A frequency domain receiving method and device of a multicarrier signal

Abstract

The application provides a frequency domain receiving method and device of a multi-carrier signal, and relates to the technical field of signal processing, which comprises the following steps: for each time domain data frame extracted from the multi-carrier signal, performing windowing processing on the time domain data frame, performing multiplication operation on the pre-computed and cached pseudo-inverse matrix and the time domain data frame after the windowing processing, and obtaining the complex amplitude estimation of each sub-carrier in the frequency domain corresponding to the time domain data frame; the pseudo-inverse matrix is the least square pseudo-inverse matrix of a window weighting matrix, and the window weighting matrix is determined based on a complex exponential basis matrix corresponding to the frequency of each known sub-carrier. The least square pseudo-inverse matrix is pre-computed and cached once in the system initialization stage, so that the matrix-vector multiplication operation is only needed to be performed once during the processing of each frame, the robust estimation of the sub-carrier complex amplitude can be realized, the real-time calculation amount is significantly reduced, and the phase estimation error caused by the FFT fence effect is eliminated.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a frequency domain receiving method and apparatus for multi-carrier signals. Background Technology

[0002] Link-11 is characterized by its use of analog audio channels to transmit voice and data, and it can operate in both HF (High Frequency) and UHF (Ultra High Frequency) bands. Link-11 has a relatively low data transmission rate and lacks interference immunity; however, it can use the HF band and has cross-horizon communication capabilities. Link-11A is a mesh half-duplex data link that uses the conventional Link-11 waveform (CLEW) for data exchange, employing a parallel transmission system and standard information format. Its modulation method is π / 4-DQPSK (Differential Quadrature Phase Shift Keying), using multiple single-tone parallel bearers and differential phase modulation to form frame-based data in the baseband.

[0003] Conventional engineering implementations typically use FFT (Fast Fourier Transform) for audio extraction per frame: extracting audio from each frame... FFT is performed on each sampling point, and the complex spectrum value falling at each known frequency point is read as the single-tone amplitude and phase estimate. Then, the inter-frame phase difference is calculated and DQPSK demodulation hard decision is performed.

[0004] However, the commonly used FFT method for calculating phase is limited by frequency resolution. ( Frequency resolution; Sampling rate; The picket fence effect (the number of points in the FFT transformation, i.e., the number of time-domain sampling points participating in one FFT operation) and the mutual interference between subcarriers. When carrier frequency offset, Doppler frequency offset, or narrowband interference are present, simply calculating the phase of the complex signal at the nearest frequency or the corresponding frequency will collectively generate mutual interference between multiple subcarriers, thereby degrading the performance of DQPSK decision. Summary of the Invention

[0005] This invention provides a frequency domain reception method and apparatus for multi-carrier signals to address the shortcomings of existing technologies where, in the presence of carrier frequency offset, Doppler frequency offset, or narrowband interference, simply calculating the complex signal phase of the nearest or corresponding frequency point will collectively generate mutual interference among multiple subcarriers, thereby degrading the performance of DQPSK decision. This invention achieves robust estimation of subcarrier complex amplitude, thereby significantly reducing the real-time computation load and eliminating phase estimation errors caused by the FFT picket fence effect.

[0006] A frequency domain reception method for multi-carrier signals according to the present invention includes: For each time-domain data frame extracted from the multi-carrier signal, the time-domain data frame is windowed, and the pre-calculated and cached pseudo-inverse matrix is ​​multiplied with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame. The known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​the least-squares pseudo-inverse matrix of the window-weighted matrix, and the window-weighted matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set.

[0007] According to the frequency domain reception method for multi-carrier signals provided by the present invention, the pseudo-inverse matrix is ​​pre-calculated in the following manner: Based on the preset sampling rate, subcarrier spacing, and each known subcarrier frequency in the set of fixed subcarrier frequencies, construct the complex exponential basis matrix corresponding to each known subcarrier frequency; The complex exponential basis matrix is ​​weighted using a window function to obtain a window-weighted matrix; Based on the window weighting matrix, calculate the least-squares pseudo-inverse matrix of the window weighting matrix.

[0008] According to a frequency domain reception method for a multi-carrier signal provided by the present invention, after performing windowing processing on each time domain data frame extracted from the multi-carrier signal, and multiplying a pre-calculated and buffered pseudo-inverse matrix with the windowed time domain data frame to obtain the complex amplitude estimate of each subcarrier corresponding to the time domain data frame in the frequency domain, the method further includes: The overall phase difference between frames is calculated based on the complex amplitude estimates of each subcarrier in the frequency domain corresponding to the current time-domain data frame and the complex amplitude estimates of each subcarrier in the frequency domain corresponding to the previous time-domain data frame. Based on the compensation factor, the overall phase difference between the frames is compensated to obtain the modulation phase difference between the frames; the compensation factor is determined based on the length of the time-domain data frame and the known subcarrier frequency. The phase parameters in the modulation phase difference between the frames are extracted, and the corresponding modulation bits are determined based on the relationship between the phase parameters and the preset phase interval.

[0009] According to the frequency domain reception method of a multi-carrier signal provided by the present invention, the step of determining the corresponding modulation bit based on the affiliation relationship between the phase parameter and a preset phase interval includes: If the phase parameter is within the first preset phase interval, then the corresponding modulation bit is determined to be [0, 0]. If the phase parameter is within the second preset phase interval, then the corresponding modulation bit is determined to be [0, 1]. If the phase parameter is within the third preset phase interval, then the corresponding modulation bit is determined to be [1, 0]; If the phase parameter is within the fourth preset phase interval, then the corresponding modulation bit is determined to be [1, 1].

[0010] According to the frequency domain reception method for multi-carrier signals provided by the present invention, after obtaining the complex amplitude estimates of each subcarrier corresponding to the time domain data frame in the frequency domain, the method further includes: Based on the window weighting matrix, the complex amplitude estimation of each subcarrier corresponding to the time-domain data frame in the frequency domain, and the windowed time-domain data frame, the fitting residual is calculated. The covariance matrix is ​​estimated based on the noise variance and the window weighting matrix.

[0011] According to the frequency domain reception method for multi-carrier signals provided by the present invention, after estimating the covariance matrix based on the noise variance and the window weighting matrix, the method further includes: The diagonal elements of the covariance matrix are used as the uncertainty of the complex amplitude estimation of each subcarrier in the frequency domain; The soft information weight of each subcarrier is determined based on the reciprocal of the uncertainty of the complex amplitude estimation of each subcarrier in the frequency domain. The soft information weights of each subcarrier are accumulated to obtain the confidence estimate of the time-domain data frame.

