Super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC (Forward Error Correction)

Abstract

The invention relates to the technical field of wireless communication, in particular to a super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC (Forward Error Correction), which comprises the following steps of: acquiring bit streams, grouping symbols according to odd-even positions and encoding, and respectively generating and superposing odd-even sub-signals to generate FTN (Feeder Transfer Network) signals; performing matched filtering and sequence detection, and extracting soft information of odd and even positions; performing soft decoding on the soft information at the odd positions to generate Nyquist odd group signals; subtracting the odd-numbered group signals from the total signal of the receiving end to obtain even-numbered group feedback signals; performing matched filtering and soft decoding to obtain an even number position judgment bit, and regenerating an even number signal; and extracting an odd feedback signal by subtracting the total signal of the receiving end from the even regeneration signal, generating an odd decision based on Nyquist matched filtering and soft decoding, and performing loop iteration until ISI is completely eliminated. According to the invention, efficient and robust suppression of ISI in the FTN system can be realized, and the method can adapt to the dynamic change of an optical fiber channel.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC. Background Art

[0002] With the exponential growth of global data traffic, communication systems are facing unprecedented pressure on capacity and speed. In the field of optical communications, achieving higher spectral efficiency within limited bandwidth resources has become a core technical challenge in communication system design to meet the extreme demands for high throughput in applications such as data center interconnection and high-speed backbone networks. The traditional Nyquist transmission system, due to its characteristic of orthogonal transmission pulses, limits its ability to compress available bandwidth. To overcome this limitation, researchers have proposed beyond-Nyquist (FTN) transmission technology. This technology shortens the transmission time between adjacent symbols, thereby compressing the time-domain pulse interval. This increases the transmission rate while maintaining the same bandwidth, breaking the theoretical limit of the traditional Nyquist rate. The development of FTN technology has become a key direction for building optical communication systems with speeds of 400G / 800G and above, and has also laid the theoretical foundation for the construction of next-generation high-spectral-efficiency communication networks.

[0003] In FTN transmission systems, the temporal overlap between symbols naturally introduces inter-symbol interference (ISI), fundamentally different from the design philosophy of traditional Nyquist systems using orthogonal pulses. To address this non-orthogonality, academia and industry have developed various interference suppression techniques to improve system performance. Currently, two mainstream approaches include Successive Interference Cancellation (SIC) based on Minimum Mean Square Error (MMSE) equalization and soft information feedback based on Turbo equalization. In MMSE equalization, the traditional matched filter is replaced with a minimum mean square error (MMSE) filter to minimize residual ISI power. At the receiver, MMSE equalization is performed on the downsampled signal. A decision-feedback structure is then used to eliminate ISI symbol by symbol. The feedback path is constructed using the decided symbols, allowing for multiple iterations to achieve fine-grained residual interference suppression. In Turbo equalization, the FTN signal is matched filtered and downsampled at the receiver. The soft decision information is then fed into a forward error correction decoder. The soft information output by the decoder is cross-fed with the previous decision results, iteratively updating the soft decision probability of the received signal to further approximate the true symbol. Furthermore, in recent years, attempts have been made to apply deep learning-based sequence detection algorithms to FTN signal detection. By training on large amounts of data under channel conditions, the nonlinear relationship between symbol mapping and interference characteristics is learned, achieving adaptive interference suppression and improving detection performance.

[0004] While existing technologies have made some progress in interference cancellation, they still face numerous limitations, severely impacting the practical deployment and system stability of FTN technology. Specifically, in MMSE equalization-based SIC schemes, the algorithm's strong reliance on channel state information, coupled with the fact that the actual optical communication channel environment, influenced by factors such as temperature and mechanical perturbations, often exhibits rapidly time-varying characteristics, leads to a high risk of algorithm failure in dynamic scenarios. Furthermore, this type of approach still suffers from significant bit error rate degradation under QPSK modulation, particularly when the roll-off factor and acceleration factor are large, where the performance loss is more pronounced. On the other hand, while turbo equalization improves interference cancellation, its commonly used convolutional coding (FEC) scheme has a low bit rate and high redundancy, which can offset the inherent advantages of FTN systems in terms of spectral efficiency and even lead to a decrease in overall system spectral efficiency in certain configurations. Furthermore, deep learning-based sequence detection schemes struggle to operate reliably in rapidly changing communication environments due to their large training data requirements and weak model generalization capabilities. Therefore, achieving efficient and robust suppression of ISI in FTN systems while ensuring low redundancy and low computational complexity and adapting to the dynamic changes of fiber channels remains a key technical issue that needs to be solved urgently. Summary of the Invention

