Signal detection method and device, equipment and storage medium

Abstract

The invention provides a signal detection method, apparatus and device, and a storage medium, which can effectively avoid the problem of self-information interference without introducing iterative feedback and feeding back the estimation result of the symbol for self-correction by constructing a state based on an original received symbol and a known channel structure, and improve the detection efficiency of the signal. An interference structure with any complexity can be accurately described, high-precision symbol detection is realized, and the method is suitable for any time delay-Doppler extension scene. In the process of advancing from the state of one received symbol to the state of another received symbol, the differentiated undetermined sending symbols playing a key information updating role between adjacent states are concerned, all sending symbols do not need to be reprocessed, joint search for full combinations is avoided, and the transmission efficiency is improved. The calculation complexity is reduced from the index level to the polynomial level related to the difference scale between the states, and the processing capacity is improved on the premise that the detection performance is not reduced.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a signal detection method, apparatus, device, and storage medium. Background Technology

[0002] Affine Frequency Division Multiplexing (AFDM) signals, after transmission through time-varying channels, are subject to the combined effects of multipath propagation, time delay spread, and Doppler shift, resulting in inter-symbol interference (ISI) and inter-carrier interference (ICI) in the received signal, significantly impacting the detection accuracy of transmitted symbols. To address this issue, related techniques typically construct factor graphs based on the affine domain channel matrix and iteratively pass messages between transmitted and received symbols to achieve interference suppression and symbol detection. However, when the channel has numerous multipath components or the channel matrix has weak sparsity, short loops easily appear in the constructed factor graph, repeatedly propagating between local transmitted and received symbols, causing erroneous convergence during the iteration process and significantly degrading detection performance. Therefore, a high-precision detection method applicable to arbitrary time delay-Doppler spread characteristics is urgently needed. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a signal detection method, apparatus, device and storage medium.

[0004] To achieve the above objectives, this application provides a signal detection method, comprising: obtaining the correspondence between a target matrix and a received symbol sequence, wherein the target matrix is ​​a channel matrix or a matrix obtained by processing the channel matrix; determining one or more undetermined transmission symbols affected by each received symbol to construct the state of the received symbol based on the channel coefficients at each position in the target matrix, wherein the value range of the undetermined transmission symbols is multiple candidate transmission symbols; recursively calculating the detection probability of differentiated transmission symbols in different states under different candidate transmission symbols based on the state of the forward received symbols, the state of the backward received symbols, the state transition probability, the received symbols, and the channel coefficients; wherein the differentiated transmission symbols refer to undetermined transmission symbols that play a key information updating role between adjacent states; the detection probability is the likelihood probability of the predicted received symbol calculated when the differentiated transmission symbol is a certain candidate transmission symbol and the other undetermined transmission symbols are different candidate transmission symbols; and obtaining the estimated transmitted signal based on the detection probability of the differentiated transmission symbols.

[0005] Optionally, based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated, including: calculating the likelihood probability of the predicted received symbol in the first state and the last state respectively, based on the assignment information of each undetermined transmitted symbol in the first state and the last state, as well as its prior probability and channel coefficient; calculating the state transition probability of different states based on the differentiated transmitted symbol; calculating the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol transitioning from the first state to other states based on the likelihood probability of the first state, the state of the forward received symbol, and the state transition probability, as the forward path probability of other states; calculating the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol transitioning from the last state to other states based on the likelihood probability of the last state, the state of the backward received symbol, and the state transition probability, as the backward path probability of other states; and obtaining the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols based on the forward path probability and the backward path probability.

[0006] Optionally, based on the differentiated transmitted symbols, the state transition probabilities of different states are calculated, including: obtaining the undetermined transmitted symbols not involved in the first state from the second state to obtain the differentiated transmitted symbols of the second state; the first state and the second state are adjacent; based on the channel coefficient, the first assignment information corresponding to the first state, the first state and the second state, the predicted second received symbols corresponding to the differentiated transmitted symbols with different assignments are calculated; based on the predicted second received symbols and the second received symbols, the likelihood probability of the second state is obtained; based on the prior probability of the differentiated transmitted symbols with different assignments and the likelihood probability of the corresponding second state, the transition probability from the first state to the second state is calculated as the state transition probability between the first state and the second state.

[0007] Optionally, based on the likelihood probability of the first state, the state of the forward received symbol, and the state transition probability, the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the first state to other states under different candidate transmitted symbols is calculated, serving as the forward path probability for other states; based on the likelihood probability of the last state, the state of the backward received symbol, and the state transition probability, the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the last state to other states under different candidate transmitted symbols is calculated, serving as the backward path probability for other states, including: calculating the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the last state to other states under different candidate transmitted symbols based on the state transition probability between the previous state and the current state, and the forward path probability of the previous state. The likelihood probability of the predicted received symbol under different candidate symbols for the differentiated transmitted symbol in the current state is obtained from the transition starting from the first state, and is used as the forward path probability of the current state; if the previous state is the first state, the forward path probability of the previous state is the likelihood probability of the first state; based on the state transition probabilities between the current state and the next state, and the backward path probability of the next state, the likelihood probability of the predicted received symbol under different candidate symbols for the differentiated transmitted symbol in the current state is calculated from the transition starting from the last state, and is used as the backward path probability of the current state; if the next state is the last state, the backward path probability of the next state is the likelihood probability of the last state.

[0008] Optionally, based on the channel coefficients at each position in the target matrix, one or more undetermined transmission symbols affected by each received symbol are determined to construct the state of the received symbol, including: determining one or more undetermined transmission symbols affected by each received symbol based on the position occupied by the effective channel coefficients in the target matrix; determining the target undetermined transmission symbol corresponding to each received symbol based on the memory length corresponding to each received symbol in the target matrix; and constructing the state of the received symbol based on the target undetermined transmission symbol.

[0009] Optionally, obtaining the correspondence between the target matrix and the received symbol sequence includes: processing the channel matrix to obtain an adjacency matrix; rearranging the adjacency matrix with the goal of concentrating the effective channel coefficients in the channel matrix into the main diagonal region; extracting the row and column sequences corresponding to the rearranged adjacency matrix; processing the positions in the channel matrix according to the row and column sequences to obtain the target matrix; processing the received symbol sequence according to the processing method of the positions in the channel matrix to obtain the correspondence between the target matrix and the received symbol sequence; correspondingly, obtaining the estimated transmitted signal according to the detection probability of the differentiated transmitted symbols includes: sorting the undetermined transmitted symbols corresponding to the target matrix according to the undetermined transmitted symbol sequence corresponding to the channel matrix; obtaining the transmitted symbol according to the detection probability of each differentiated transmitted symbol after sorting.

[0010] Optionally, the channel matrix is ​​processed to obtain an adjacency matrix, including: dividing the channel matrix into blocks to obtain multiple sub-matrices; and calculating the adjacency matrix of each sub-matrix.

[0011] Optionally, based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated, including: if the undetermined transmitted symbol in the first sub-matrix and the undetermined transmitted symbol in the second sub-matrix both have a first undetermined transmitted symbol, then the detection probability of the first undetermined transmitted symbol in the first sub-matrix is ​​used as the prior probability of the first undetermined transmitted symbol in the second sub-matrix to calculate the detection probability of the differentiated transmitted symbol in different states of the second sub-matrix under different candidate transmitted symbols.