[0012] The present invention also provides a frequency domain receiving device for multi-carrier signals, comprising: The complex amplitude estimation module is used to perform windowing processing on each time-domain data frame extracted from the multi-carrier signal, and to multiply the pre-calculated and cached pseudo-inverse matrix with the windowed time-domain data frame to obtain the complex amplitude estimation of each subcarrier in the frequency domain corresponding to the time-domain data frame. The known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​the least-squares pseudo-inverse matrix of the window-weighted matrix, and the window-weighted matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set.

[0013] The present invention 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 computer program to implement the frequency domain reception method of the multi-carrier signal as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the frequency domain reception method for multi-carrier signals as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the frequency domain reception method for multi-carrier signals as described above.

[0016] This invention provides a frequency domain reception method and apparatus for multi-carrier signals. It determines a window weighting matrix based on the complex exponential basis matrix corresponding to each known subcarrier frequency in a fixed subcarrier frequency set, and pre-calculates the least-squares pseudo-inverse matrix of the window weighting matrix. This pre-calculation of the window weighted least-squares projection and the pseudo-inverse matrix ensures that the pre-calculated pseudo-inverse matrix itself contains a mapping from the time domain to the frequency domain. Therefore, by multiplying the pre-calculated and buffered pseudo-inverse matrix with the windowed time-domain data frame, the complex amplitude estimates of each subcarrier corresponding to the time-domain data frame in the frequency domain are obtained. This allows for simultaneous frequency domain transformation and least-squares transformation without explicitly performing an FFT transformation on the time-domain data frame. Frequency domain reception can be achieved by using the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set as the model basis vector, and directly estimating the complex amplitude of each subcarrier in the time domain using a windowed least squares algorithm. Since the known subcarrier frequencies of the Link-11CLEW signal are fixed constants (forming a fixed subcarrier frequency set), the pseudo-inverse matrix of least squares can be calculated and cached once during the system initialization phase. Therefore, in the subsequent demodulation process, only one matrix-vector multiplication is needed per frame to obtain the complex amplitude estimate of all subcarriers, thereby significantly reducing the real-time computation load and eliminating the phase estimation error caused by the FFT picket fence effect. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the frequency domain reception method for multi-carrier signals provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the receiving and processing flow of CLEW signals for multiple carriers provided in an embodiment of the present invention.

[0020] Figure 3 This is a time-domain waveform diagram of the generated preamble real signal provided in an embodiment of the present invention.

[0021] Figure 4 This is a time-domain waveform diagram of the generated phase reference frame provided in an embodiment of the present invention.

[0022] Figure 5 This is a comparison chart of bit error rate test results provided in an embodiment of the present invention.

[0023] Figure 6 This is a schematic diagram of the confidence test results provided in an embodiment of the present invention.

[0024] Figure 7 This is a schematic diagram of the structure of the frequency domain receiving device for multi-carrier signals provided in an embodiment of the present invention.

[0025] Figure 8 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] In the description of embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Those skilled in the art will understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0028] It should be noted that this embodiment of the invention takes the Link-11 CLEW signal as an example, and its physical layer protocol key points are as follows: CLEW signals are presented as multi-tone superposition in audio / intermediate frequency channels. They typically use 16 equally spaced single-tone subcarriers, the frequencies of which are agreed upon in the protocol, and their center frequencies and intervals are fixed.

[0029] The preamble consists of a Doppler reference tone (e.g., 605Hz) and a synchronization tone (e.g., 2915Hz). The Doppler reference tone has continuous phase throughout the preamble transmission and is used for Doppler frequency offset estimation and initial frequency correction. The synchronization tone uses BPSK (Binary Phase Shift Keying) or differential phase modulation and is used for frame synchronization and symbol boundary determination.

[0030] The data segment carries differential phase modulated service data such as π / 4-DQPSK on 16 subcarriers, and adopts a fixed symbol interval and frame structure. No dedicated pilot or cyclic prefix is ​​inserted separately in the entire frame structure.

[0031] In a real system, the receiver first detects the entire real signal and coarsely estimates the Doppler frequency offset, then moves the signal to the vicinity of the nominal center frequency; subsequently, it performs time-domain synchronization and symbol timing according to the CLEW frame structure, and uses methods such as LS (Least Squares) or FFT to extract complex symbols on each subcarrier in the frequency domain for subsequent differential phase demodulation and decoding.

[0032] In the Link-11 signal, apart from the 5 preamble frames, the rest are data frames. The data frames are 16-tone signals, and the values ​​of the 16 single tones are shown in Table 1: Table 1

[0033] In the data frame, 605Hz is still used for Doppler shift correction and does not carry information. Its phase is continuous throughout the entire data frame transmission, and its transmission power is 6dB, four times that of the other 15 tones. The remaining 15 tones each carry 2 bits of information in each frame, for a total of 30 bits.

[0034] The phase reference frame follows the preamble and consists of 16 single-tone frequencies. Each of the 15 data tones other than 605Hz provides a reference phase, which serves as the initial phase for differential phase modulation of the subsequent data frames.

[0035] The encoded data is 30 bits per group, then π / 4-DQPSK modulated to generate a modulated signal carrying tactical information. Each frame of encoded data is 30 bits, divided into 15 groups sequentially. Each group consists of 2 bits, each modulated with 15 data tones using π / 4-DQPSK. Within each frame, each data tone has a specific phase, which remains constant within the frame. At frame boundaries, the phase of each data tone is phase-shifted based on the phase of the previous frame, and the magnitude of the phase shift determines the value of the 2 bits. For example, in a typical implementation, 2 bits can represent four different states: 10, 00, 01, and 11, corresponding to phase differences of 45°, 135°, 225°, and 315° from the previous frame, respectively. Each phase difference is located in the center of a quadrant. This means that when the phase difference is less than 45°, it does not affect the decision result; when it is greater than 45°, the phase will fall into an adjacent quadrant, resulting in a bit error. In practice, the audio signal transmitted by the Link-11 system via radio is a combination waveform of 16 single-tone frequencies, including 605Hz. The 605Hz tone is a Doppler tone that is not modulated, while the 15 data tones are modulated using π / 4-DQPSK.

[0036] Figure 1 This is a flowchart illustrating the frequency domain reception method for multi-carrier signals provided in an embodiment of the present invention. (Refer to...) Figure 1 This invention provides a frequency domain reception method for multi-carrier signals, which may specifically include the following steps: Step 101: For each time-domain data frame extracted from the multi-carrier signal, windowing is applied to the time-domain data frame, and a pre-calculated and cached pseudo-inverse matrix is ​​multiplied with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame; wherein, the known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​the least squares pseudo-inverse matrix of the window weighting matrix, and the window weighting matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set.

[0037] It should be noted that the execution subject of the frequency domain reception method for multi-carrier signals provided in the embodiments of the present invention can be an electronic device, a component in the electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. For example, a mobile electronic device can be a laptop computer, an Ultra-mobile Personal Computer (UMPC), etc., and a non-mobile electronic device can be a Personal Computer (PC). The embodiments of the present invention do not specifically limit this. The following embodiments of the present invention describe the execution subject as the receiving end of the multi-carrier signal.

