminals and performs time-frequency resource block (RB) allocation through the following steps S21-S23.

[0207] S21, When each user terminal is first activated in the cell of the current base station, it randomly selects a pilot sequence from the fixed codebook and feeds it back to the base station.

[0208] In one feasible implementation, the base station broadcasts a fixed codebook to the user terminal, and the user terminal randomly selects a lead sequence from the stored fixed codebook and feeds it back to the base station upon initial activation.

[0209] S22, the base station obtains the pilot sequences fed back by multiple user terminals, and determines the grouping of user terminals based on the correlation of the pilot sequences fed back by each user terminal.

[0210] It should be understood that the wireless communication method provided in this application is a non-coherent demodulation scheme, and the preamble sequence is independent of channel estimation. Therefore, collisions of the user terminal on the preamble sequence have no impact on the transmission performance.

[0211] S23, the base station allocates time-frequency resource blocks (RBs) according to the grouping of user terminals, which is achieved through the following steps S231-S235.

[0212] S231, the base station determines the correlation between the number of user terminals and scheduling parameters.

[0213] Generally, each time-frequency resource block (RB) is the same size, and different numbers of time-frequency resource blocks can be selected to carry user terminal information according to requirements.

[0214] In one feasible implementation, the base station performs tensor block decomposition on the received composite information and determines an upper bound on the number of active user terminals carried on a fixed-size time-frequency resource block based on the constraint of the decomposition uniqueness condition.

[0215] In one feasible implementation, the activation rate of user terminals can be estimated based on the specific business scenario, thereby estimating the total number of active user terminals, and adjustments can be made in real time based on the first-come-first-served sequence of user terminals.

[0216] In one feasible implementation, the estimation of the number of user terminals includes the following steps S2311-S2312.

[0217] S2311 issues uplink unlicensed scheduling signaling for the new air interface, estimating the lower bound K of the number of active user terminals. a，total .

[0218] For example, the total number K of scheduled user terminals can be obtained based on uplink unauthorized scheduling signaling. tot , such as K tot =1000; Then, determine the activation ratio, and estimate the lower bound of the number of active user terminals based on the activation ratio and the total number of scheduled user terminals. For example, when the activation ratio is 10%, the estimated lower bound of the number of active user terminals is K. a，total =1000 × 10% = 100.

[0219] S2312, Based on the uniqueness condition of tensor block term decomposition, estimate the number K of active user terminals on a single time-frequency resource block (RB). a，R The upper boundary.

[0220] For example, based on the uniqueness condition of tensor block term decomposition, the inequality relationship between tensor rank and tensor dimension can be obtained, where tensor rank indicates the number of active user terminals. Therefore, the upper bound K for estimating the number of active user terminals on a single time-frequency resource block (RB) can be calculated. a for:

[0221]

[0222] or

[0223]

[0224] Where N is the number of receiving antennas; the row dimensions T1 and T2 of the tensor block terms need to satisfy... That is, the dimension of the tensor block term of the modulation signal of each user terminal must not exceed the number of resource particles (REs) in its time-frequency resource block (RB) #RE; where L is the column dimension of the tensor block term, such as L=2. Solving the above inequality can estimate the upper bound K of the number of active user terminals on a single time-frequency resource block (RB). a，RB .

[0225] S232: Determine the user terminal group based on the preamble sequence returned by the user terminal, and allocate the time-frequency resource blocks of the channel.

[0226] In one feasible implementation, the base station (gNB) can adjust the total number of schedulable time-frequency resource blocks (RBs) based on the correlation between the upper bound of the number of active user terminals and scheduling parameters, and allocate resource blocks based on demand rationality.

[0227] Since the number of activated user terminals will affect various parameters including scheduling cycle and specific time-frequency resource block allocation, it will trigger RRC and DCI signaling.

[0228] In one feasible implementation, the base station can allocate time-frequency resource blocks based on the total number of time-frequency resource blocks (RBs) and the number of active user terminals, triggering RRC and DCI signaling to instruct uplink user terminals to periodically transmit their modulated signals on the channels of the configured time-frequency resource blocks.

[0229] S24, receive the modulated signal on the time-frequency resource block of the configured channel.

[0230] In one feasible implementation, the modulation signals of multiple user terminals are superimposed on the time-frequency resource block (RB) to form a composite signal y. (u) The significance of tensor block decomposition lies in its application to this composite signal y. (u) The tensor blocks are split and preliminarily recovered.

[0231] However, due to the essential uniqueness of block decomposition defining the composite signal y (u) The tensor terms undergo order swapping, scalar scaling, and invertible linear transformations between the complex matrices constituting the tensor block terms. Therefore, the initially recovered tensor block terms... The original tensor block item X transmitted with the time-frequency resource block (RB) i，k The symbols in the mapped constellation diagram cannot be directly matched.