[0012] This application also provides a signal detection device, comprising: an acquisition module for acquiring the correspondence between a target matrix and a received symbol sequence, wherein the target matrix is ​​a channel matrix or a matrix obtained by processing the channel matrix; a detection module for determining one or more undetermined transmission symbols affected by each received symbol to construct the state of the received symbol based on the channel coefficients at each position in the target matrix, wherein the value range of the undetermined transmission symbols is multiple candidate transmission symbols; recursively calculating the detection probability of differentiated transmission symbols in different states under different candidate transmission symbols based on the state of the forward received symbols, the state of the backward received symbols, the state transition probability, the received symbols, and the channel coefficients; wherein the differentiated transmission symbol refers to the undetermined transmission symbol that plays a key information updating role between adjacent states; the detection probability is the likelihood probability of the predicted received symbol calculated when the differentiated transmission symbol is a certain candidate transmission symbol and the other undetermined transmission symbols are different candidate transmission symbols; and obtaining the estimated transmitted signal based on the detection probability of the differentiated transmission symbol.

[0013] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0014] This application proposes a signal detection method with high accuracy under arbitrary time-delay-Doppler spread characteristics. In this method, by utilizing the channel coefficients in the affine domain channel matrix and the received symbols, a sequence of interfering undetermined transmitted symbols is constructed for each received symbol to establish a deterministic association between the received and transmitted symbols. Under possible combinations of candidate transmitted symbols, the likelihood probability of the predicted received symbol is calculated based on the deterministic relationship between the received symbol and the known channel coefficients, thus obtaining the detection probability of the undetermined transmitted symbol associated with the received symbol. Since the state construction and probability calculation are based on the original received symbol and the known channel structure, no iterative feedback is introduced, and the symbol estimation results are not fed back for self-correction. Therefore, there are no closed loops in the message propagation path, effectively avoiding self-information interference and improving computational accuracy. Therefore, regardless of whether the channel exhibits sparse or dense characteristics in the affine domain, this embodiment can accurately characterize interference structures of arbitrary complexity, achieving high-precision symbol detection, and is applicable to arbitrary time-delay-Doppler spread scenarios. In addition, in the embodiments of this application, by focusing on the differentiated pending transmission symbols that play a key role in updating information between adjacent states during the process of advancing from the state of one received symbol to the state of another received symbol, without having to reprocess all transmission symbols, the computational complexity is reduced from exponential to polynomial level related to the scale of the differences between states, thereby improving processing capabilities without reducing detection performance. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a signal detection method according to an embodiment of this application; Figure 2 This is a schematic flowchart of a signal detection method according to an embodiment of this application; Figure 3 This is a schematic diagram of the channel matrix and its matrix bandwidth according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating the effect of channel matrix rearrangement before and after an embodiment of this application. Figure 5 This is a schematic diagram illustrating the effect of rearranging the submatrices after dividing the channel matrix into multiple submatrices according to an embodiment of this application, and a comparative schematic diagram of the effect of directly rearranging the channel matrix. Figure 6 This is a bit error rate performance curve according to an embodiment of this application; Figure 7 This is another bit error rate performance curve according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of the signal detection device according to an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by a person skilled in the art to which this application pertains. The terms "first," "second," and similar words used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Communication waveforms are a crucial foundational technology supporting reliable transmission in modern wireless communication. As the basic carrier of signals propagating over the air interface, communication waveforms directly determine key performance dimensions such as spectral efficiency, anti-interference capability, and robustness under multipath and Doppler environments. Currently, Orthogonal Frequency Division Multiplexing (OFDM) effectively combats frequency-selective fading and supports high-speed data transmission in static or quasi-static channel environments due to the strict orthogonality between subcarriers in the frequency domain. However, the orthogonality of OFDM is highly dependent on the time-invariant assumption of the channel. In high-dynamic scenarios (such as vehicle-to-everything (V2X), low-altitude UAV communication, and high-speed rail communication), the channel exhibits significant time-varying characteristics, namely, the simultaneous existence of delay spread and Doppler frequency shift. This leads to the destruction of orthogonality between subcarriers, causing severe inter-carrier interference (ICI) and inter-symbol interference (ISI). In such environments, OFDM waveforms often experience significant performance degradation, making it difficult to meet the future communication requirements for high-reliability, low-latency transmission.

[0020] To address this, a class of modulation waveforms based on linear frequency modulation (AFDM) is proposed. AFDM is based on orthogonal linear frequency modulated subcarriers and achieves a joint representation of time delay and Doppler components in the affine domain (also known as the time delay-Doppler domain), thereby effectively breaking the traditional two-dimensional modeling of time delay and Doppler components.

[0021] However, after passing through a time-varying wireless channel, AFDM signals are still subject to the combined effects of multipath propagation, delay spread, and Doppler shift, which disrupts the orthogonality between affine domain subcarriers. This leads to inter-symbol interference (ISI) and inter-carrier interference (ICI) in the received signal, reducing the detection accuracy of transmitted symbols. To suppress this interference, the Unified Approximate Message Passing (UAMP) method can be used. This method constructs a factor graph model based on the affine domain channel matrix and iteratively passes messages between transmitted and received symbols. Specifically, when updating its estimate, each transmitted symbol directly utilizes only the local observation information provided by its related received symbols; the influence of other transmitted symbols not directly connected depends entirely on the messages passed step-by-step through intermediate received symbols. Ideally, after multiple iterations, the global observation information from all received symbols can gradually converge to each transmitted symbol, thereby achieving accurate symbol estimation.

[0022] However, the performance of this method is highly dependent on the topology of the factor graph. On the one hand, when the channel has many multipath components or a wide delay-Doppler spread, the channel matrix tends to be dense, causing each received symbol to be affected by multiple transmitted symbols, while each transmitted symbol also participates in the generation of multiple received symbols. This dense coupling makes it easy for short loops to form in the factor graph, that is, the message of a certain transmitted symbol forms a closed loop through two or more shared received symbols and feeds back to itself. This self-information feedback mechanism causes small deviations in the initial estimation to be repeatedly amplified during the iteration process, making it difficult to be effectively corrected by messages from other independent paths. On the other hand, to reduce computational complexity, joint interference from non-adjacent transmitted symbols is usually modeled as a Gaussian distribution. However, in real channels with dense multipath components and strongly correlated channel coefficients, real interference is often dominated by a few strongly correlated paths, and its statistical characteristics deviate significantly from the Gaussian assumption, leading to inaccurate local likelihood functions. The messages generated as a result already contain systematic biases.

[0023] The two effects described above are coupled together. Short loops cause distorted messages to propagate cyclically within local loops, while the Gaussian approximation continuously introduces new systematic biases. As a result, the message received by a certain transmitted symbol not only fails to accurately reflect the true state of other transmitted symbols but may also carry amplified erroneous signals. Therefore, even after multiple iterations, it may still converge to a locally consistent but globally erroneous solution, ultimately leading to a severe degradation in detection performance.

[0024] In summary, the methods described above rely on the sparsity assumption of the channel in the affine domain and are only applicable to scenarios with partial delay-Doppler characteristics. Therefore, this application proposes a signal detection method with high accuracy under arbitrary delay-Doppler spread characteristics. In this method, by utilizing the channel coefficients in the affine domain channel matrix and the received symbols, a sequence of interfering undetermined transmitted symbols is constructed for each received symbol to establish a deterministic association between the received and transmitted symbols. Under possible combinations of candidate transmitted symbols, the likelihood probability of the predicted received symbol is calculated based on the deterministic relationship between the received symbol and the known channel coefficients, thus obtaining the detection probability of the undetermined transmitted symbol associated with the received symbol. Since the state construction and probability calculation are based on the original received symbol and the known channel structure, no iterative feedback is introduced, and the symbol estimation results are not fed back for self-correction. Therefore, there are no closed loops in the message propagation path, effectively avoiding self-information interference and improving computational accuracy. Therefore, regardless of whether the channel exhibits sparse or dense characteristics in the affine domain, this embodiment can accurately characterize interference structures of arbitrary complexity, achieve high-precision symbol detection, and is applicable to arbitrary delay-Doppler spread scenarios.