[0005] This application provides a super-Nyquist signal iterative interference cancellation method based on symbol grouping and FEC, which can achieve efficient and robust suppression of ISI in FTN systems and adapt to the dynamic changes of fiber channels. This application provides the following technical solutions:

[0006] In a first aspect, the present application provides a super-Nyquist signal iterative interference cancellation method based on symbol grouping and FEC, the method comprising:

[0007] Obtain the upper layer original bit stream, group the symbols by odd and even positions and perform FEC coding independently, generate and superimpose the odd and even sub-signals based on RRC pulse shaping to generate the FTN signal;

[0008] Acquire the FTN signal and perform matched filtering and BCJR sequence detection to extract the LLR soft information of the odd and even positions;

[0009] Perform LDPC soft decoding on the LLR soft information at odd positions and generate Nyquist odd-number array signals based on bit decisions;

[0010] The even array feedback signal is obtained by subtracting the total signal at the receiving end from the generated regenerated Nyquist odd array signal;

[0011] Perform matched filtering and LDPC soft decoding on the even array feedback signal to obtain even position decision bits and regenerate the even signal part;

[0012] The odd feedback signal is extracted by subtracting the even regenerated signal from the total signal at the receiving end. The odd decision is generated based on Nyquist matched filtering and LDPC soft decoding, and the process is iterated until the ISI is completely eliminated.

[0013] In a specific embodiment, grouping symbols according to odd and even positions includes:

[0014] Mapping the original bit stream into a symbol sequence {a n}, based on the position attribute of the symbols in the time series, the symbols are grouped into data groups with odd and even positions, and the symbols are divided into two categories:

[0015]

[0016] In a specific implementation scheme, generating and superimposing the odd and even sub-signals based on RRC pulse shaping to generate the FTN signal includes:

[0017] Each coded symbol is pulse-shaped based on the raised cosine pulse c(t), and the symbol interval is compressed to τT using the acceleration factor τ. The overall transmitted waveform s(t) can be expressed as:

[0018]

[0019] Among them, a n Indicates the symbol value of the nth position, τ represents the acceleration factor, E s is the average power of the transmitted signal, and T represents the traditional Nyquist symbol period. Combined with the symbol position offset after parity grouping, the above formula can be expanded to the sum of two sub-signals with different time domain offsets as follows:

[0020]

[0021] Among them, s(t) odd and s(t) even They represent the sub-signals generated by the odd and even arrays respectively. The two sub-signals are superimposed and output as the final super-Nyquist signal s(t) to the receiving end.

[0022] In a specific implementation scheme, acquiring the FTN signal and performing matched filtering and BCJR sequence detection to extract LLR soft information at odd and even positions includes:

[0023] The receiver receives the generated super-Nyquist signal s(t), which is then narrow-band filtered by the front end and then enters the coherent detection module, converting the optical carrier into an RF baseband signal. The baseband signal is then filtered by the analog-to-digital converter to generate a discrete time sequence. The filtered signal is then input into the sequence receiving module based on the BCJR algorithm, which performs maximum a posteriori probability estimation on the symbols at odd and even positions, obtaining the LLR soft information for each symbol at the corresponding position.

[0024] Under ideal linear additive conditions, the total signal at the receiving end can be expressed as follows:

[0025]

[0026] Where g(t) is the impulse response of the matched filter, and w(t) represents Gaussian additive white noise. The above equation can be expanded into the sum of two sub-signals with different time domain offsets as follows:

[0027]

[0028] Among them, r(t) odd and r(t) even Represent the parts of the received signal generated by odd-indexed symbols and even-indexed symbols, respectively.