[0232] Based on this, this application proposes a wireless communication method for modulated signals encoded at the transmitting end, which performs constellation diagram symbol recovery based on pilot information at a multi-antenna receiving end (base station). The receiving end, acting as the reverse of the transmitting end, demodulates the modulated information using a numerical algorithm of tensor block term decomposition and Grassmann constellation diagram symbol inverse operation.

[0233] The wireless communication method proposed in this application employs user terminal grouping and multi-core parallel demodulation algorithms at the multi-antenna receiver, and utilizes complexity analysis to select a tensor block term decomposition numerical algorithm for demodulation. The multi-antenna receiver in this application includes the base station described in the above embodiments.

[0234] Figure 13 This is a functional block diagram of the multi-antenna receiver in the method provided in the embodiments of this application. Figure 13 As shown, the receiving end includes the following functional modules:

[0235] The multi-antenna receiving module 31 is used to receive composite signals from multiple user terminals on a configured single time-frequency resource block (RB).

[0236] Tensor block term decomposition module 32 is used to demodulate the composite signal of multiple user terminals on a single time-frequency resource block to obtain constellation diagram symbols.

[0237] The constellation diagram inverse mapping module 33 is used to perform Grassmann constellation diagram inverse mapping based on the obtained constellation diagram symbols to obtain the quantization set of Euclidean space grid coordinates corresponding to the constellation diagram symbols. The quantization set of Euclidean space grid coordinates corresponding to the constellation diagram symbols is used to obtain the BCH codeword by superimposing the two L-column complex matrices.

[0238] BCH decoding module 34 is used to decode BCH codewords to obtain the original information.

[0239] The iterative detection and demodulation module 35 is used to perform iterative detection of composite signals and output demodulated raw information one by one.

[0240] The wireless communication method proposed in this application is applied to a multi-antenna receiver. The method includes: receiving a modulated signal on a time-frequency resource block of a channel based on a preamble sequence; the preamble sequence is used to determine the grouping of a single user terminal; the time-frequency resource block of the channel is a time-frequency resource block of a channel multiplexed by multiple user terminals, determined based on the grouping of the single user terminal; demodulating the modulated signal to obtain constellation diagram symbols; determining the decision region of the quantization set of Euclidean space grid coordinates in the constellation diagram partition based on the constellation diagram symbols; performing inverse mapping of the constellation diagram based on the decision region of the constellation diagram partition to obtain the quantization set of Euclidean space grid coordinates corresponding to the constellation diagram symbols; the constellation diagram is an L-dimensional Grassmann manifold. Where T is a high-dimensional Grassmann manifold. The dimensions of the Euclidean space in which it resides, L and T, are natural numbers greater than 1; the quantized set of Euclidean space grid coordinates is converted into each of the two tensor block terms; each of the two tensor block terms is a complex matrix of L columns; pilot information is removed from each tensor block term; pilot information is used to determine the decision region of the tensor block term within the partition of the constellation diagram; each of the two tensor block terms is decoded to restore the original information of the single-user terminal.

[0241] The mathematical symbols and definitions used are shown in Table 5.

[0242] Table 5

[0243]

[0244] Figure 14 This is a flowchart illustrating the workflow of a multi-antenna receiver in the method provided in this application embodiment. Figure 14 As shown, for a multi-antenna receiver, demodulation of composite signals from multiple users accessed on a single time-frequency resource block can be achieved by executing the following steps S41-S45.

[0245] S41, receive a modulated signal on the time-frequency resource block of the channel; the time-frequency resource block is a time-frequency resource block of the channel multiplexed by multiple user terminals, determined according to a preamble sequence, wherein the preamble sequence is used to determine the grouping of a single user terminal.

[0246] In one feasible implementation, for the u-th group of user terminals scheduled to a certain time-frequency resource block (RB), the composite signal y received by it through the wireless channel (u) for:

[0247]

[0248] Where w (u) Additive white Gaussian noise is introduced into the wireless channel.

[0249] In one feasible implementation, a composite signal transmitted on the nth time-frequency resource block is received, the composite signal being a signal obtained by superimposing signals from multiple user terminals in the same group; the composite signal is demodulated to obtain the modulated signal of each user terminal in the multiple user terminals in the same group.

[0250] S42 demodulates the modulated signal to obtain constellation symbols.

[0251] In one feasible implementation, step S42 can be achieved through the following steps.

[0252] S421, Demodulate the modulated signal to obtain the initially recovered i-th rotated tensor block term. And the channel fading vector, i = 1, 2; the i-th rotated tensor block term initially recovered. For the original tensor block term X i，k The symbol c of the mapped constellation diagram i，k It is obtained through sequential swapping, scalar scaling, and invertible linear transformations between the complex matrices constituting the tensor block terms; where Q is the rotation scaling matrix; k is the user terminal identifier.

[0253] S422, according to The constellation diagram symbol c is determined by the rotation and scaling matrix Q. i，k .