[0025] In addition, in the embodiments of this application, by focusing on the differentiated pending transmission symbols that play a key role in updating information between adjacent states during the process of advancing from the state of one received symbol to the state of another received symbol, without having to reprocess all transmission symbols, the computational complexity is reduced from exponential to polynomial level related to the scale of the differences between states, thereby improving processing capabilities without reducing detection performance.

[0026] refer to Figure 1 This is a schematic diagram illustrating an application scenario of the signal detection method provided in this application embodiment. The application scenario includes a transmitting device 101, a receiving device 102, and a channel environment 103. The transmitting device 101 and the receiving device 102 transmit data via a communication link, and the channel environment 103 in which this communication link is located can be a time-varying dual-select channel with arbitrary delay spread and Doppler frequency shift characteristics.

[0027] Transmitting device 101 includes, but is not limited to, base stations, user terminals, vehicle-mounted communication units, UAV communication modules, satellite communication terminals, or other communication devices capable of generating and transmitting AFDM signals. Receiving device 102 includes, but is not limited to, mobile terminals, base station receivers, vehicle-to-everything (V2X) roadside units, low-altitude aircraft receiving modules, or other devices capable of receiving and processing AFDM signals.

[0028] During communication, the transmitting device 101 maps the information bits to be transmitted into complex symbols, generates a time-frequency coupled transmission signal through AFDM modulation, and transmits it to the channel environment 103. This transmission signal is affected by the combined effects of multipath propagation, time delay spread, and Doppler shift during propagation, resulting in ISI and ICI in the received signal received by the receiving device 102.

[0029] After receiving the interfered AFDM signal, the receiving device 102 acquires the corresponding received symbol sequence and affine domain channel matrix. Subsequently, based on the signal detection method provided in this application embodiment, the receiving device 102 can achieve high-precision and low-complexity transmitted symbol recovery under arbitrary time-delay-Doppler spread characteristics.

[0030] The signal detection method of this application embodiment can be widely applied to various scenarios, including but not limited to vehicle networking, high-speed railway communication, low-altitude drone communication, satellite mobile communication, and wide-area coverage and high-speed mobile access in future 6G systems.

[0031] The following is combined with Figure 1 The application scenarios described above illustrate the specific implementation flow of the signal detection method according to exemplary embodiments of this application. It should be noted that the above application scenarios are merely shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way in this respect. Rather, the embodiments of this application can be applied to any applicable communication system or signal detection scenario.

[0032] refer to Figure 2 The signal detection method in this application embodiment may include: S1. Obtain the correspondence between the target matrix and the received symbol sequence. The target matrix is ​​the channel matrix or a matrix obtained by processing the channel matrix.

[0033] S2. Based on the channel coefficients at each position in the target matrix, determine one or more undetermined transmission symbols that affect each received symbol to construct the state of the received symbol. The range of values ​​for the undetermined transmission symbols is multiple candidate transmission symbols.

[0034] S3. Based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is calculated recursively. The differentiated transmitted symbol refers to the undetermined transmitted symbol that plays a key role in updating information between adjacent states. The detection probability is the likelihood probability of the predicted received symbol calculated when the differentiated transmitted symbol is a certain candidate transmitted symbol and the other undetermined transmitted symbols are different candidate transmitted symbols.

[0035] S4. Based on the detection probability of the differentiated transmitted symbols, the estimated transmitted signal is obtained.

[0036] In some alternative embodiments, the target matrix can reflect the influence of channel characteristics on the transmitted signal, thereby enabling the quantification of the relationship between transmitted and received symbols using the channel coefficients in the target matrix. The received symbol sequence can refer to the sequence obtained by the receiving device processing the acquired raw received signal; this sequence may include multiple received symbols, each corresponding one-to-one with a pending transmitted symbol. The pending transmitted symbol can refer to the raw modulation symbol emitted by the transmitting device and awaiting recovery by the receiving device. The estimated transmitted signal can refer to the estimated transmitted symbol sequence output from the joint detection of multiple pending transmitted symbols; this estimated transmitted symbol sequence corresponds one-to-one with the received symbol sequence in time, and is used to characterize the information symbol stream transmitted by the transmitting device.

[0037] The channel matrix can refer to the affine domain channel response obtained after preprocessing the received signal. In one implementation, the preprocessing process may include: performing frame synchronization on the received signal and removing the cyclic prefix (CP) to recover a valid time-domain data block; subsequently, applying a Discrete Affine Fourier Transform (DAFT) to the time-domain data block to map the signal to the affine domain (i.e., the discrete delay-Doppler domain); in this affine domain, identifying the pilot locations based on the known pilot pattern and using them for channel estimation, thereby constructing a complete affine domain channel matrix. Based on this, removing pilot resource elements and a preset guard interval yields the observation region, which satisfies... The model, in which, It can represent the affine domain channel matrix. It can represent a symbol to be sent. It can represent the received symbol. It can represent noise.

[0038] Based on this, the detection objective in this application embodiment is to obtain the detection probability of the to-be-transmitted symbol based on the received symbol and the channel matrix. . refer to Figure 3 The provided diagram illustrates the relationship between the channel matrix, received symbols, and symbols to be transmitted. The middle area represents the channel matrix, the upper area represents the symbols to be transmitted, and the left area represents the received symbols. The middle area consists of regularly arranged squares, each corresponding to an element in the channel matrix. Each square represents the relationship between a symbol to be transmitted and a received symbol, and the channel gain is described by corresponding channel coefficients. Black squares represent effective channel coefficients, and white squares represent ineffective channel coefficients. In one implementation, effective channel coefficients can be non-zero elements, and ineffective channel coefficients can be zero elements.

[0039] Effective channel coefficients are typically not randomly distributed, but rather concentrated on several diagonals parallel to the main diagonal. Specifically: the effective channel coefficients on the main diagonal and its parallel diagonals correspond to different multipath propagation paths; each diagonal represents an independent propagation path, and all channel coefficients on it share the same combination of time delay and Doppler shift; the number of diagonals equals the number of effective multipath components; the offset of a diagonal relative to the main diagonal is determined by the relative time delay and Doppler shift of that propagation path; generally, the greater the time delay and the greater the Doppler shift, the greater the offset. Furthermore, the channel matrix exhibits a convolutional structure, where each received symbol can depend on a finite number of undetermined transmitted symbols and is obtained by weighting the corresponding channel coefficients.

[0040] After obtaining the target matrix, the channel coefficients at each position in the target matrix can be analyzed to determine the undetermined transmit symbols affected by each received symbol, thereby constructing a state. The range of undetermined transmit symbols is multiple candidate transmit symbols, which can be obtained according to the constellation points defined by the modulation scheme. These constellation points can be agreed upon by both the transmitting and receiving devices. The state can refer to the combination of undetermined transmit symbols that interfere with a certain received symbol at a certain moment. For example, candidate transmit symbols can be Quadrature Amplitude Modulation (QAM) modulation constellation points, but are not limited to this. The number of values ​​for each undetermined transmit symbol's candidate transmit symbols can be... The possible values ​​for each state are .in The number of points in the modulated constellation.

[0041] After constructing the states, the detection probabilities of differentiated transmission symbols in different states under different candidate transmission symbols can be recursively calculated based on the states of the forward and backward received symbols. Differential transmission symbols are pending transmission symbols that play a crucial role in updating information between adjacent states, arising with state transitions. In one implementation, if a newly introduced pending transmission symbol exists compared to the previous state, this newly introduced symbol is a differentiated transmission symbol. For example, if the previous state contains pending transmission symbols {x1, x2, x3}, and the current state contains pending transmission symbols {x2, x3, x4}, then x4 is a differentiated transmission symbol. The differentiated transmission symbol of the first state can be a pending transmission symbol included in the first state itself. There can be one or more differentiated transmission symbols.