[0038] A multicarrier signal can refer to a composite broadband signal generated by modulating a data stream onto multiple subcarriers of different frequencies and transmitting them in parallel within the same symbol period. In this embodiment of the invention, a multicarrier signal specifically refers to a Link-11 CLEW signal. The receiving end can process the received Link-11 CLEW signal to obtain the complex amplitude estimate of each subcarrier corresponding to each time-domain data frame in the frequency domain.

[0039] Multicarrier signals can be composed of consecutive symbol periods (i.e., time-domain data frames), with multiple parallel subcarriers existing within each symbol period. The subcarrier complex amplitude estimate corresponding to the time-domain data frame (i.e., the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame) can refer to the estimate of the complex signal carried by each subcarrier (a frequency component in the frequency dimension) within each time-domain data frame (a symbol unit in the time dimension) after synchronization and time-frequency transformation at the receiving end of a multicarrier communication system.

[0040] In some embodiments, for the input current time-domain data frame, windowing processing can be applied to the current time-domain data frame to obtain a windowed current time-domain data frame. The windowing formula can be: ; in, It is a time index. , It is the frame length; It is the first The window function corresponding to the frame signal data; the window function can be Hamming window, Hanning window, etc. It is the first Frame signal data (i.e., the first frame) (one time-domain data frame); The first step after windowing Frame signal data.

[0041] In this embodiment of the invention, for each frame of input signal data (i.e., each time-domain data frame), the spatial decoupling of each frame of time-domain signal data can be performed using a pre-calculated and cached least-squares pseudo-inverse matrix, so as to simultaneously achieve spatial interference cancellation and channel equalization, thereby obtaining the optimal subcarrier complex amplitude estimate corresponding to each frame of signal data in the least-squares sense.

[0042] In some embodiments, for each frame of input signal data, the complex amplitude estimate of each subcarrier in the frequency domain corresponding to each frame can be calculated using the following formula: ; in, It is a time-domain data frame after windowing processing; It is a pre-calculated pseudo-inverse matrix of the cache; It is the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame (that is, the vector composed of the amplitudes of all subcarriers in the time-domain data frame). By performing a simple matrix multiplication operation between the windowed time-domain data frame and the pre-calculated least-squares pseudo-inverse matrix, the complex amplitude estimates of the 16 subcarriers in the frequency domain can be obtained, and the mutual interference between these subcarriers has been canceled out.

[0043] This invention achieves robust estimation of subcarrier complex amplitude by pre-compiling and caching the LS (Least Squares) pseudo-inverse matrix once during system initialization, thereby requiring only one matrix-vector multiplication operation during each frame processing.

[0044] In some embodiments, the fixed subcarrier frequency set may include the known subcarrier frequencies of the multicarrier signal, and may be adopted using... Represents the set of fixed subcarrier frequencies. Indicates the first Subcarrier frequencies, in CLEW =16, and the values ​​for each frequency point can be as shown in Table 1. A suitable sampling rate needs to be selected. and subcarrier spacing This makes the set of fixed subcarrier frequencies It can accurately include the frequency values ​​in CLEW, thereby constructing the complex exponential basis matrix corresponding to each known subcarrier frequency.

[0045] In some embodiments, the complex exponential basis matrix can be weighted using a window function to obtain a window-weighted matrix.

[0046] In some embodiments, least squares estimation of the complex amplitude vector of a single tone can be used. ( for (Complex vector space) ; The formula for calculating the least-squares pseudo-inverse of the window-weighted matrix can be as follows: ; in, For window weighting matrix, The conjugate transpose of the window weighting matrix. It is the inverse matrix of the product of the window weighting matrix and the conjugate transpose matrix. The least-squares pseudo-inverse matrix of the window weighting matrix (i.e., the pre-computed cached pseudo-inverse matrix). This embodiment of the invention utilizes the pre-computed cached least-squares pseudo-inverse matrix to perform a linear transformation on each received frame of signal data, thereby obtaining the optimal subcarrier complex amplitude estimate in the least-squares sense. This statistically approximates the original multi-carrier signal to the maximum extent possible, which is beneficial for providing the highest theoretically accurate input for subsequent symbol decision.

[0047] It should be noted that since the window weighting matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set, and the pseudo-inverse matrix is ​​the least-squares pseudo-inverse matrix of the window weighting matrix, the pre-calculated pseudo-inverse matrix itself contains a mapping from the time domain to the frequency domain. Therefore, by multiplying the pre-calculated and buffered pseudo-inverse matrix with the windowed time-domain data frame, the complex amplitude estimate of each subcarrier in the frequency domain can be directly obtained, thereby achieving frequency-domain reception. This process simultaneously completes the frequency domain transformation and least squares transformation, without needing to explicitly perform an FFT transformation to the frequency domain on the time-domain data frame.

[0048] This invention determines the window weighting matrix based on the complex exponential basis matrix corresponding to each known subcarrier frequency in a fixed subcarrier frequency set, and pre-calculates the least-squares pseudo-inverse matrix of the window weighting matrix. This achieves the pre-calculation of the window-weighted least-squares projection and the pseudo-inverse matrix, ensuring that the pre-calculated pseudo-inverse matrix itself contains a mapping from the time domain to the frequency domain. Therefore, by multiplying the pre-calculated and buffered pseudo-inverse matrix with the windowed time-domain data frame, the complex amplitude estimate of each subcarrier corresponding to the time-domain data frame in the frequency domain is obtained. This simultaneously completes the frequency domain transformation and least squares, eliminating the need for explicit FFT transformation of the time-domain data frame to the frequency domain for frequency domain reception. By using the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set as the model basis vector, and directly estimating the complex amplitude of each subcarrier in the time domain using a windowed least-squares algorithm, the complex amplitude of each subcarrier is optimized. (Note: The last sentence about Link-11 seems unrelated and likely refers to a different invention.) The known subcarrier frequencies of the CLEW signal are fixed constants (forming a fixed set of subcarrier frequencies). The pseudo-inverse matrix of least squares can be calculated and cached once during the system initialization phase. Therefore, in the subsequent demodulation process, only one matrix-vector multiplication is needed per frame to obtain the complex amplitude estimate of all subcarriers, thereby significantly reducing the real-time computation load and eliminating the phase estimation error caused by the FFT picket fence effect.

[0049] Based on any of the above embodiments, the pseudo-inverse matrix is ​​pre-calculated in the following manner: based on the preset sampling rate, subcarrier spacing and each known subcarrier frequency in the fixed subcarrier frequency set, construct the complex exponential basis matrix corresponding to each known subcarrier frequency; apply a window function to the complex exponential basis matrix to obtain a window weighted matrix; and calculate the least squares pseudo-inverse matrix of the window weighted matrix based on the window weighted matrix.