[0029] In a specific implementation scheme, performing LDPC soft decoding on the LLR soft information at odd positions and generating Nyquist odd-number array signals based on bit decisions includes:

[0030] All LLR values at odd positions are input into the LDPC soft decoder, and the decoder outputs the decision value sequence of the odd position bits, which is recorded as:

[0031] {a 2k-1 '|k=1,2,…}

[0032] Among them, a 2k-1 ' is the hard decision bit of the 2k-1th symbol. After soft decoding, the same raised cosine impulse response function as the matched filter at the receiving end is used to perform pulse shaping on each decision bit. The regenerated signal is recorded as:

[0033]

[0034] In a specific possible implementation scheme, the subtraction of the total signal at the receiving end from the generated regenerated Nyquist odd-number array signal to obtain the even-number array feedback signal includes:

[0035] The original signal r(t) is combined with the generated Nyquist array to generate the regenerated signal r(t)' oddPerform differential operation to remove the interference component of the odd array on the received signal and extract the feedback signal containing the even array information The calculation formula is as follows:

[0036]

[0037] In a specific implementation scheme, extracting an odd feedback signal by subtracting an even regenerated signal from a total signal at the receiving end, generating an odd decision based on Nyquist matched filtering and LDPC soft decoding, and iterating until ISI is completely eliminated includes:

[0038] Perform a difference operation on the generated Nyquist even array regeneration signal and the original aliased signal r(t) at the receiving end to extract the odd array feedback signal as follows:

[0039]

[0040] Nyquist matched filtering is applied to the extracted odd-numbered feedback signal. The filtered output signal is deinterleaved and then sent to the LDPC decoder corresponding to the odd-numbered group for soft decoding, outputting a new odd-numbered position bit decision sequence.

[0041] This decision sequence is used for bit mapping and pulse shaping of the next round of regenerated odd-number signals. By repeating the closed-loop iterative process, inter-symbol interference is gradually eliminated in each round of iteration.

[0042] As the iteration proceeds, when the decoding decisions of the odd and even groups gradually approach the true symbols, the feedback signal of the even group The ISI term disappears and the output signal completely approaches the ideal Nyquist form:

[0043]

[0044] Similarly, the odd-numbered feedback signals can gradually eliminate interference and approach the ideal Nyquist signal.

[0045] In a second aspect, the present application provides a super-Nyquist signal iterative interference cancellation system based on symbol grouping and FEC, which adopts the following technical solutions:

[0046] A super-Nyquist signal iterative interference cancellation system based on symbol grouping and FEC, comprising:

[0047] The symbol grouping module is used to obtain the upper layer original bit stream, group the symbols according to the odd and even positions and independently perform FEC coding. Based on RRC pulse shaping, the parity and even sub-signals are generated and superimposed to generate the FTN signal.

[0048] Soft information extraction module, used to obtain FTN signals and perform matched filtering and BCJR sequence detection to extract LLR soft information at odd and even positions;

[0049] An odd signal regeneration module is used to perform LDPC soft decoding on the LLR soft information at odd positions and generate Nyquist odd-number array signals based on bit decisions;

[0050] an even feedback signal generating module, configured to obtain an even feedback signal by subtracting the generated regenerated Nyquist odd-number array signal from the total signal at the receiving end;

[0051] The even signal processing module is used to perform matched filtering and LDPC soft decoding on the even array feedback signal, obtain the even position decision bits, and regenerate the even signal part;

[0052] The loop iteration module is used to extract the odd feedback signal by subtracting the even regenerated signal from the total signal at the receiving end, generate the odd decision based on Nyquist matched filtering and LDPC soft decoding, and loop iterate until the ISI is completely eliminated.

[0053] In a third aspect, the present application provides an electronic device comprising a processor and a memory; the memory stores a program, which is loaded and executed by the processor to implement a super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC as described in the first aspect.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium storing a program, which, when executed by a processor, is used to implement a super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC as described in the first aspect.

[0055] In summary, the beneficial effects of this application include at least:

[0056] (1) In order to completely eliminate ISI and restore the signal to a Nyquist signal to resist the performance loss caused by the FTN system, a symbol group separation method is proposed. Combined with forward error correction and Nyquist digital signal processing, a new iterative interference cancellation mechanism is formed, which significantly enhances the tolerance of FTN signals to inter-symbol interference (ISI). By pre-grouping the data according to the odd and even positions during the transmission process and performing independent forward error correction (FEC) on each group, the forward error correction decoding of a single group of data can be performed independently at the receiving end. The odd group data after error correction decoding is regenerated, and the regenerated signal of the odd group is subtracted from the receiving end signal to separate the even group feedback signal. At this time, the inter-symbol interference (ISI) in the even group feedback signal is eliminated. The even group feedback signal is purified to a Nyquist signal, and Nyquist signal processing can be applied to it to approach the optimal performance. Similarly, the odd group can also achieve performance optimization through the above steps, realizing a positive feedback closed loop. This cycle is repeated to achieve continuous interference cancellation (SIC).