[0042] In this embodiment, "forward" and "backward" refer to the preset processing order of received symbols in the target matrix. This processing order can be determined based on the row / column index of the target matrix or specific rules such as channel quality sorting. The state of the forward received symbol can refer to the cumulative state corresponding to the received symbol preceding the current received symbol in the processing sequence. The state of the backward received symbol can refer to the cumulative state corresponding to the received symbol following the current received symbol in the processing sequence. It should be noted that the processing sequence is not necessarily the same as the sequence of received symbols received by the receiving device. Taking the processing sequence of the vertical position (corresponding to the received symbol) of the target matrix as {y4,y2,y1,y3} as an example, and the received symbol sequence as {y1,y2,y3,y4}: when calculating the target object as received symbol y2 and its associated differentiated transmitted symbol, the state of its forward received symbol can refer to the state of y4, which is located before y2 in the sequence, and the state of its backward received symbol can refer to the state of {y1,y3}, which is located after y2 in the sequence.

[0043] The state transition probability refers to the probability of transitioning from the state of one received symbol to the state of another received symbol. In multiple states, if the same undetermined transmit symbols are assigned the same candidate transmit symbols, the contributions of the remaining undetermined transmit symbols to the prediction of the received symbols can be reused or incrementally updated without retracing all symbol combinations. Therefore, the state transition probability can take into account the local changes of differentiated transmit symbols in their respective candidate sets, thus efficiently calculating the transition probabilities between states.

[0044] The predicted received symbol refers to the received symbol calculated given that each undetermined transmitted symbol in the state is assigned a certain set of candidate transmitted symbols. The likelihood probability of the predicted received symbol refers to the conditional probability density between the predicted received symbol and the actual received symbol.

[0045] In this embodiment, based on the state, state transition probability, and channel coefficient of the forward received symbol, the cumulative probability accumulated in the forward path for other undetermined transmission symbols (excluding differentiated transmission symbols) in the current received symbol state can be recursively determined. Based on the state, state transition probability, and channel coefficient of the backward received symbol, the cumulative probability accumulated in the backward path for undetermined transmission symbols in the current received symbol state that are in the same state as the backward received symbol can be recursively obtained. Considering both the cumulative probability of the forward path and the cumulative probability of the backward path, the posterior probability of the differentiated transmission symbol under different candidate transmission symbols is calculated and used as the final detection probability of the differentiated transmission symbol. By combining the detection probabilities of multiple differentiated transmission symbols, the detection probability of each undetermined transmission symbol in the channel matrix can be obtained. For example, each candidate transmission symbol of each undetermined transmission symbol corresponds to a detection probability. The candidate transmission symbol with the highest detection probability can be selected as the detection result of the undetermined transmission symbol, thereby outputting the estimated transmission signal obtained from the detection.

[0046] In this embodiment, by utilizing the convolutional structure of AFDM modulation in the affine domain channel, a generalized global maximum a posteriori detection can be performed for different Doppler and time delay channels, effectively suppressing multipath interference and time-frequency spread interference caused by Doppler spread. This is applicable to the design of AFDM demodulators under different time delay and Doppler scenarios.

[0047] In some optional embodiments, determining one or more undetermined transmission symbols affected by each received symbol to construct the state of the received symbol based on the channel coefficients at each position in the target matrix includes: determining one or more undetermined transmission symbols affected by each received symbol based on the position occupied by the effective channel coefficients in the target matrix; determining the target undetermined transmission symbol corresponding to each received symbol based on the memory length corresponding to each received symbol in the target matrix; and constructing the state of the received symbol based on the target undetermined transmission symbol.

[0048] Memory length Based on matrix bandwidth The matrix bandwidth can be defined as the distance between the two most distant effective channel coefficients for each received symbol within the target matrix. The number of undetermined transmission symbols in each state can be determined based on the memory length. The undetermined transmission symbols corresponding to the memory length can identify the target undetermined transmission symbols. These target undetermined transmission symbols can refer to the undetermined transmission symbols involved in the state. For example... Figure 3 As shown, the matrix bandwidth The value is 5, and the target undetermined transmission symbols are the undetermined transmission symbols corresponding to each position within the middle rectangle.

[0049] Based on this, the construction state is established. ,in This can represent a pending transmission symbol with identifier i. The identification information can be represented as The pending transmission symbol, It can represent the length of memory. It can represent the state corresponding to the received symbol with identification information j.

[0050] In some optional embodiments, based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficients, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated, including: S300. Based on the assignment information of each undetermined transmitted symbol in the first and last states, as well as its prior probability and channel coefficient, calculate the likelihood probability of the predicted received symbol in the first and last states respectively.

[0051] In this embodiment, the first state can refer to the state corresponding to the first received symbol in the first row of the entire target matrix, or it can refer to the state corresponding to the first received symbol in each sub-matrix after the target matrix has been rearranged into multiple sub-matrixes. Similarly, the last state can refer to the state corresponding to the last received symbol in the last row of the entire target matrix or a sub-matrix. The process of rearranging the target matrix can be found in the detailed description below.

[0052] Prior probability refers to the probability that a candidate transmit symbol will be selected as the actual transmit symbol without the current predicted receive symbol being obtained. This prior probability can be preset or obtained through processing.

[0053] S310. Calculate the state transition probabilities for different states based on the differentiated transmitted symbols.

[0054] For each state, the state transition probability between it and the previous state can be calculated. Its expression can be:

[0055] in, Can represent state With state The state transition probability between them It can represent the prior probability of a candidate transmission symbol assigned a value to a differentiated transmission symbol with identifier i. It can represent the state to state The likelihood probability of the predicted received symbol corresponding to the differentiated transmitted symbol with identifier i under the assigned candidate transmitted symbol. The specific value can be determined according to the state. ,state ,state The channel coefficients and received symbols are obtained.

[0056] In this application embodiment, the calculation of the state transition probability is essentially a calculation of the local symbol probability. Regardless of the calculation method or estimation method used to obtain the state transition probability, it is within the protection scope of this application embodiment. In one implementation, under additive white Gaussian noise conditions, the expression for the state transition probability can be:

[0057] Where exp() can represent an exponential function, It can represent the state The corresponding received symbol, This can represent the equivalent delay of the Pth equivalent propagation path. The state in the target matrix can be represented. The identification information is The channel coefficients corresponding to the undetermined transmitted symbols. Can represent state The identification information is The candidate symbols assigned to the undetermined transmission symbols. In the case of colored noise channels, the above expression needs to be adjusted according to the power spectral density of the noise.

[0058] S320. Based on the likelihood probability of the first state, the state of the forward received symbol, and the state transition probability, calculate the likelihood probability of the predicted received symbol for the differentiated transmitted symbol that transitions from the first state to other states under different candidate transmitted symbols, and use it as the forward path probability for other states; based on the likelihood probability of the last state, the state of the backward received symbol, and the state transition probability, calculate the likelihood probability of the predicted received symbol for the differentiated transmitted symbol that transitions from the last state to other states under different candidate transmitted symbols, and use it as the backward path probability for other states.

[0059] S340. Based on the forward path probability and the backward path probability, obtain the detection probability of the differentiated transmission symbol in different states under different candidate transmission symbols.

[0060] In this embodiment of the application, the expression for the detection probability can be:

[0061] in, Can represent The detection probability, Can represent state The forward path probability, Can represent state The probability of the backward path.

[0062] In some optional embodiments, the state transition probabilities of different states are calculated based on the differentiated transmitted symbols, including: S3101. Obtain the pending transmission symbols not involved in the first state from the second state to obtain the differentiated transmission symbols of the second state; the first state and the second state are adjacent.

[0063] In this embodiment of the application, the first state may refer to any state in the target matrix, and the second state may refer to the state adjacent to the first state.

[0064] S3102. Based on the target matrix, the first assignment information corresponding to the first state, the first state and the second state, calculate the predicted second received symbol corresponding to the differentiated transmitted symbol for different assignments.