[0050] In some embodiments, the sampling rate is recorded. Frame length Time Index Fixed subcarrier frequency set In CLEW =16, and the values ​​for each frequency point are listed in Table 1. Appropriate values ​​need to be selected. and subcarrier spacing This makes the set of fixed subcarrier frequencies It can accurately contain the frequency values ​​in CLEW. The constructed complex exponential basis matrix can be as follows: ; in, It is the preset sampling rate; It is a time index. , Frame length (i.e., the length of the time domain data frame). It is the first The subcarrier frequency (i.e. the first) (single audio frequency); It is the imaginary unit; for A complex matrix space of dimension 1 It is a complex exponential basis matrix. It is the complex exponential basis matrix of the th Line number The elements of the column are the monophonic frequencies shown in Table 1.

[0051] In some embodiments, the window weighting matrix can be obtained by the following formula: ; in, It is a window function. It is the complex exponential basis matrix of the th Line number Column elements, The window-weighted matrix is ​​obtained by weighting the complex exponential basis matrices using a window function. The Middle Line number The elements of the column. The window function can be selected from Hamming window, Hanning window, etc.

[0052] In some embodiments, the formula for calculating the least-squares pseudo-inverse matrix can be as follows: ; in, For window weighting matrix, The conjugate transpose of the window weighting matrix. It is the inverse matrix of the product of the window weighting matrix and the conjugate transpose matrix. The least-squares pseudo-inverse of the window-weighted matrix. Only rely on the first Subcarrier frequency Sampling rate Frame length Window function For CLEW signals, the 16 frequency points are fixed. , and Alternatively, a fixed value can be pre-configured, resulting in a least-squares pseudo-inverse matrix. It only needs to be pre-computed once, and then cached.

[0053] This invention provides a robust estimation of the subcarrier complex amplitude by pre-compiling and caching the least-squares pseudo-inverse matrix once during system initialization, thereby requiring only one matrix-vector multiplication operation during each frame processing.

[0054] Based on any of the above embodiments, after windowing the time-domain data frame extracted from the multi-carrier signal and multiplying the pre-calculated cached pseudo-inverse matrix with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame, the method further includes: calculating the overall phase difference between frames based on the complex amplitude estimates of each subcarrier in the frequency domain corresponding to the current time-domain data frame and the complex amplitude estimates of each subcarrier in the frequency domain corresponding to the previous time-domain data frame; compensating the overall phase difference between frames based on a compensation factor to obtain the modulation phase difference between frames; the compensation factor is determined based on the length of the time-domain data frame and the known subcarrier frequency; extracting the phase parameter in the modulation phase difference between frames, and determining the corresponding modulation bit based on the association relationship between the phase parameter and a preset phase interval.

[0055] In some embodiments, the complex amplitude estimate corresponding to the reference frame (frame 0) is assumed to be , No. The complex amplitude estimate corresponding to the frame is First, we can calculate the overall phase difference between frames: ; ; in, The reference frame (frame 0) corresponds to the first... Estimation of the complex amplitude of each subcarrier in the frequency domain; For the first frame corresponding to the first frame Estimation of the complex amplitude of each subcarrier in the frequency domain; For the first The overall phase difference between the first frame and the reference frame on each subcarrier; For the first The first frame corresponding to Estimation of the complex amplitude of each subcarrier in the frequency domain; For the first The first frame corresponding to Estimation of the complex amplitude of each subcarrier in the frequency domain; For the first On the nth subcarrier, the nth Frame and the Overall phase difference between frames; This represents a point-to-point complex multiplication of each element of the vector. Embodiments of this invention can remove channel and amplitude / phase uncertainties through intra-frame cancellation.

[0056] In some embodiments, a It is the time-domain length of each frame of data, in seconds. Each subcarrier in CLEW exhibits deterministic phase accumulation between adjacent symbols. .by As a compensation factor, the DQPSK modulation phase difference is obtained using the following formula: ; in, The length of the time-domain data frame (i.e., the time-domain length of each data frame). For the first The subcarrier frequency (i.e. the first) (single audio frequency); For complex units; As a compensation factor; In order to the first The first frame is calculated during frame processing. On the subcarrier Frame and the Overall phase difference between frames; For the first On the nth subcarrier, the nth Frame and the The modulation phase difference between frames. Embodiments of the present invention, through cross-symbol phase compensation, can eliminate the systematic phase rotation caused by the carrier frequency.

[0057] To improve the stability of differential decision, this invention employs a dual-reference phase compensation mechanism, namely a dual-reference structure of "intra-frame reference + cross-symbol phase compensation". First, using the amplitude and phase of the reference frame as a benchmark, intra-frame cancellation is performed on the phases of each subcarrier in the current frame to remove channel and amplitude / phase uncertainties. Second, based on the determined relationship between subcarrier frequency and symbol duration, phase precession between consecutive symbols is compensated to eliminate systematic phase rotation caused by carrier frequency. The dual-reference structure of "intra-frame reference + cross-symbol phase compensation" effectively improves the reliability of DQPSK decision under conditions of frequency offset or clock drift. Furthermore, this invention does not rely on additional pilots, but only utilizes the inherent phase reference frame of CLEW to complete intra-frame and inter-frame compensation.

[0058] In some embodiments, it is possible to let This allows for the execution of DQPSK decisions, specifically by adjusting the phase parameters. The modulation bits corresponding to the current time-domain data frame are determined based on the comparison result with the preset phase interval.

[0059] in, It is the first On the nth subcarrier, the nth Frame and the The phase difference is modulated between frames to carry phase difference information and characterize the correlation between signals at two time points; It is a phase extraction operator used to perform the transformation from the complex domain to the angle domain, which can eliminate the influence of amplitude changes on subsequent DQPSK decisions, allowing the demodulator to focus on the phase parameters; It is the extracted phase parameter, i.e., the first... On the nth subcarrier, the nth The angular difference between a frame and the previous frame (or between the first frame and the reference frame).

[0060] To eliminate the impact of amplitude fluctuations caused by channel fading or automatic gain control on demodulation, this embodiment of the invention performs a phase extraction operation on the modulation phase difference between frames to obtain a phase parameter that only reflects the phase change between adjacent data frames (excluding the amplitude component). The phase parameter can be directly used for phase interval decision.

[0061] Based on any of the above embodiments, determining the corresponding modulation bit based on the affiliation relationship between the phase parameter and the preset phase interval includes: if the phase parameter is within the first preset phase interval, then the corresponding modulation bit is determined to be [0, 0]; if the phase parameter is within the second preset phase interval, then the corresponding modulation bit is determined to be [0, 1]; if the phase parameter is within the third preset phase interval, then the corresponding modulation bit is determined to be [1, 0]; if the phase parameter is within the fourth preset phase interval, then the corresponding modulation bit is determined to be [1, 1].