[0254] In one feasible implementation, for the initially recovered tensor block items To facilitate the identification of zones and eliminate amplitude variations and rotation effects introduced by channel fading, step S422 can be achieved through the following steps S4221-S4224.

[0255] S4221, determine the rotation scaling matrix Q, which is a second-order invertible matrix.

[0256] In one feasible implementation, for a two-column tensor block item, first determine constellation diagram symbol c of the original tensor block terms 1，k The rotation scaling matrix Q is a second-order invertible matrix. Right now:

[0257]

[0258] Then there is in: The pilot information term that has not been normalized and de-rotated is denoted as rotated pilot information.

[0259] S4222, Determine the kernel matrix QQ based on the rotation scaling matrix Q. H And the Frobenius norm transformation relationships for each row.

[0260] In one feasible implementation, the constellation diagram symbol c of the original tensor block term can be determined. 1，k After rotation of the invertible matrix Q, the Frobenius norm transformation relationship of each row is as follows:

[0261]

[0262] in The Frobenius norm of a matrix or vector. For the core matrix QQ H Weighted Frobenius norm.

[0263] S4223, Determine the kernel matrix QQ H According to QQ H It possesses Hermitian and positive semidefinite properties, and when the value of the constant z is sufficiently large, for example: At that time, determine the index of the partition containing the tensor block item. Rotation pilot information terms of two row vectors Rotating pilot information item It will still be the initially recovered tensor block items. The row with the two largest Frobenius norms.

[0264] S4224, from the initially recovered rotated tensor block terms Extracting rotating pilot information

[0265] S4225, Determine the rotating pilot information item exist The row position is used to recover the partition position index as the (p, q)th partition from the modulation information of the row position, and the quantization set of Euclidean space grid coordinates is obtained in the decision region of the partition of the constellation diagram.

[0266] In one feasible implementation, based on the constellation diagram symbol c corresponding to the original tensor block term of the transmitter... i，k Extract the pilot information from the first two rows of the matrix, i.e., p=1 and q=2. (Constellation diagram symbol c) 1，k as follows:

[0267]

[0268] S4226, based on the location index of the above partitions and the kernel matrix QQ H The Frobenius norm transformation relation for each row eliminates the amplitude variations introduced by rotation effects and channel fading, yielding the original tensor block term X. i，k The symbol c of the mapped constellation diagram i，k .

[0269] S43, perform inverse mapping of the constellation diagram based on the decision regions of the constellation diagram's partitions to determine the quantization set of the Euclidean space grid coordinates; the constellation diagram is an L-dimensional Grassmann manifold. Where T is a high-dimensional Grassmann manifold. The dimensions of the Euclidean space in which it resides, L and T, are natural numbers greater than 1.

[0270] In one feasible implementation, step S43 can be achieved through the following steps.

[0271] S431, determine from constellation symbol c i，k Quantization set A of grid coordinates in the j-th dimension j The image space of the inverse mapping

[0272]

[0273] Where V is a Grassmann manifold The number of partitions, [V] = {1, 2, ..., V}, is the set of all partition indexes; like space It is composed of V different partitions, each of which is a set of points formed around a reference point according to distance.

[0274] S432, Determine the constellation symbol c i，k The decision area of ​​the partition in which it is located.

[0275] In one feasible implementation, it can be derived from the constellation symbol c. i，k Extract pilot information C; determine pilot information C in constellation symbol c. i，k The decision region of the partition is the (p, q)th partition, which is recovered from the modulation information of the row position.

[0276] S433, determine the constellation symbol c based on the decision region of the partition. i，k Quantized set A of grid coordinates j The inverse mapping rule.

[0277] In one feasible implementation, the quantized set A of the constellation symbol c to the grid coordinates can be determined based on the rotation scaling matrix Q. j The inverse mapping rule.

[0278] In one feasible implementation, the second-order invertible matrix Q is expressed as the product of a diagonal matrix and a unitary matrix, i.e. Thus, the constellation symbol c was determined. i，k to grid coordinate quantization set A j The inverse mapping rule.

[0279] For example, based on the design of tensor block term notation, a second-order invertible matrix Q can be equivalently expressed as the product of a diagonal matrix and a unitary matrix, as follows:

[0280]

[0281] Where N0 is the norm normalization matrix; As mentioned earlier, ti It is the original tensor block term X i，k The actual data items in the diagram are the spherical coordinates of the information points at the original grid coordinates on the constellation map.

[0282] It is understandable that the right-hand side of the equation, U, is a unitary matrix and Λ is a diagonal matrix. This holds true because, according to the property of Schur decomposition, for a Hermitian matrix, there always exists a unitary matrix (i.e., U). H U = I) can be transformed into a diagonal matrix through similarity transformation. Therefore, a second-order invertible matrix Q can be equivalently expressed as the product of a diagonal matrix and a unitary matrix, i.e. Thus, the constellation symbol c was determined. i，k Quantized set A of grid coordinates j The inverse mapping rule.