[0065] In the embodiments of this application, the first assignment information may refer to the candidate transmission symbol assigned to each pending transmission symbol in the first state, and the predicted second reception symbol may refer to the predicted reception symbol corresponding to the second state.

[0066] S3103. Based on the predicted second received symbol and the second received symbol, obtain the likelihood probability of the second state.

[0067] S3104. Based on the prior probability of the differentiated transmission symbol with different assignments and the likelihood probability of the corresponding second state, calculate the transition probability from the first state to the second state, and use it as the state transition probability between the first state and the second state.

[0068] In some optional embodiments, based on the likelihood probability of the first state, the state of the forward received symbol, and the state transition probability, the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the first state to other states under different candidate transmitted symbols is calculated, serving as the forward path probability for other states; based on the likelihood probability of the last state, the state of the backward received symbol, and the state transition probability, the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the last state to other states under different candidate transmitted symbols is calculated, serving as the backward path probability for other states, including: S3201. Based on the state transition probabilities of the previous state and the current state, and the forward path probability of the previous state, calculate the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol that transitions from the first state to the current state, and use it as the forward path probability of the current state; when the previous state is the first state, the forward path probability of the previous state is the likelihood probability of the first state.

[0069] In this embodiment, a recursive algorithm is used for forward iteration. Based on the previous state and the state transition probability, the forward path probability of each state is updated progressively. The expression for this algorithm is as follows:

[0070] in, Can represent state The forward path probability, for the first state, can be set to equal probability for candidate sending symbols.

[0071] S3202. Based on the state transition probabilities of the current state and the next state, and the backward path probability of the next state, calculate the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol that transitions from the last state to the current state, and use it as the backward path probability of the current state; when the next state is the last state, the backward path probability of the next state is the likelihood probability of the last state.

[0072] In this embodiment, a recursive algorithm is used for backward iteration. Based on the next state and the state transition probability, the backward path probability of each state is updated progressively. The expression for this algorithm is as follows:

[0073] in, Can represent state The probability of the backward path, Can represent state With state The state transition probability between them Can represent state The backward path probability. For the last state, the candidate transmission symbols can be set to equal probability.

[0074] In some optional embodiments, obtaining the correspondence between the target matrix and the received symbol sequence may include: S100. Process the channel matrix to obtain the adjacency matrix.

[0075] In one implementation, a channel matrix can be constructed. adjacency matrix Where N represents the number of columns in the channel matrix, M represents the number of rows in the channel matrix, and the adjacency matrix is ​​a... A symmetric matrix.

[0076] S101. The adjacency matrix is ​​rearranged with the goal of concentrating the effective channel coefficients in the channel matrix into the main diagonal region.

[0077] For example, for the adjacency matrix Execute the rearrangement algorithm, where have There are 10 nodes, each corresponding to 10 nodes. A row or a column. Because... It is a symmetric matrix, therefore its rows and columns have duality. The embodiments of this application do not limit the implementation of the rearrangement algorithm. In one implementation, a binary flag vector can be initialized. This is used to record whether each node has been visited and to create an empty list. Store the sorted sequence. Iterate through the nodes to select the unvisited nodes with the smallest degree and start a breadth-first traversal to rearrange the adjacency matrix.

[0078] Specifically, for each selected node Can be initialized to include queue and will Mark it as visited. Then perform a breadth-first search, repeatedly checking the queue. Mid-team node Add it to the sort list In the process, it identifies its unvisited neighbor nodes. ,Right now The The column index corresponding to the non-zero element in the row. These neighboring nodes are sorted in ascending order of degree. All Neighboring nodes can be marked as visited and added to the queue. Awaiting further processing. Continue executing the breadth-first search described above until the current connected component is completely explored. Move to the next unvisited node and repeat the process until all nodes have been traversed. Finally, generate the complete sequence. Reverse the order to obtain the rearranged adjacency matrix. .

[0079] S102. Extract the row and column sequences corresponding to the rearranged adjacency matrix; process the positions in the channel matrix according to the row and column sequences to obtain the target matrix.

[0080] For example, from Extract the row and column reorder numbers to obtain the row number. and column number Then according to and For the channel matrix Rearrange to obtain The rearranged As the target matrix.

[0081] like Figure 4 As shown in the figure, the left side displays the original channel matrix and the corresponding received symbol sequence and the pending transmitted symbol sequence, while the right side displays the rearranged channel matrix and the corresponding received symbol sequence and the pending transmitted symbol sequence. From the figure, it can be seen that in the original channel matrix, the matrix bandwidth corresponding to row y7 is... If the value is 5, then the memory length of the state corresponding to y7 is 5; in the rearranged channel matrix, the matrix bandwidth of the row corresponding to y7 is... If the value is 2, then the memory length of the state corresponding to y7 is 2. It is obvious that the rearranged channel matrix... The matrix bandwidth is smaller than that of the original channel matrix. Yes. Especially in two-path channels, The matrix bandwidth is always equal to 2.

[0082] S103. Process the received symbol sequence according to the processing method to obtain the correspondence between the target matrix and the received symbol sequence.

[0083] according to The received symbol sequence is rearranged to obtain Therefore, based on the rearranged and Calculate the detection probability of differentiated transmitted symbols in different states under different candidate transmitted symbols; obtain the corresponding estimated transmitted symbols based on the detection probability. .

[0084] Furthermore, obtaining the estimated transmission signal based on the detection probability of the differentiated transmission symbols may include: sorting the undetermined transmission symbols corresponding to the target matrix according to the undetermined transmission symbol sequence corresponding to the channel matrix; and obtaining the estimated transmission signal based on the detection probability of each sorted differentiated transmission symbol.

[0085] Since the estimated transmitted symbol sequence of the target matrix does not correspond to the received symbol sequence at the receiver, it is necessary to rearrange the estimated transmitted symbol sequence of the target matrix to obtain an estimated transmitted symbol sequence corresponding to the received symbol sequence as the estimated transmitted signal. For example, according to... right The final test result is obtained by performing a reverse rearrangement. .

[0086] In practical applications, the original channel matrix Typically, the matrix bandwidth is large; for example, in a scenario with N=128 subcarriers, the matrix bandwidth can typically reach 5 to 20. The length of this matrix bandwidth is determined by the parameter configuration of the AFDM signal and the Doppler offset. A larger matrix bandwidth results in a larger memory length and higher algorithm complexity. In this embodiment, the channel matrix and received signal are rearranged before detection, and the results are then reversed to obtain the final detected symbols. This process reduces the matrix bandwidth, which is equivalent to reducing the memory length, thereby reducing the number of undetermined transmission symbols in the state, achieving the goal of reducing the overall algorithm complexity without sacrificing performance. Furthermore, since this method only rearranges the matrix and does not change its inherent input-output relationship, its performance is not compromised.

[0087] In some alternative embodiments, the channel matrix is ​​processed to obtain an adjacency matrix, including: dividing the channel matrix into blocks to obtain multiple sub-matrices; and calculating the adjacency matrix of each sub-matrix.

[0088] In one implementation, the target matrix can be modularly partitioned based on the rows or columns corresponding to the received symbols. Modular partitioning refers to the process of dividing the target matrix into k submatrices based on the remainders of the row or column indices modulo a positive integer k. After modular partitioning, the adjacency matrices of each submatrix can be calculated.

[0089] For example, obtaining the number of blocks For the received symbol sequence and channel matrix Perform the model The expression for the submatrix with segmentation and identification information b can be: Where b can represent the identification information of the submatrix, It can represent blocks.

[0090] Furthermore, the adjacency matrix of the submatrix can be rearranged so that the positions corresponding to different effective channel coefficients in the same row or column of the adjacency matrix are adjacent; the row sequence and column sequence corresponding to the adjacency matrix of the rearranged submatrix can be extracted.