[0062] In this embodiment of the invention, DQPSK decision can be performed based on the extracted phase parameters, and the phase parameters can be compared with each preset phase interval to determine which preset phase interval the phase parameters belong to. Based on the belonging result, the corresponding data bits are generated, thereby completing the demodulation of the current time domain data frame.

[0063] In this embodiment of the invention, at the receiving end, by making a decision on the phase difference between adjacent symbols, two bits of information can be recovered each time.

[0064] In some embodiments, the first preset phase interval can be The second preset phase interval can be The third preset phase interval can be The fourth preset phase interval can be .like ,but ;like ,but ;like ,but ;like ,but .in, It refers to the first The subcarrier, the first The first group of modulation bits in frame transmission.

[0065] Based on any of the above embodiments, for each data frame extracted from the multi-carrier signal, after obtaining the complex amplitude estimate of each subcarrier corresponding to the time-domain data frame in the frequency domain, the method further includes: calculating the fitting residual based on the window weighting matrix, the complex amplitude estimate of each subcarrier corresponding to the time-domain data frame in the frequency domain, and the windowed time-domain data frame; and estimating the covariance matrix based on the noise variance and the window weighting matrix.

[0066] In some embodiments, the fitting residual can be expressed as: ; in, This is a time-domain data frame after windowing processing; This is for estimating the complex amplitude of each subcarrier corresponding to the time-domain data frame in the frequency domain; Window weighting matrix; To fit the residuals, this embodiment of the invention applies windowing processing to each time-domain data frame. First, use the pre-computed cached least-squares pseudo-inverse matrix right Perform a linear transformation to obtain the complex amplitude estimates of each subcarrier corresponding to the time-domain data frame in the frequency domain. Then based on complex amplitude estimation Reconstructing observation data Thus, the reconstructed observation data can be calculated. Compared with actual observation data The smaller the fitting error, the higher the estimation accuracy of the subcarrier complex amplitude.

[0067] In the least squares estimation process, the embodiment of the present invention calculates the fitting residual, which can be used for frame-level reception quality assessment, channel state monitoring, etc., providing additional information for receiver performance optimization and state monitoring.

[0068] In some embodiments, assuming a Gaussian white noise environment, the noise variance is... The covariance can be approximated as: ; in, For window weighting matrix, The conjugate transpose of the window weighting matrix. It is the inverse matrix of the product of the window weighting matrix and the conjugate transpose matrix; For noise variance; The covariance matrix, i.e., the complex amplitude estimate. The covariance matrix of the complex amplitude estimates of each subcarrier corresponding to the time-domain data frame in the frequency domain is used to describe the complex amplitude estimates. Statistical uncertainty. Noise variance. The larger the value, the greater the uncertainty in the estimate; The larger the eigenvalue, the smaller the uncertainty of the estimate.

[0069] In the least squares estimation process, the covariance matrix is ​​approximated and can be used for frame-level reception quality assessment, channel state monitoring, etc., providing additional information for receiver performance optimization and state monitoring.

[0070] Based on any of the above embodiments, after estimating the covariance matrix based on the noise variance and the window weighting matrix, the method further includes: using the diagonal elements of the covariance matrix as the uncertainty of the complex amplitude estimation of each subcarrier in the frequency domain; determining the soft information weight of each subcarrier based on the reciprocal of the uncertainty of the complex amplitude estimation of each subcarrier in the frequency domain; and accumulating the soft information weights of each subcarrier to obtain the confidence estimate of the time-domain data frame.

[0071] In some embodiments, the phase / amplitude uncertainty of the complex amplitude estimate for each subcarrier can be approximated by the diagonal elements of the covariance matrix, and the soft information weights can be constructed as follows: ; in, The first time domain data frame corresponding to Estimation of the complex amplitude of each subcarrier in the frequency domain; For the first Uncertainty in the estimation of complex amplitude of individual subcarriers in the frequency domain; For the first The soft information weight of the complex amplitude estimation of a subcarrier in the frequency domain is proportional to the reciprocal of the uncertainty of the subcarrier estimation.

[0072] In some embodiments, the entire data frame can be... The weights are accumulated to form a confidence estimate of the reception accuracy of the entire data frame. In subsequent processing, such as multi-frame merging, multi-station merging, and trajectory fusion at the upper layer, low-confidence frames can be downweighted or discarded. In addition, it can also serve as a reference for receiver performance evaluation: when the residual is consistently large across multiple frames, it can indicate problems such as inaccurate frequency offset estimation, deteriorated channel conditions, or front-end failures.

[0073] To enable those skilled in the art to better understand the embodiments of the present invention, the embodiments of the present invention will be described below through a specific example.

[0074] The existing method for FFT demodulating DQPSK signals is as follows: (1) Take a frame of time series ( for (3D complex vector space), window function (Or without windows): ; (2) Do Point FFT, obtain Find the nearest frequency grid for each known frequency point. ,by This serves as the estimate of the complex amplitude of the subcarrier. For the first m The complex spectrum values ​​of each frequency grid. For the first The nearest frequency raster index corresponding to each subcarrier. For the first Subcarrier frequencies, This refers to the frequency resolution.

[0075] (3) Take the complex amplitude of the frequency points corresponding to the reference frame and the data frame as the differential phase, and decide on DQPSK.

[0076] However, this method has the following problems: (1) Resolution-latency trade-off: When it is smaller, it needs to satisfy a larger one. This leads to increased latency and computing power.

[0077] (2) Spectrum leakage: when When the frequency is not an integer multiple of the FFT grid, other frequency points have a fixed deviation from this frequency point; when When the length of the data in the previous frame does not satisfy the time-domain periodicity of 16 subcarriers, the FFT calculation result still has mutual interference of 16 subcarriers. In addition, CFO (Carrier Frequency Offset) / Doppler / sampling rate mismatch will further amplify the deviation.

[0078] (3) The window function is only used to suppress partial leakage. In the end, the energy / phase of the broadband neighborhood is still represented by the single-point spectrum value, and there is mutual interference between frequency points.

[0079] This invention addresses the problems of spectral leakage, phase drift accumulation, and lack of soft-determination information in traditional FFT decimation methods during Link-11 CLEW multi-tone DQPSK demodulation by proposing the following three improvement techniques: (1) Window-weighted least squares projection and pseudo-inverse pre-computation method.

[0080] The complex exponential sequences corresponding to each known subcarrier frequency are used as model basis vectors, and the complex amplitude of each subcarrier is directly estimated in the time domain using a windowed least squares algorithm. Since the subcarrier frequency set of Link-11 CLEW is a fixed constant, the pseudo-inverse matrix of the least squares can be calculated and cached once during the system initialization phase. In the subsequent demodulation process, only one matrix-vector multiplication is required per frame to obtain the complex amplitude of all subcarriers, thereby significantly reducing the real-time computation load and eliminating the phase estimation error caused by the FFT picket fence effect.