[0283] S434, Determine the constellation symbol c according to the inverse mapping rule. i，k The quantization set A of the corresponding grid coordinates in the j-th dimension j .

[0284] In one feasible implementation, step S434 can be achieved through the following steps.

[0285] S4341, recover the grid coordinate encoded signal vector t based on the norm normalization matrix. i , t i For tensor block term X i，k The data items in the dataset. The norm normalization matrix N0 is:

[0286]

[0287] Because the unitary matrix U itself does not affect the calculation. The Frobenius norm of each row, and the influence of the diagonal matrix can be directly obtained through... By using norm normalization of each column, the degrees of freedom introduced by the second-order invertible matrix Q can be completely eliminated by the receiver itself, thus recovering the signal vector t with grid coordinates. i .

[0288] S4342, based on signal vector t i Calculate the inverse transform to recover the quantized set A of the grid coordinates in the j-th dimension. j .

[0289] S44 converts the quantized set of Euclidean space grid coordinates into each of two tensor block terms; each of the two tensor block terms is a complex matrix of L columns.

[0290] In one feasible implementation, step S44 is achieved through the following steps S441-S442.

[0291] S441, based on each tensor block item X i，k The real and imaginary parts of each dimension are quantized using a grid coordinate quantization algorithm:

[0292]

[0293] Inverse transformation of the grid coordinate quantization set A in the j-th dimension j Corresponding B j B j The quantization set A of the grid coordinates in the j-th dimension j The number of bits that can be represented.

[0294] S442, using decimal coordinates to Gray code conversion, converts the quantized set of the Euclidean space grid coordinates into each of the two tensor block terms, where each tensor block term X... i，k It is a complex matrix with L columns.

[0295] S45, remove pilot information from each tensor block term.

[0296] S46, decode each of the two tensor block items to restore the original information of the single-user terminal.

[0297] In one feasible implementation, the BCH codeword can be obtained by superimposing two complex matrices of L columns; the BCH codeword can then be decoded to recover the original information of the user terminal.

[0298] The wireless communication method proposed in this application adopts a combination of uplink unlicensed scheduling and user terminal grouping. Each group of user terminals performs independent transmission processes on different time-frequency resource blocks (RBs). Therefore, when the computing resources at the receiving end are sufficient, tensor block term decomposition and symbol recovery processes can be performed in parallel on the composite signals on each resource block.

[0299] The wireless communication method proposed in this application adopts a grouped multi-core parallel demodulation scheme at the receiving end. Based on algorithm research, the time complexity per iteration of the wireless communication method proposed in this application and the tensor CP decomposition algorithm can be compared, as shown in Table 5.

[0300] Table 5

[0301]

[0302] Among them, (L,L,1)-BDT with UE Grouping is a block item decomposition algorithm based on user terminal groups using the Gauss-Newton method provided in the embodiments of this application; (L,L,1)-BDT is a block item decomposition algorithm based on user terminal groups without user terminals provided in the embodiments of this application; and TMB with CPD is a tensor CP decomposition algorithm.

[0303] K a Let G be the total number of active user terminals, G be the number of user terminal groups, and the value of G is equal to the number of time-frequency resource blocks scheduled; L be the number of columns in the complex matrix of block items, L = 2; and N be the number of receiving antennas, N > 1. The tensor dimensions corresponding to different algorithms are constants I1 and I2, respectively; for example: I1 = 30; I2 = 90.

[0304] As can be seen from Table 5, the single-step iterative algorithm of the tensor modulation scheme based on block item decomposition and user terminal grouping proposed in this application has a single-step iteration time complexity that is inversely proportional to the square of the number of groups compared with tensor CP decomposition. That is, the larger the number of frequency resource blocks, the lower the time complexity of a single iteration.

[0305] Furthermore, due to the non-convexity of the tensor decomposition problem, it is impossible to characterize the theoretical limit of the number of iterations required for convergence. Therefore, numerical simulations were performed for different parameters.

[0306] Figure 15 This is a simulation diagram illustrating the number of iterations of the tensor fast term decomposition algorithm in the method provided in the embodiments of this application. Figure 15 As shown in the numerical simulation results, under the same number of information bits, the same signal-to-noise ratio, and the same number of user terminals (without grouping), the demodulation algorithm based on tensor block term decomposition (BTD) in the wireless communication method proposed in this application requires approximately 2 to 4 times more iterations than the demodulation algorithm based on tensor CP decomposition (CPD). Furthermore, since the number of user terminals in a single group is relatively small after grouping, there is still room for further reduction in the number of iterations of block term decomposition. Through simulation and data analysis, when the number of groups is sufficiently large (e.g., G is 3 to 10), after considering both the single-step iteration time complexity and the number of iterations, the time complexity of the block term decomposition method proposed in this application is superior to the tensor CP decomposition scheme of the prior art.