[0091] Furthermore, for each submatrix, the positions within the submatrix are rearranged according to the row and column sequences corresponding to the rearranged adjacency matrix to obtain the target matrix of the submatrix. Each submatrix corresponds to one adjacency matrix and one target matrix. Then, based on the target matrix of the submatrix, the detection probability corresponding to each submatrix can be calculated.

[0092] To establish the correspondence between the target matrix and the received symbol sequence, the same modulus segmentation step can be performed on the received symbol sequence using the same modulus segmentation method as for the target matrix, resulting in multiple received symbol subsequences; its expression is as follows: The multiple received symbol subsequences are rearranged according to the row or column sequence of the target matrix to obtain the received symbol sequence corresponding to the target matrix.

[0093] For example, such as Figure 5 As shown, the target matrix on the left is obtained by directly rearranging the middle channel matrix with the goal of concentrating the effective channel coefficients in the main diagonal region. Clearly, this method does not reduce the memory length of some received symbol states; for example, the memory length of state y5 in the channel matrix is ​​5, while the memory length of state y5 in the rearranged target matrix is ​​6. Based on this, the channel matrix can be modulo-2 divided to obtain two sub-matrices, and these two sub-matrices can be rearranged to obtain the corresponding target matrices. Obviously, all states in the two target matrices do not involve undetermined transmitted symbols corresponding to invalid channel coefficients. Compared to the sub-matrices, this significantly reduces the memory length of states, avoiding the processing of invalid channel coefficients and their corresponding undetermined transmitted symbols, thus reducing computational complexity.

[0094] In some optional implementations, the detection probability corresponding to each submatrix can be calculated based on the target matrix of the submatrix. This can include: performing iterative detection on the target matrix of multiple submatrixes. The iterative detection process can include: To determine whether the first condition is true, the first condition can be whether the number of iterations has reached the number threshold or whether the change in detection probability is less than the magnitude threshold.

[0095] If the first condition is not met, each submatrix can be traversed sequentially. For each submatrix, the following operations can be performed: For the target matrix corresponding to the current submatrix and the received symbol sequence corresponding to the target matrix, calculate the detection probability of the target matrix corresponding to the current submatrix; based on the transmitted symbol sequence corresponding to the current submatrix, reverse the estimated transmitted symbol sequence of the current submatrix to obtain the detection probability of the current submatrix; use the detection probability of the current submatrix as the prior probability of the next submatrix, and calculate the detection probability of the next submatrix based on the target matrix of the next submatrix and the received symbol sequence corresponding to the target matrix. This process continues until the detection probability of the last submatrix is ​​calculated.

[0096] If the first condition is met, the final detection result can be obtained based on the detection probabilities of all submatrices.

[0097] In this embodiment, the method of obtaining the final detection result based on the detection probabilities of all sub-matrices is not limited. The candidate transmitting symbol with the highest confidence among the detection probabilities of all sub-matrices can be selected to obtain the estimated transmitting signal, or a weighted merging algorithm can be used to obtain the estimated transmitting signal. Alternatively, a progressive approximation of the maximum a posteriori detection result can be adopted; as the number of iterations increases, the detection result will gradually approach the optimal result. In practice, a maximum number of iterations or a threshold can be set to balance complexity and detection performance.

[0098] In some optional embodiments, based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated, including: if the undetermined transmitted symbol of the first sub-matrix and the undetermined transmitted symbol of the second sub-matrix both have a first undetermined transmitted symbol, then the detection probability of the first undetermined transmitted symbol in the first sub-matrix is ​​used as the prior probability of the first undetermined transmitted symbol in the second sub-matrix to calculate the detection probability of the differentiated transmitted symbol in different states of the second sub-matrix under different candidate transmitted symbols.

[0099] In this embodiment, determining whether the first and second sub-matrices do not interfere with each other can be achieved by checking whether the first and second sub-matrices simultaneously contain the first undetermined transmission symbol. If both contain the first undetermined transmission symbol, the first and second sub-matrices interfere with each other; otherwise, they do not interfere with each other. If interference exists, the multiple interfering matrices need to be coupled to calculate the detection probability of the undetermined transmission symbol. If they do not interfere with each other, the detection results of the undetermined transmission symbols in the multiple sub-matrices can be calculated separately, and then the detection results can be concatenated to obtain the estimated transmission signal.

[0100] For example, such as Figure 4As shown, the rearranged channel matrix It can be divided into four independent submatrices, namely the submatrices formed by y9, y5, and y1, and the submatrices formed by y... 10 The submatrix formed by y6 and y2 is composed of y 11 The submatrix formed by y7 and y3 is composed of y 12 The submatrix formed by y8 and y4.

[0101] In this embodiment, the performance degradation caused by reduced matrix bandwidth in complex multipath channels is improved. Block processing further reduces matrix bandwidth, thereby reducing the number of states in the matrix and significantly lowering computational complexity. Simultaneously, iterative processing compensates for the performance loss caused by block processing. Specifically, the channel matrix is ​​divided into multiple sub-matrices using different moduli, and the received symbol sequences are processed similarly to obtain the received symbol sequences corresponding to the sub-matrices. Then, the detection probability is calculated by rearranging the sub-matrices and their corresponding received symbol sequences. The detection results obtained from each sub-matrix are used as prior information and passed to other sub-matrices. This process is iterated several times to obtain the final detection result, thereby overcoming the performance loss caused by block processing and achieving the effect of reducing complexity and improving performance.

[0102] In this application, several detection methods are compared: ① Signal detection using the channel matrix as the target matrix (referred to as Method 1); ② Signal detection based on the target matrix obtained by directly rearranging the channel matrix (referred to as Method 2); ③ Signal detection based on dividing the channel matrix into multiple sub-matrices and rearranging them to obtain the target matrix (referred to as Method 3); ④ UAMP (Using the above description, which will not be elaborated upon here, referred to as Method 4); ⑤ Calculating the mean square error between the estimated signal and the true signal using the Minimum Mean Square Error (MMSE) algorithm to obtain the detection result (referred to as Method 5); ⑥ Maximizing the output signal-to-noise ratio by weighting the maximum ratios using the Maximum Ratio Combining (MRC) algorithm according to the conjugate channel gain (referred to as Method 6).

[0103] Experiments were conducted under the same conditions based on the above detection method. These conditions included: simulation at a carrier frequency of 80 GHz with a subcarrier spacing of 15 kHz, according to standard configuration. Each AFDM symbol occupied 64 subcarriers. QPSK modulation was used. In the discrete domain, the maximum delay and maximum Doppler exponent were set to τmax = 2 and αmax = 2, respectively. This configuration supports a maximum moving speed of 1200 km / h. The Doppler exponent was modeled using the Jakes spectrum for each path. The delay exponent for each path was... From the set The selection is uniform, while the first path is fixed. Assume the channel follows a Rayleigh fading model, where the path gain... Extracted independently from a complex Gaussian distribution, its normalized power spectrum is derived from... Given. It is assumed that ideal channel estimation is used, which means that the receiving device has complete knowledge of the channel matrix.

[0104] Through the above experiments, detection results for ideal channel estimation can be obtained, such as... Figure 6 and Figure 7 As shown, the horizontal axis E s / N0 represents the ratio of energy per symbol to power spectral density of one-sided noise, reflecting the magnitude of symbol energy relative to noise intensity per unit bandwidth. Es represents the average energy of each modulation symbol, and N0 represents additive white Gaussian noise; the vertical axis BER represents the ratio of the number of bits incorrectly determined by the receiving device to the total number of transmitted bits, specifically: Figure 6 The performance comparison of different detection algorithms in a two-path (P=2) channel is presented. It can be seen that Method 1 and Method 2 outperform other baseline methods. Specifically, at medium to high signal-to-noise ratios, Method 1 has a gain of approximately 1.8 dB compared to Method 5 and approximately 0.5 dB compared to Method 4. As mentioned earlier, Method 1 achieves symbol-level maximum a posteriori probability detection, making it the optimal detector. The curve for Method 2 almost completely overlaps with that of Method 1, verifying the theoretical result that rearrangement in the two-path case achieves a reduction in memory length without performance degradation.