[0081] Furthermore, this invention has a significant advantage in terms of computational complexity. With a frequency of 12kHz, a frame length of 13.33ms, and a sample count of 160, traditional FFT requires a 2400-point complex FFT to meet the 5Hz frequency resolution. This necessitates approximately 1.35 × 10^4 complex multiplications and 2.7 × 10^4 complex additions per frame, and introduces a 2400-point processing buffer and corresponding latency. In contrast, this embodiment of the invention uses window-weighted least squares... × Matrix-vector multiplication requires only approximately 2.56 × 10^3 complex multiplications and 2.54 × 10^3 complex additions per frame; the overhead of complex multiplication is about 1 / 5.3 of that of FFT, and the overhead of complex addition is about 1 / 10.6, with latency and memory usage of only 160 points. This significantly reduces real-time computation and processing latency while maintaining or even improving demodulation accuracy. The above parameter values ​​can be used in practical applications.

[0082] (2) Dual reference phase compensation mechanism.

[0083] To improve the stability of differential decision, a two-layer structure of "intra-frame reference + cross-symbol phase compensation" is adopted. First, using the amplitude and phase of the reference frame as a benchmark, intra-frame cancellation is performed on the phases of each subcarrier in the current frame to remove channel and amplitude / phase uncertainties. Second, based on the determined relationship between subcarrier frequency and symbol duration, phase precession between consecutive symbols is compensated to eliminate systematic phase rotation caused by carrier frequency. This dual-reference structure effectively improves the reliability of DQPSK decision under conditions of frequency offset or clock drift. This invention does not rely on additional pilots; it only utilizes the inherent phase reference frame of CLEW to complete intra-frame and inter-frame compensation.

[0084] (3) Construction of soft decision information based on residual driving.

[0085] In the least squares estimation process, the confidence information of the amplitude estimation of each subcarrier is obtained by calculating the fitting residual and estimating the covariance approximation. This information is used for frame-level reception quality assessment, channel state monitoring, etc., and provides additional information for receiver performance optimization and state monitoring.

[0086] Figure 2This is a schematic diagram of the receiving and processing flow for multi-carrier CLEW signals provided in an embodiment of the present invention. (Refer to...) Figure 2 In one specific embodiment, the receiving and processing procedure for multi-carrier CLEW signals may include the following steps: Step 1: Construct the complex exponential basis matrix.

[0087] Record the sampling rate Frame length Time Index Given the set of subcarrier frequencies In CLEW =16, and the values ​​for each frequency point are listed in Table 1. Appropriate values ​​need to be selected. and subcarrier spacing This makes the set of subcarrier frequencies It can accurately include the frequency values ​​in CLEW.

[0088] Construct a complex exponential basis matrix (columns represent individual tones): ; Step 2, Window weighting.

[0089] Window function is Multiply the window by each row to get: ; Window functions can include Hamming window, Hanning window, etc.

[0090] Step 3: LS projection and pseudo-inverse matrix pre-calculation.

[0091] Least squares estimation of complex amplitude vector of a single tone : ; The formula for calculating the pseudo-inverse matrix is ​​as follows: ; Only depend For CLEW, the 16 frequency points are fixed. and , Alternatively, a fixed value can be pre-configured, in which case H only needs to be pre-calculated once and then cached.

[0092] Step 4: Calculate the complex phase of the subcarriers for each frame.

[0093] When inputting data for each subsequent frame, use the following formula: ; A simple matrix multiplication operation can yield the complex amplitude estimate of the 16 subcarriers, and the mutual interference between these subcarriers has been canceled out.

[0094] Step 5: Calculate the overall phase difference between frames.

[0095] Suppose the complex amplitude estimate of the reference frame (frame 0) is The complex amplitude estimate of the nth frame is First, calculate the overall phase difference between frames: ; ; In the above formula, ∙ represents a point-to-point complex multiplication of each element of the vector.

[0096] Step 6: Calculate the modulation phase difference between frames.

[0097] set up This is the time-domain length of each frame of data, measured in seconds. Each subcarrier in CLEW exhibits deterministic phase accumulation between adjacent symbols: and will As a compensation factor, the DQPSK modulation phase difference is obtained by the following formula: ; Step 7, DQPSK decision.

[0098] make The DQPSK decision is as follows: like ,but ;like ,but ;like ,but ;like ,but .

[0099] Step 8: Calculate the LS residual.

[0100] The residual of the pre-calculated LS pseudo-inverse matrix can be expressed as: ; Assuming a Gaussian white noise environment, the noise variance is... The covariance is approximately: ; Step 9: Calculate the subcarrier confidence.

[0101] The phase / amplitude uncertainty of the complex amplitude estimate for each subcarrier can be approximated by the diagonal elements of the covariance matrix, and the soft information weights can be constructed as follows: ; in Indicates the first The soft information weight (i.e., soft reliability weight) of the complex amplitude estimation of a subcarrier in the frequency domain is proportional to the reciprocal of the uncertainty of the subcarrier estimation.

[0102] Take the entire frame The weights are accumulated to form a confidence estimate of the reception accuracy of the entire frame. In subsequent processing, such as multi-frame merging, multi-site merging, and trajectory fusion at the upper layer, low-confidence frames can be downweighted or discarded. In addition, it can also serve as a reference for receiver performance evaluation: when the residual is consistently large across multiple frames, it can indicate problems such as inaccurate frequency offset estimation, deteriorated channel conditions, or front-end failures.

[0103] This invention proposes a frequency domain reception method for multi-carrier signals using a window-weighted least squares (LS) projection matrix instead of an open-ended free-float (FFT) method. By pre-compiling and buffering the LS pseudo-inverse matrix during system initialization, only one matrix-vector multiplication operation is required during each frame processing, achieving robust estimation of subcarrier complex amplitudes. Compared to traditional FFT decimation methods, this invention reduces computational complexity to approximately 20% and memory and processing latency by about an order of magnitude. Furthermore, through a dual-reference mechanism of "intra-frame reference cancellation + deterministic phase accumulation compensation," amplitude and phase uncertainties and inter-symbol phase accumulation drift are effectively eliminated, significantly improving the stability of DQPSK decisions. Simultaneously with least squares estimation, confidence indices can be constructed based on residuals and covariance for frame-level reception quality assessment and receiver status monitoring.

[0104] In one specific embodiment, the simulation conditions and simulation results may be as follows.

[0105] The waveform is a 16-tone CLEW. Sampling rate. =12k. Frame length 13.33ms. Gaussian white noise environment, no frequency offset. For different temporal SNRs (Signal-to-Noise Ratios), the bit error rate performance was compared using the traditional NFFT method and the window-weighted LS method proposed in this paper. 10,000 Monte Carlo simulations of 100 frames each were performed at each SNR, and the overall bit error rate was finally calculated.