[0307] In summary, this application proposes a wireless communication method that, at the receiving end, can offset channel fading on each time-frequency resource block (RB) by using symbol recovery based on pilot information and a parallel packet demodulation scheme, thus achieving noncoherent demodulation. The time complexity of the parallel demodulation algorithm is reduced and is inversely proportional to the square of the number of user terminal packets. Compared with the scheme based on tensor CP decomposition, the combination of block term decomposition and user terminal packets in the wireless communication method proposed in this application makes the demodulation process more robust to time-varying and frequency-selective effects of the channel.

[0308] The wireless communication method proposed in this application, based on tensor block term modulation and uplink unlicensed multiple access for user terminal groups, can improve spectrum efficiency and demodulation performance, including increasing the number of information bits carried per unit spectrum resource; reducing the average demodulation error probability of a single user terminal under the same signal-to-noise ratio (SNR); and maintaining a larger number of access user terminals under the above conditions.

[0309] The wireless communication method proposed in this application, based on tensor block term modulation and uplink unlicensed multiple access for user terminal groups, is capable of noncoherent demodulation in block fading scenarios. This includes handling cases where channel fading differs across resource blocks, improving the Grassmann constellation diagram structure, and utilizing the rotation invariance of the constellation diagram and noncoherent demodulation algorithms to cancel out different channel fading across multiple resource blocks. It also solves the matching problem between user terminals and resource blocks.

[0310] The wireless communication method proposed in this application, based on tensor block term modulation and uplink unlicensed multiple access for user terminal groups, achieves algorithm parallelization and complexity improvement, including improving the tensor coding method to enable multiple user terminals to access multiple resource blocks in parallel; trying different tensor decomposition algorithms to reduce complexity; and introducing a multi-core parallel processing method in the receiver to further reduce the actual computing time.

[0311] Example 1

[0312] The wireless communication method proposed in Embodiment 1 of this application is for... Figure 3 The large-scale uplink multiple access shown implements an encoding and decoding scheme suitable for noncoherent demodulation for a massive number of uplink single-antenna user terminals and a multi-antenna receiver. Specifically, this application selects an appropriate channel coding method and codeword length for the original user terminal data, and selects the corresponding tensor signal dimension for a fixed-size time-frequency resource block, thereby supporting an uplink user terminal group with a relatively fixed size and activation ratio.

[0313] The specific implementation plan is as follows:

[0314] (1) Transmitter

[0315] S51, for each newly accessed or activated user terminal, a preamble sequence is randomly selected from the preamble codebook broadcast by the base station and fed back to the base station.

[0316] S52, the base station groups multiple user terminals on time-frequency resource blocks based on the correlation between the preamble sequences fed back by each user terminal. In the nth time-frequency resource block, the set of actually accessing active user terminals is...

[0317] S53 uses Bose–Chaudhuri–Hocquenghem (BCH) coding to perform channel coding on the raw information of each user terminal, increasing redundancy.

[0318] S54, the BCH codeword formed by the channel redundancy encoded bit information is processed according to... Figure 12 The codewords are divided into two parts, and the two parts of the BCH codewords are converted into two tensor block items, each of which is an L-column complex matrix.

[0319] S55, perform binary-to-decimal conversion on each tensor block item to obtain the quantization set on the grid coordinates.

[0320] S56. Using the Grassmann constellation mapping rules described in detail above, the quantization set of grid coordinates is converted into constellation symbols.

[0321] S57, According to the definition of tensor block decomposition, the constellation diagram symbols generated by mapping the two tensor block items of each user terminal are used to form the actual transmitted information on the time-frequency resource block through matrix multiplication.

[0322] S58 will transmit the actual information s on the time-frequency resource block. k According to the uplink channel fading vector h of different user terminals k The coupled tensor data form is generated through the matrix-vector Kronecker product.

[0323] S58, superimpose the user terminal tensor data from group n to form a composite signal. Transmitted on time-frequency resource block n.

[0324] (2) Receiver

[0325] S61 uses the uniqueness condition of tensor block term decomposition and information such as user terminal activation ratio to estimate the number of activated user terminals (i.e., tensor rank).

[0326] S62, on each resource block of group n, uses a multi-core processor in parallel to process the received noisy composite signal. Perform tensor block decomposition.

[0327] S63, for the recovered symbols Partition index identification is performed by extracting pilot information.

[0328] S64 utilizes pilot information to eliminate amplitude and phase changes introduced by channel fading.

[0329] S65 recovers the original information using noncoherent demodulation.

[0330] For example, in the following scenario: the time-frequency resource block size is 720 resource particles (REs), the original user terminal information bit length is 204 bits, and a 16-bit redundant bit is added through BCH channel coding to form a codeword of length 220 bits.

[0331] Considering 10–30 activated user terminals, corresponding to 100–300 uplink user terminals in the user terminal group, with an activation ratio of 10%, and the modulated data dimension of a single user terminal being (T1, T2) = (30, 24), the simulation results are as follows. Figure 16 As shown.