[0105] Figure 7 The performance of the proposed Method 3 algorithm under a P=3 multipath channel is compared with that of different iteration numbers, as well as with Methods 4, 5, and 6. It can be seen that the performance of Method 3 is highly sensitive to the number of iterations. With only two iterations, Method 3 outperforms Methods 5 and 6 in detection performance and approaches that of Method 4. After three iterations, its performance reaches a level comparable to Method 4.

[0106] The verification data results show that the channel detection method proposed in this application can effectively improve the detection performance of AFDM.

[0107] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0108] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a signal detection device.

[0110] refer to Figure 8 The signal detection device 800 includes: The acquisition module 801 is used to acquire the correspondence between the target matrix and the received symbol sequence. The target matrix is ​​the channel matrix or a matrix obtained by processing the channel matrix.

[0111] The detection module 802 is used to determine one or more undetermined transmission symbols affected by each received symbol based on the channel coefficients at each position in the target matrix to construct the state of the received symbol. The range of undetermined transmission symbols is multiple candidate transmission symbols. Based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficients, the detection probability of the differentiated transmission symbol in different states under different candidate transmission symbols is recursively calculated. The differentiated transmission symbol refers to the undetermined transmission symbol that plays a key role in updating information between adjacent states. The detection probability is the likelihood probability of the predicted received symbol calculated when the differentiated transmission symbol is a certain candidate transmission symbol and the other undetermined transmission symbols are different candidate transmission symbols. The estimated transmission signal is obtained based on the detection probability of the differentiated transmission symbol.

[0112] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0113] The apparatus of the above embodiments is used to implement the corresponding signal detection method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0114] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the signal detection method described in any of the above embodiments.

[0115] Figure 9This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0116] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0117] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0118] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0119] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0120] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0121] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0122] The electronic devices described above are used to implement the corresponding signal detection methods in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0123] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute the signal detection method as described in any of the above embodiments.

[0124] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0125] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the signal detection method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0126] A signal detection method includes: acquiring the correspondence between a target matrix and a sequence of received symbols, wherein the target matrix is ​​a channel matrix or a matrix obtained by processing the channel matrix; determining one or more undetermined transmission symbols affected by each received symbol to construct the state of the received symbol based on the channel coefficients at each position in the target matrix, wherein the range of undetermined transmission symbols is multiple candidate transmission symbols; recursively calculating the detection probability of differentiated transmission symbols in different states under different candidate transmission symbols based on the state of the forward received symbols, the state of the backward received symbols, the state transition probability, the received symbols, and the channel coefficients; wherein the differentiated transmission symbol refers to the undetermined transmission symbol that plays a key role in updating information between adjacent states; the detection probability is the likelihood probability of the predicted received symbol calculated when the differentiated transmission symbol is a certain candidate transmission symbol and the other undetermined transmission symbols are different candidate transmission symbols; and obtaining an estimated transmitted signal based on the detection probability of the differentiated transmission symbol.

[0127] Optionally, based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated, including: calculating the likelihood probability of the predicted received symbol in the first state and the last state respectively, based on the assignment information of each undetermined transmitted symbol in the first state and the last state, as well as its prior probability and channel coefficient; calculating the state transition probability of different states based on the differentiated transmitted symbol; calculating the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol transitioning from the first state to other states based on the likelihood probability of the first state, the state of the forward received symbol, and the state transition probability, as the forward path probability of other states; calculating the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol transitioning from the last state to other states based on the likelihood probability of the last state, the state of the backward received symbol, and the state transition probability, as the backward path probability of other states; and obtaining the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols based on the forward path probability and the backward path probability.

[0128] Optionally, based on the differentiated transmitted symbols, the state transition probabilities of different states are calculated, including: obtaining the undetermined transmitted symbols not involved in the first state from the second state to obtain the differentiated transmitted symbols of the second state; the first state and the second state are adjacent; based on the channel coefficient, the first assignment information corresponding to the first state, the first state and the second state, the predicted second received symbols corresponding to the differentiated transmitted symbols with different assignments are calculated; based on the predicted second received symbols and the second received symbols, the likelihood probability of the second state is obtained; based on the prior probability of the differentiated transmitted symbols with different assignments and the likelihood probability of the corresponding second state, the transition probability from the first state to the second state is calculated as the state transition probability between the first state and the second state.

[0129] Optionally, based on the likelihood probability of the first state, the state of the forward received symbol, and the state transition probability, the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the first state to other states under different candidate transmitted symbols is calculated, serving as the forward path probability for other states; based on the likelihood probability of the last state, the state of the backward received symbol, and the state transition probability, the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the last state to other states under different candidate transmitted symbols is calculated, serving as the backward path probability for other states, including: calculating the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the last state to other states under different candidate transmitted symbols based on the state transition probability between the previous state and the current state, and the forward path probability of the previous state. The likelihood probability of the predicted received symbol under different candidate symbols for the differentiated transmitted symbol in the current state is obtained from the transition starting from the first state, and is used as the forward path probability of the current state; if the previous state is the first state, the forward path probability of the previous state is the likelihood probability of the first state; based on the state transition probabilities between the current state and the next state, and the backward path probability of the next state, the likelihood probability of the predicted received symbol under different candidate symbols for the differentiated transmitted symbol in the current state is calculated from the transition starting from the last state, and is used as the backward path probability of the current state; if the next state is the last state, the backward path probability of the next state is the likelihood probability of the last state.

[0130] Optionally, based on the channel coefficients at each position in the target matrix, one or more undetermined transmission symbols affected by each received symbol are determined to construct the state of the received symbol, including: determining one or more undetermined transmission symbols affected by each received symbol based on the position occupied by the effective channel coefficients in the target matrix; determining the target undetermined transmission symbol corresponding to each received symbol based on the memory length corresponding to each received symbol in the target matrix; and constructing the state of the received symbol based on the target undetermined transmission symbol.

[0131] Optionally, obtaining the correspondence between the target matrix and the received symbol sequence includes: processing the channel matrix to obtain an adjacency matrix; rearranging the adjacency matrix with the goal of concentrating the effective channel coefficients in the channel matrix into the main diagonal region; extracting the row and column sequences corresponding to the rearranged adjacency matrix; processing the positions in the channel matrix according to the row and column sequences to obtain the target matrix; processing the received symbol sequence according to the processing method of the positions in the channel matrix to obtain the correspondence between the target matrix and the received symbol sequence; correspondingly, obtaining the estimated transmitted signal according to the detection probability of the differentiated transmitted symbols includes: sorting the undetermined transmitted symbols corresponding to the target matrix according to the undetermined transmitted symbol sequence corresponding to the channel matrix; obtaining the transmitted symbol according to the detection probability of each differentiated transmitted symbol after sorting.

[0132] Optionally, the channel matrix is ​​processed to obtain an adjacency matrix, including: dividing the channel matrix into blocks to obtain multiple sub-matrices; and calculating the adjacency matrix of each sub-matrix.

[0133] Optionally, based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated, including: if the undetermined transmitted symbol in the first sub-matrix and the undetermined transmitted symbol in the second sub-matrix both have a first undetermined transmitted symbol, then the detection probability of the first undetermined transmitted symbol in the first sub-matrix is ​​used as the prior probability of the first undetermined transmitted symbol in the second sub-matrix to calculate the detection probability of the differentiated transmitted symbol in different states of the second sub-matrix under different candidate transmitted symbols.