[0106] Figure 3 This is a time-domain waveform diagram of the generated preamble real signal provided in an embodiment of the present invention.

[0107] Figure 4 This is a time-domain waveform diagram (time-domain magnitude and frequency-domain magnitude) of the generated phase reference frame provided in an embodiment of the present invention. (Refer to...) Figure 4The time-domain magnitude is the amplitude envelope of the time-domain data, and the frequency-domain magnitude is the amplitude of the frequency-domain data. An accurate time-domain phase reference can be obtained based on the time-domain data and frequency-domain data of the phase reference frame for coherent demodulation.

[0108] Figure 5 This is a comparison chart of bit error rate test results provided in an embodiment of the present invention. (Refer to...) Figure 5 Under the same bit error rate, the window-weighted LS matrix method of this invention achieves an SNR gain of approximately 2.5 to 3.5 dB compared to the traditional NFFT method.

[0109] Under the above conditions, the confidence level of each frame is statistically analyzed, and the test result is defined as the average confidence level of all subcarriers of all frame data under each SNR.

[0110] Figure 6 This is a schematic diagram of the confidence test results provided in an embodiment of the present invention. (Refer to...) Figure 6 As SNR increases, the confidence index constructed based on window-weighted LS residuals and covariance gradually increases, reflecting the improved reliability of subcarrier complex amplitude estimation. This confidence index can be used for frame quality assessment and receiver status monitoring.

[0111] The frequency domain receiving apparatus for multi-carrier signals provided by the present invention will be described below. The frequency domain receiving apparatus for multi-carrier signals described below can be referred to in correspondence with the frequency domain receiving method for multi-carrier signals described above.

[0112] Figure 7 This is a schematic diagram of the structure of a frequency domain receiving device for multi-carrier signals provided in an embodiment of the present invention. (Refer to...) Figure 7 This invention provides a frequency domain receiving device for multi-carrier signals, which may specifically include the following modules: The complex amplitude estimation module 710 is used to perform windowing processing on each time-domain data frame extracted from the multi-carrier signal, and to perform multiplication operation between the pre-calculated and cached pseudo-inverse matrix and the windowed time-domain data frame to obtain the complex amplitude estimation of each subcarrier corresponding to the time-domain data frame in the frequency domain. The known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​the least-squares pseudo-inverse matrix of the window-weighted matrix, and the window-weighted matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set.

[0113] Based on any of the above embodiments, the pseudo-inverse matrix is ​​pre-calculated using the following modules: The complex exponential basis matrix construction module is used to construct the complex exponential basis matrix corresponding to each known subcarrier frequency based on the preset sampling rate, subcarrier spacing and each known subcarrier frequency in the fixed subcarrier frequency set; The window weighting module is used to weight the complex exponential basis matrix using a window function to obtain a window weighted matrix. The pseudo-inverse matrix calculation module is used to calculate the least-squares pseudo-inverse matrix of the window-weighted matrix based on the window-weighted matrix.

[0114] Based on any of the above embodiments, after performing windowing processing on each time-domain data frame extracted from the multi-carrier signal, and multiplying the pre-calculated and cached pseudo-inverse matrix with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier corresponding to the time-domain data frame in the frequency domain, the method further includes: The overall phase difference calculation module is used to calculate the overall phase difference between frames based on the complex amplitude estimation of each subcarrier in the frequency domain corresponding to the current time domain data frame and the complex amplitude estimation of each subcarrier in the frequency domain corresponding to the previous time domain data frame. The modulation phase difference calculation module is used to compensate the overall phase difference between the frames based on a compensation factor to obtain the modulation phase difference between the frames; the compensation factor is determined based on the length of the time-domain data frame and the known subcarrier frequency. The modulation bit determination module is used to extract the phase parameter from the modulation phase difference between the frames and determine the corresponding modulation bit based on the association relationship between the phase parameter and the preset phase interval.

[0115] Based on any of the above embodiments, the modulation bit determination module includes: The first modulation bit determination unit is used to determine the corresponding modulation bit as [0, 0] if the phase parameter is within a first preset phase interval; The second modulation bit determination unit is used to determine the corresponding modulation bit as [0, 1] if the phase parameter is within the second preset phase interval; The third modulation bit determination unit is used to determine the corresponding modulation bit as [1, 0] if the phase parameter is within the third preset phase interval; The fourth modulation bit determination unit is used to determine the corresponding modulation bit as [1, 1] if the phase parameter is within the fourth preset phase interval.

[0116] Based on any of the above embodiments, after obtaining the complex amplitude estimate of each subcarrier corresponding to the time-domain data frame in the frequency domain, the method further includes: The fitting residual calculation module is used to calculate the fitting residual based on the window weighting matrix, the complex amplitude estimation of each subcarrier corresponding to the time-domain data frame in the frequency domain, and the windowed time-domain data frame. The covariance matrix estimation module is used to estimate the covariance matrix based on the noise variance and the window weighting matrix.

[0117] Based on any of the above embodiments, after estimating the covariance matrix based on the noise variance and the window weighting matrix, the method further includes: An uncertainty determination module is used to use the diagonal elements of the covariance matrix as the uncertainty of the complex amplitude estimation of each subcarrier in the frequency domain. The soft information weight determination module is used to determine the soft information weight of each subcarrier based on the reciprocal of the uncertainty of the complex amplitude estimation of each subcarrier in the frequency domain. The confidence estimation module is used to accumulate the soft information weights of each subcarrier to obtain the confidence estimate of the time-domain data frame.

[0118] This invention determines the window weighting matrix based on the complex exponential basis matrix corresponding to each known subcarrier frequency in a fixed subcarrier frequency set, and pre-calculates the least-squares pseudo-inverse matrix of the window weighting matrix. This achieves the pre-calculation of the window-weighted least-squares projection and the pseudo-inverse matrix, ensuring that the pre-calculated pseudo-inverse matrix itself contains a mapping from the time domain to the frequency domain. Therefore, by multiplying the pre-calculated and buffered pseudo-inverse matrix with the windowed time-domain data frame, the complex amplitude estimate of each subcarrier corresponding to the time-domain data frame in the frequency domain is obtained. This simultaneously completes the frequency domain transformation and least squares, eliminating the need for explicit FFT transformation of the time-domain data frame to the frequency domain for frequency domain reception. By using the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set as the model basis vector, and directly estimating the complex amplitude of each subcarrier in the time domain using a windowed least-squares algorithm, the complex amplitude of each subcarrier is optimized. (Note: The last sentence about Link-11 seems unrelated and likely refers to a different invention.) The known subcarrier frequencies of the CLEW signal are fixed constants (forming a fixed set of subcarrier frequencies). The pseudo-inverse matrix of least squares can be calculated and cached once during the system initialization phase. Therefore, in the subsequent demodulation process, only one matrix-vector multiplication is needed per frame to obtain the complex amplitude estimate of all subcarriers, thereby significantly reducing the real-time computation load and eliminating the phase estimation error caused by the FFT picket fence effect.