[0332] To further reduce the average error probability of the user terminal group, this application employs an iterative detection method. Specifically, the first tensor block decomposition removes successfully demodulated user terminal data from the received composite signal, and then the remaining tensor superposition terms are decomposed again. Because the number of superposition terms is reduced, the robustness of the block decomposition is improved, allowing for the recovery of more data, as shown in the BTDM-SC curve in the figure. If necessary, the above steps can be repeated to achieve a second iterative detection, as shown in the BTDM-SC2 curve in the figure.

[0333] The numerical results above only consider a small number of user terminals accessing a relatively small time-frequency resource block. To support massive user terminal access in massive machine-type communication (mMTC) scenarios, a large time-frequency resource block can be divided and combined with user terminal grouping to achieve simultaneous access for a large number of user terminals. Specific numerical results are as follows: Figure 17 As shown.

[0334] For example, in the following scenario: the total time-frequency resource block size is 7200 resource particles (REs), which are divided into 10 groups along with the user terminal group. Considering the cases where the total number of active user terminals is 100, 300, and 500 respectively, the modulated data dimension of a single user terminal is (T1, T2) = (30, 24). In this case, the information bit length is 395 bits, the signal encoding method is the same as above, plus 45 bits of redundancy based on BCH encoding, forming a 440-bit codeword.

[0335] The simulation results show that combining the coding and modulation method based on tensor block term decomposition and Grassmann constellation diagram with the access method of grouping user terminals into different sub-time-frequency resource blocks can effectively support large-scale user terminal access while improving spectrum efficiency.

[0336] The wireless communication method proposed in Embodiment 1 of this application changes the CP form of tensors in the prior art to a tensor block term form, enabling more information to be carried on the same size resource block and improving spectral efficiency. To match the tensor block term form, a high-dimensional Grassmann constellation diagram coupled with it is used for modulation. Since the uniqueness of tensor block term decomposition is stronger than that of CP decomposition, the number of user terminals carried on a unit time-frequency resource block will decrease. In order to increase the scale of access user terminals, a parallel demodulation method of small resource block grouping is adopted, which can reduce time complexity.

[0337] Example 2

[0338] The wireless communication method proposed in Embodiment 2 of this application utilizes a high-dimensional Grassmann constellation diagram for blind source signal separation in fading channels. Assuming the transmitter has L antennas and the receiver has N antennas, since it is a single-source end-to-end transmission, the single-user terminal signal can be directly modulated into a matrix block mapped by the high-dimensional Grassmann constellation diagram. The number of columns L is equal to the number of transmitter antennas N. The received signal model from the receiver is:

[0339] y = HX′ + w

[0340] in The received noisy tensor signal. This is the gain matrix of the MIMO channel. It is the constellation symbol of the signal. The number of columns of this constellation symbol is exactly the number of transmitting antennas L. For cases with more than two columns, the original design principle is still followed.

[0341] At the receiver, demodulation employs blind source separation, bypassing channel gain matrix estimation and directly utilizing the rotation invariance of the Grassmann constellation diagram for demodulation. Similar to Example 1, channel fading effects are eliminated through pilot information extraction and normalization, thereby enabling the recovery of the original transmitted data.

[0342] This application proposes a wireless communication method, which is a new wireless communication signal modulation and demodulation standard. It achieves strong coupling between the high-dimensional Grassmann constellation diagram and tensor block term modulation by embedding a lead signal and coupling it with the high-dimensional Grassmann constellation diagram. This improves the existing tensor-based incoherent demodulation method and enhances spectral efficiency. It can serve as a potential direction for New Radio (NR) standardization.

[0343] The wireless communication method proposed in this application, through packet scheduling signaling and uplink unlicensed scheduling, enables active user terminals to select a preamble sequence from the codebook as the scheduling basis in passive random access. The receiving end (base station) adjusts the scheduling parameters using RRC / DCI signaling according to the number of user terminals and configures user terminals on different packet time-frequency resources in real time according to the preamble sequence. This achieves real-time scheduling of randomly accessed user terminals; and by utilizing resource partitioning and user terminal grouping, the scheme of this application can support large-scale access.

[0344] The wireless communication method proposed in this application employs a block-parallel demodulation algorithm, where demodulation is performed in parallel using a multi-core processor on each block of resources. Combined with block scheduling, this allows for the allocation of channel time-frequency resources with finer granularity. With the aid of block scheduling, the time complexity of a single-step parallel iteration of block decomposition after employing parallel demodulation is approximately [missing information]. The complexity is inversely proportional to the square of the number of groups G when no groups are grouped, thus reducing the time complexity.

[0345] In one feasible implementation, a scheme based on sparse regression coding (SRC) and message passing decoding can serve as an alternative to this application in massive machine-to-computer communication (mMTC) scenarios.