[0057] (2) A method based on symbol position group separation is proposed, and combined with low-density parity check code (LDPC) forward error correction and Nyquist digital signal processing, a new iterative continuous interference cancellation (SIC) mechanism is formed. Unlike the existing methods, it is noted that the performance of matched filtering of signals with ISI is not as good as that of matched filtering of orthogonal Nyquist signals. The super-Nyquist signals are innovatively grouped according to position, and after eliminating ISI at the receiving end, the feedback signal is used for matched filtering. The tolerance of FTN signals to inter-symbol interference (ISI) is significantly enhanced, and the bit error rate of the FTN system can be reduced to the theoretical bit error rate after Nyquist forward error correction when EbN0 is high, providing a reliable technical solution for practical engineering applications.

[0058] The transmitter's symbol sequence is divided into two sub-streams based on their timing positions, each of which is then coded and interleaved with higher-rate LDPC codes to maximize the error correction gain within each group. At the receiver, the two intertwined FTN signals are first subjected to matched filtering and sequence detection to extract the log-likelihood ratio soft information for the odd and even positions. LDPC soft decoding and decision signal regeneration are then performed alternately on the soft information of the odd and even groups. The regenerated Nyquist sub-signals are then subtracted from the original received signal to remove interference from the corresponding group, forming a feedback signal. The feedback signal is then matched filtered and decoded, and this cycle repeats, achieving continuous iterative interference cancellation until convergence. This closed-loop iterative structure, based on FEC-regeneration-feedback-decoding, offers strong channel robustness.

[0059] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application and to implement it in accordance with the contents of the specification, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of the super-Nyquist signal iterative interference cancellation method based on symbol grouping and FEC in an embodiment of the present application. Figure 1 .

[0061] Figure 2 It is a flowchart of step S101 in the embodiment of the present application.

[0062] Figure 3 This is a flow chart of the super-Nyquist signal iterative interference cancellation method based on symbol grouping and FEC in an embodiment of the present application. Figure 2 .

[0063] Figure 4 This is a schematic diagram of the effect of verifying the technical effect in the embodiment of this application Figure 1 .

[0064] Figure 5 This is a schematic diagram of the effect of verifying the technical effect in the embodiment of this application Figure 2 .

[0065] Figure 6 It is a structural block diagram of a super-Nyquist signal iterative interference cancellation system based on symbol grouping and FEC in an embodiment of the present application.

[0066] Figure 7 It is a block diagram of an electronic device for super-Nyquist signal iterative interference cancellation based on symbol grouping and FEC in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0068] Optionally, the present application uses the super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC provided in various embodiments as an example for explanation in an electronic device, where the electronic device is a terminal or a server. The terminal can be a computer, a tablet computer, etc. This embodiment does not limit the type of electronic device.

[0069] Reference Figure 1 , is a flow chart of a method for iterative interference cancellation of super-Nyquist signals based on symbol grouping and FEC provided by an embodiment of the present application. The method includes at least the following steps:

[0070] Step S101: Obtain the upper layer original bit stream, group the symbols according to the odd and even positions and independently perform FEC coding, generate and superimpose the odd and even sub-signals based on RRC pulse shaping, and generate an FTN signal.