[0119] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logic instructions in the memory 830 to execute a frequency domain reception method for multi-carrier signals. This method includes: for each time-domain data frame extracted from the multi-carrier signal, performing windowing processing on the time-domain data frame, and multiplying a pre-calculated and cached pseudo-inverse matrix with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame; wherein the known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​the least-squares pseudo-inverse matrix of the window-weighted matrix, and the window-weighted matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set.

[0120] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0121] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the frequency domain reception method for multi-carrier signals provided by the above methods. The method includes: for each time-domain data frame extracted from the multi-carrier signal, performing windowing processing on the time-domain data frame, and multiplying a pre-calculated and cached pseudo-inverse matrix with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier corresponding to the time-domain data frame in the frequency domain; wherein, the known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​the least squares pseudo-inverse matrix of the window weighting matrix, and the window weighting matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set.

[0122] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a frequency domain reception method for multi-carrier signals provided by the methods described above. This method includes: for each time-domain data frame extracted from the multi-carrier signal, performing windowing processing on the time-domain data frame, and multiplying a pre-calculated cached pseudo-inverse matrix with the windowed time-domain data frame to obtain a complex amplitude estimate of each subcarrier corresponding to the time-domain data frame in the frequency domain; wherein, the known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​a least-squares pseudo-inverse matrix of a window-weighted matrix, and the window-weighted matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A frequency domain reception method for multi-carrier signals, characterized in that, include: For each time-domain data frame extracted from the multi-carrier signal, the time-domain data frame is windowed, and the pre-calculated and cached pseudo-inverse matrix is ​​multiplied with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame. Wherein, the known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​the least squares pseudo-inverse matrix of the window weighting matrix, and the window weighting matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set; The process includes, for each time-domain data frame extracted from the multi-carrier signal, windowing the time-domain data frame, and multiplying the pre-calculated and cached pseudo-inverse matrix with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame. The overall phase difference between frames is calculated based on the complex amplitude estimates of each subcarrier in the frequency domain corresponding to the current time-domain data frame and the complex amplitude estimates of each subcarrier in the frequency domain corresponding to the previous time-domain data frame. Based on the compensation factor, the overall phase difference between the frames is compensated to obtain the modulation phase difference between the frames; the compensation factor is determined based on the length of the time-domain data frame and the known subcarrier frequency. The phase parameters in the modulation phase difference between the frames are extracted, and the corresponding modulation bits are determined based on the relationship between the phase parameters and the preset phase interval.

2. The frequency domain reception method for multi-carrier signals according to claim 1, characterized in that, The pseudo-inverse matrix is ​​pre-calculated in the following way: Based on the preset sampling rate, subcarrier spacing, and each known subcarrier frequency in the set of fixed subcarrier frequencies, construct the complex exponential basis matrix corresponding to each known subcarrier frequency; The complex exponential basis matrix is ​​weighted using a window function to obtain a window-weighted matrix; Based on the window weighting matrix, calculate the least-squares pseudo-inverse matrix of the window weighting matrix.

3. The frequency domain reception method for multi-carrier signals according to claim 1, characterized in that, The step of determining the corresponding modulation bit based on the affiliation relationship between the phase parameter and the preset phase interval includes: If the phase parameter is within the first preset phase interval, then the corresponding modulation bit is determined to be [0, 0]. If the phase parameter is within the second preset phase interval, then the corresponding modulation bit is determined to be [0, 1]. If the phase parameter is within the third preset phase interval, then the corresponding modulation bit is determined to be [1, 0]; If the phase parameter is within the fourth preset phase interval, then the corresponding modulation bit is determined to be [1, 1].

4. The frequency domain reception method for multi-carrier signals according to claim 1, characterized in that, After obtaining the complex amplitude estimates of each subcarrier corresponding to the time-domain data frame in the frequency domain, the method further includes: Based on the window weighting matrix, the complex amplitude estimation of each subcarrier corresponding to the time-domain data frame in the frequency domain, and the windowed time-domain data frame, the fitting residual is calculated. The covariance matrix is ​​estimated based on the noise variance and the window weighting matrix.

5. The frequency domain reception method for multi-carrier signals according to claim 4, characterized in that, After estimating the covariance matrix based on the noise variance and the window weighting matrix, the method further includes: The diagonal elements of the covariance matrix are used as the uncertainty of the complex amplitude estimation of each subcarrier in the frequency domain; The soft information weight of each subcarrier is determined based on the reciprocal of the uncertainty of the complex amplitude estimation of each subcarrier in the frequency domain. The soft information weights of each subcarrier are accumulated to obtain the confidence estimate of the time-domain data frame.

6. A frequency domain receiving device for multi-carrier signals, characterized in that, include: The complex amplitude estimation module is used to perform windowing processing on each time-domain data frame extracted from the multi-carrier signal, and to multiply the pre-calculated and cached pseudo-inverse matrix with the windowed time-domain data frame to obtain the complex amplitude estimation of each subcarrier in the frequency domain corresponding to the time-domain data frame. Wherein, the known subcarrier frequencies of the multi-carrier signal constitute a fixed subcarrier frequency set; the pseudo-inverse matrix is ​​the least squares pseudo-inverse matrix of the window weighting matrix, and the window weighting matrix is ​​determined based on the complex exponential basis matrix corresponding to each known subcarrier frequency in the fixed subcarrier frequency set; The process includes, for each time-domain data frame extracted from the multi-carrier signal, windowing the time-domain data frame, and multiplying the pre-calculated and cached pseudo-inverse matrix with the windowed time-domain data frame to obtain the complex amplitude estimate of each subcarrier in the frequency domain corresponding to the time-domain data frame. The overall phase difference calculation module is used to calculate the overall phase difference between frames based on the complex amplitude estimation of each subcarrier in the frequency domain corresponding to the current time domain data frame and the complex amplitude estimation of each subcarrier in the frequency domain corresponding to the previous time domain data frame. The modulation phase difference calculation module is used to compensate the overall phase difference between the frames based on a compensation factor to obtain the modulation phase difference between the frames; the compensation factor is determined based on the length of the time-domain data frame and the known subcarrier frequency. The modulation bit determination module is used to extract the phase parameter from the modulation phase difference between the frames and determine the corresponding modulation bit based on the association relationship between the phase parameter and the preset phase interval.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the frequency domain reception method for multi-carrier signals as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the frequency domain reception method for multi-carrier signals as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the frequency domain reception method for multi-carrier signals as described in any one of claims 1 to 5.

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