[0346] The high-dimensional constellation diagram in the wireless communication method proposed in this application is a special spherical coding method. The insertion of pilot information and the mapping of information bits to constellation diagram coordinates still have degrees of freedom and can be further optimized.

[0347] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0348] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0349] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0350] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application.

Claims

1. A wireless communication method, applied at a transmitting end, characterized in that, The method includes: Two tensor block items are obtained by encoding the original information of a single user terminal; each of the two tensor block items is a complex matrix with L columns; Pilot information is added to each tensor block entry; the pilot information is used to determine whether the tensor block entry is confined to a decision region of a partition in the constellation graph; the constellation graph is an L-dimensional Grassmann manifold. Where T is the L-dimensional Grassmann manifold The dimensions of the Euclidean space in which it exists, where L and T are natural numbers greater than 1; Each tensor block item is converted into a quantized set of Euclidean space grid coordinates; The quantized set of the Euclidean space grid coordinates is mapped to the decision region of the constellation diagram to obtain the constellation diagram symbol; The modulation signal is obtained based on the constellation diagram symbols; The modulated signal is transmitted on a time-frequency resource block of the channel determined based on a preamble sequence; the preamble sequence is used to determine the grouping of the single user terminal; the time-frequency resource block of the channel is a time-frequency resource block of a channel multiplexed by multiple user terminals determined according to the grouping of the single user terminal.

2. The method according to claim 1, characterized in that, Adding pilot information to each tensor block item includes: Values ​​are assigned to the p-th and q-th rows of each of the two tensor block entries to determine pilot information, which limits the tensor block entry to the decision region of the (p,q)-th partition of the constellation graph.

3. The method according to claim 1 or 2, characterized in that, The step of converting each tensor block item into a quantization set of Euclidean space grid coordinates includes: For each dimension of the tensor block item, perform grid coordinate quantization on each dimension, then the grid coordinate quantization set A on the j-th real dimension is obtained. j for: Among them, B j The quantization set A of the grid coordinates in the j-th real dimension j The number of bits that can be represented.

4. The method according to claim 3, characterized in that, The step of performing grid coordinate quantization on the real and imaginary parts of each dimension of each tensor block item includes: Gray code and decimal coordinates are used to convert between each other, and the real and imaginary parts of each dimension of each tensor block item are quantized using grid coordinates.

5. The method according to any one of claims 1-4, characterized in that, The step of mapping the quantized set of the Euclidean space grid coordinates to the decision region of the constellation diagram partition to obtain the constellation diagram symbol includes: Determine the quantization set A of the grid coordinates in the j-th dimension. j Image space mapped to constellation symbols Where V is a Grassmann manifold The number of upper partitions, [V] = {1, 2, ..., V} is the set of all partition indexes; the image space It is composed of V different partitions, each partition being a set of points formed around a reference point according to distance; g v (a) Quantized grid coordinates A within each partition j The rules for generating the sub-constellation diagram symbol c, where v is the partition index; Determine the mapping rules for generating constellation diagram symbols from quantized grid coordinates within each of V distinct partitions; The quantization set A of the grid coordinates in the j-th dimension is determined according to the mapping rule. j The constellation symbol is obtained by mapping it to the decision region of the partition of the constellation diagram.

6. The method according to claim 5, characterized in that, The mapping rules for generating constellation diagram symbols from quantized grid coordinates within each of the V different partitions include: For the Grassmann manifold The partitions are cut to obtain V partitions; The coordinates of the reference point for each of the V partitions are determined as g. pq ; Based on the coordinates g of the reference point pq The subscript determines the position index of the partition where the constellation symbol is located, which is the (p,q)th partition of the constellation diagram; The decision region of the (p,q)th partition is determined, wherein the decision region is the distance from the reference point coordinate g. pq The region containing the nearest set of points.

7. The method according to any one of claims 1-6, characterized in that, After transmitting the modulated signal on a time-frequency resource block that determines the channel based on a preamble sequence, the process includes: The modulated signal is superimposed on the signals of multiple user terminals in the same group on the time-frequency resource block of the channel to form a composite signal.

8. The method according to any one of claims 1-7, characterized in that, Before transmitting the modulated signal on the time-frequency resource block of the channel determined based on the preamble sequence, the following steps are included: The pilot sequence is sent in response to the uplink unauthorized scheduling signaling of the new air interface.

9. A wireless communication method applied to a multi-antenna receiver, characterized in that, The method includes: A modulated signal is received on a time-frequency resource block of a channel; the time-frequency resource block of the channel is a time-frequency resource block of a channel multiplexed by multiple user terminals, determined based on a preamble sequence; the preamble sequence is used to determine the grouping of a single user terminal. The modulated signal is demodulated to obtain constellation diagram symbols; The quantization set of Euclidean space grid coordinates is determined by inverse mapping of the constellation diagram symbols to the decision regions of the constellation diagram partitions; the constellation diagram is an L-dimensional Grassmann manifold. Where T is the L-dimensional Grassmann manifold The dimensions of the Euclidean space in which it exists, where L and T are natural numbers greater than 1; The quantized set of the Euclidean space grid coordinates is converted into at least two tensor block terms; each of the at least two tensor block terms is a complex matrix of L columns; In each tensor block entry, pilot information is removed, which is used to determine that the tensor block entry is confined to the decision region of the constellation diagram partition; each of the two tensor block entries is decoded to restore the original information of the single-user terminal.