[0134] A signal detection device includes: an acquisition module for acquiring the correspondence between a target matrix and a sequence of received symbols, wherein the target matrix is ​​a channel matrix or a matrix obtained by processing the channel matrix; a detection module for determining one or more undetermined transmission symbols affected by each received symbol to construct the state of the received symbol based on the channel coefficients at each position in the target matrix, wherein the value range of the undetermined transmission symbols is multiple candidate transmission symbols; recursively calculating the detection probability of differentiated transmission symbols in different states under different candidate transmission symbols based on the state of the forward received symbols, the state of the backward received symbols, the state transition probability, the received symbols, and the channel coefficients; wherein the differentiated transmission symbols refer to undetermined transmission symbols that play a key information updating role between adjacent states; the detection probability is the likelihood probability of the predicted received symbol calculated when the differentiated transmission symbol is a certain candidate transmission symbol and the other undetermined transmission symbols are different candidate transmission symbols; and obtaining an estimated transmission signal based on the detection probability of the differentiated transmission symbols.

[0135] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement any one of the signal detection methods.

[0136] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0137] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0138] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0139] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A signal detection method, characterized in that, include: Obtain the correspondence between the target matrix and the received symbol sequence, wherein the target matrix is ​​the channel matrix or a matrix obtained by processing the channel matrix; Based on the channel coefficients at each position in the target matrix, one or more undetermined transmission symbols affected by each received symbol are determined to construct the state of the received symbol. The value range of the undetermined transmission symbols is multiple candidate transmission symbols. Based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated; the differentiated transmitted symbol refers to the undetermined transmitted symbol that plays a key role in updating information between adjacent states; the detection probability is the likelihood probability of the predicted received symbol calculated when the differentiated transmitted symbol is a certain candidate transmitted symbol, and when the other undetermined transmitted symbols are different candidate transmitted symbols. The estimated transmission signal is obtained based on the detection probability of the differentiated transmission symbols.

2. The method according to claim 1, characterized in that, Based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated, including: Based on the assignment information of each undetermined transmitted symbol in the first state and the last state, as well as its prior probability and channel coefficient, calculate the likelihood probability of the predicted received symbol in the first state and the last state, respectively. Calculate the state transition probabilities for different states based on the differentiated transmitted symbols; Based on the likelihood probability of the first state, the state of the forward received symbol, and the state transition probability, calculate the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol that transitions from the first state to other states, and use it as the forward path probability of other states; Based on the likelihood probability of the last state, the state of the backward received symbol, and the state transition probability, calculate the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol that transitions from the last state to other states, and use it as the backward path probability for other states. Based on the forward path probability and the backward path probability, the detection probability of differentiated transmission symbols in different states under different candidate transmission symbols is obtained.

3. The method according to claim 2, characterized in that, Based on the differentiated transmitted symbols, calculate the state transition probabilities for different states, including: From the second state, obtain the pending transmission symbols not involved in the first state to obtain the differentiated transmission symbols of the second state; the first state is adjacent to the second state. Based on the channel coefficient, the first assignment information corresponding to the first state, the first state and the second state, calculate the predicted second received symbol corresponding to the differentiated transmitted symbol for different assignments; Based on the predicted second received symbol and the second received symbol, the likelihood probability of the second state is obtained; Based on the prior probabilities of the differentiated transmission symbols with different assignments and the likelihood probabilities of the corresponding second states, the transition probability from the first state to the second state is calculated and used as the state transition probability between the first state and the second state.

4. The method according to claim 2, characterized in that, Based on the likelihood probability of the first state, the state of the forward received symbol, and the state transition probability, calculate the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the first state to other states under different candidate transmitted symbols, as the forward path probability for other states; based on the likelihood probability of the last state, the state of the backward received symbol, and the state transition probability, calculate the likelihood probability of the predicted received symbol for the differentiated transmitted symbol transitioning from the last state to other states under different candidate transmitted symbols, as the backward path probability for other states, including: Based on the state transition probabilities of the previous state and the current state, and the forward path probability of the previous state, the likelihood probability of the predicted received symbol under different candidate transmitted symbols is calculated to obtain the current state from the first state, and is used as the forward path probability of the current state; when the previous state is the first state, the forward path probability of the previous state is the likelihood probability of the first state. Based on the state transition probabilities of the current state and the next state, and the backward path probability of the next state, the likelihood probability of the predicted received symbol under different candidate transmitted symbols for the differentiated transmitted symbol transitioning from the last state to the current state is calculated as the backward path probability of the current state; when the next state is the last state, the backward path probability of the next state is the likelihood probability of the last state.

5. The method according to claim 1, characterized in that, Based on the channel coefficients at each position in the target matrix, determine one or more undetermined transmit symbols affected by each received symbol to construct the state of the received symbol, including: Based on the position of the effective channel coefficients in the target matrix, determine one or more undetermined transmit symbols that will be affected by each received symbol; Based on the memory length corresponding to each received symbol in the target matrix, determine the target undetermined transmission symbol corresponding to each received symbol; Based on the target to be transmitted symbol, construct the state of the received symbol.

6. The method according to claim 1, characterized in that, Obtain the correspondence between the target matrix and the received symbol sequence, including: The channel matrix is ​​processed to obtain the adjacency matrix; The adjacency matrix is ​​rearranged with the goal of concentrating the effective channel coefficients in the channel matrix into the main diagonal region; Extract the row and column sequences corresponding to the rearranged adjacency matrix; The target matrix is ​​obtained by processing the positions in the channel matrix according to the row sequence and the column sequence; The received symbol sequence is processed according to the method of processing the positions in the channel matrix to obtain the correspondence between the target matrix and the received symbol sequence; Accordingly, based on the detection probability of the differentiated transmitted symbols, an estimated transmitted signal is obtained, including: Sort the undetermined transmission symbols corresponding to the target matrix according to the undetermined transmission symbol sequence corresponding to the channel matrix; The transmitted symbol is obtained based on the detection probability of each differentiated transmitted symbol after sorting.

7. The method according to claim 6, characterized in that, The channel matrix is ​​processed to obtain the adjacency matrix, including: The channel matrix is ​​divided into blocks to obtain multiple sub-matrices; Calculate the adjacency matrix of each submatrix.

8. The method according to claim 7, characterized in that, Based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated, including: If both the undetermined transmission symbols in the first submatrix and the undetermined transmission symbols in the second submatrix contain a first undetermined transmission symbol, then the detection probability of the first undetermined transmission symbol in the first submatrix is ​​used as the prior probability of the first undetermined transmission symbol in the second submatrix to calculate the detection probability of the differentiated transmission symbol in different states of the second submatrix under different candidate transmission symbols.

9. A signal detection device, characterized in that, include: An acquisition module is used to acquire the correspondence between a target matrix and a received symbol sequence, wherein the target matrix is ​​a channel matrix or a matrix obtained by processing the channel matrix; The detection module is used to determine one or more undetermined transmission symbols affected by each received symbol based on the channel coefficients at each position in the target matrix in order to construct the state of the received symbol. The value range of the undetermined transmission symbols is multiple candidate transmission symbols. Based on the state of the forward received symbol, the state of the backward received symbol, the state transition probability, the received symbol, and the channel coefficient, the detection probability of the differentiated transmitted symbol in different states under different candidate transmitted symbols is recursively calculated; the differentiated transmitted symbol refers to the undetermined transmitted symbol that plays a key role in updating information between adjacent states; the detection probability is the likelihood probability of the predicted received symbol calculated when the differentiated transmitted symbol is a certain candidate transmitted symbol, and when the other undetermined transmitted symbols are different candidate transmitted symbols; based on the detection probability of the differentiated transmitted symbol, the estimated transmitted signal is obtained.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as claimed in any one of claims 1 to 8.

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