10. The method according to claim 9, characterized in that, Before receiving the modulated signal on the time-frequency resource block of the channel, the process includes: Receive a composite signal transmitted on the time-frequency resource block of the channel, wherein the composite signal is obtained by superimposing the signals of multiple user terminals in the same group on the time-frequency resource block of the channel; The composite signal is demodulated to obtain the modulated signal of each individual user terminal in the same group of multiple user terminals.

11. The method according to claim 9 or 10, characterized in that, The demodulation of the modulated signal to obtain constellation symbols includes: Demodulating the modulated signal yields at least one rotated tensor block term. The rotation tensor block item The constellation symbol c i,k Obtained through rotation and scaling; where Q is the rotation scaling matrix; k is the user terminal identifier; i = 1, 2, ..., d, d is the order of the tensor; According to the rotation tensor block item The constellation symbol c is determined by the rotation scaling matrix Q. i,k .

12. The method according to claim 11, characterized in that, The term based on the rotation tensor block The constellation symbol c is determined by the rotation scaling matrix Q. i,k ,include: From the rotation tensor block item Extract the rotating pilot information Recover the location index of the partition; Determine the rotation scaling matrix Q, wherein the rotation scaling matrix Q is a second-order invertible matrix; The kernel matrix QQ is determined based on the rotation scaling matrix Q. H And the Frobenius norm transformation relationships for each row; Based on the location index of the partition and the kernel matrix QQ H The Frobenius norm transform relationships of each row eliminate the amplitude variations introduced by rotation effects and channel fading, thus obtaining the constellation diagram symbol c. i,k .

13. The method according to any one of claims 9-12, characterized in that, The step of performing inverse mapping of the constellation diagram according to the constellation diagram symbols in the decision region of the constellation diagram partition to determine the quantization set of Euclidean space grid coordinates includes: Determine the quantization set A of the grid coordinates from the constellation symbol to the j-th dimension. j The image space of the inverse mapping Where V is a Grassmann manifold The number of partitions, [V] = {1, 2, ..., V}, is the set of all partition indexes; the image space It is composed of V different partitions, each partition being a set of points formed around a reference point according to distance; g v (a) Quantized grid coordinates A within each partition j Rules for generating sub-constellation diagram symbols c; Determine the decision region of the partition where the constellation symbol is located; The constellation symbol is determined to the grid coordinate quantization set A based on the decision region of the partition. j The inverse mapping rule; The quantization set A of the Euclidean space grid coordinates in the j-th dimension corresponding to the constellation symbol is determined according to the inverse mapping rule. j .

14. The method according to claim 13, characterized in that, The constellation symbol to grid coordinate quantization set A determined according to the decision region of the partition. j The inverse mapping rule is: The rotation-scaling matrix Q can be expressed as the product of a diagonal matrix and a unitary matrix: that is...

15. The method according to claim 13, characterized in that, The quantization set A of the Euclidean space grid coordinates in the j-th dimension corresponding to the constellation symbol is determined according to the inverse mapping rule. j ,include: The rotation scaling matrix Q is expressed as the product of a diagonal matrix and a unitary matrix; The grid coordinate encoded signal vector t is recovered from the product of the norm normalization matrix, a diagonal matrix, and a unitary matrix. i The signal vector t encoded by grid coordinates i It is a tensor block term X i,k The data items in the tensor; i = 1, 2, ..., d, where d is the order of the tensor; Based on signal vector t i Calculate the inverse transform to recover the quantized set A of the grid coordinates in the j-th dimension. j .

16. The method according to any one of claims 9-15, characterized in that, The step of converting the quantized set of the Euclidean space grid coordinates into each of the two tensor block items includes: For each tensor block item X i,k Let i = 1, 2, ..., d, where d is the order of the tensor; based on each tensor block term X i,k An algorithm for quantizing grid coordinates for the real and imaginary parts of each dimension: Inverse transformation of the grid coordinate quantization set A in the j-th dimension j Corresponding B j B j The quantization set A of the grid coordinates in the j-th dimension j The number of bits that can be represented; The quantization set A of the Euclidean space grid coordinates j Convert to each of the two tensor block items.

17. The method according to any one of claims 9-16, characterized in that, The method further includes: Issue uplink unauthorized scheduling signaling for the new air interface; The user terminal group is determined based on the lead sequence returned by the user terminal; The time-frequency resource blocks of the channel are allocated according to the user terminal group; The modulated signal is received on the time-frequency resource block of the channel.

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