[0071] In step S101, refer to Figure 2 , is a flow chart of step S101, firstly, a continuous original bit stream is obtained from the upper protocol stack or application layer, and mapped into a symbol sequence {a n}, based on the position attribute of the symbols in the time series, the symbols are grouped into data groups with odd and even positions, and the symbols are divided into two categories:

[0072]

[0073] Subsequently, the odd and even groups of symbols are independently sent to their respective forward error correction (FEC) subsystems. For example, an LDPC-based encoder and a corresponding interleaver. The interleaver is used to randomize the order of the error correction bits to improve the error correction performance during subsequent channel decoding; the encoder introduces redundant bits within each group. In this embodiment, an LDPC code with a code rate of 3 / 4 is used to generate two error correction bit streams. After completing the group-independent FEC encoding, the FTN waveform generation stage is entered. Each encoded symbol is pulse-shaped based on the raised cosine pulse c(t), and the symbol interval is compressed to τT using the acceleration factor τ, thereby actively introducing controllable inter-symbol interference (ISI). The overall transmitted waveform s(t) can be expressed as:

[0074]

[0075] Among them, a n Indicates the symbol value of the nth position, τ represents the acceleration factor, which is actually used to be 0.5, E s is the average power of the transmitted signal, and T represents the traditional Nyquist symbol period. Combined with the symbol position offset after parity grouping, the above formula can be expanded to the sum of two sub-signals with different time domain offsets as follows:

[0076]

[0077] Among them, s(t) odd and s(t) even After completing the above operations, the two sub-signals are superimposed and output as the final super-Nyquist signal s(t) to the receiver.

[0078] Step S102: Acquire the FTN signal and perform matched filtering and BCJR sequence detection to extract LLR soft information at odd and even positions.

[0079] In step S102, the receiving end first receives the super-Nyquist signal s(t) generated and transmitted in step S101 through an optical fiber or other physical channel. After the signal is narrow-band filtered by the front end, it enters the coherent detection module and converts the optical carrier into a radio frequency baseband signal. Subsequently, the baseband signal passes through the analog-to-digital converter (ADC) and is converted into a radio frequency baseband signal in the sampling period T. s = τT, generating a discrete-time sequence. After sampling, the signal is filtered through a matched filter whose impulse response matches the raised cosine pulse shaping function used by the transmitter. This effectively improves the signal-to-noise ratio (SNR) of the received signal and initially suppresses some inter-symbol interference (ISI) introduced by super-Nyquist transmission. The matched-filtered signal is then input into the sequence receiving module based on the BCJR algorithm. This module performs maximum a posteriori probability (MAP) estimation on symbols at odd and even positions, obtaining soft information for each symbol at the corresponding position, namely the log-likelihood ratio (LLR). The LLR value reflects the relative confidence that each symbol in the received signal is "1" or "0" and is directly used in the subsequent soft decoding and interference cancellation processes.

[0080] Under ideal linear additive conditions, the total signal at the receiving end can be expressed as follows:

[0081]

[0082] Where g(t) is the impulse response of the matched filter, and w(t) represents Gaussian additive white noise. Since symbols are propagated with time offsets at odd and even positions, the above equation can be decomposed into the sum of two sub-signals and the noise as follows:

[0083]

[0084] Among them, r(t) odd and r(t) even The two structures represent the received signal components generated by odd-indexed symbols and even-indexed symbols, respectively. This structure clearly reveals the overlapping structure of the received signal in the time domain caused by the different time offsets introduced by the transmitter, and also provides a mathematical basis for the subsequent design of the parity feedback interference cancellation mechanism.

[0085] Step S103: Perform LDPC soft decoding on the LLR soft information at odd positions, and generate a Nyquist odd-number array signal based on bit decision.

[0086] In step S103, the soft information sequence of the log-likelihood ratio (LLR) of the symbols in the odd positions extracted in step S102 is first obtained. The LLR values of all odd positions are input into the LDPC soft decoder. The LDPC decoder iteratively executes based on the belief propagation algorithm: in each iteration, the bit node receives the message from the check node and updates the internal confidence based on its own initial LLR value. The updated message is then transmitted back to the check node, which then generates a new check message feedback based on the check equation. After several rounds of iterations, the decoder finally outputs the decision value sequence of the odd-numbered bits, which is recorded as:

[0087] {a 2k-1 '|k=1,2,…}

[0088] Among them, a 2k-1 ' is the hard decision bit for the 2k-1th symbol. After soft decoding, the time domain waveform of the Nyquist odd-number array signal is constructed in the digital signal processor based on the decision bit sequence. Specifically, each decision bit is pulse-shaped using the same raised cosine impulse response function as the matched filter at the receiver. The symbol period is strictly followed to ensure that the reconstructed signal maintains the same temporal structure as the odd-numbered subpath signal at the transmitter. The regenerated signal is denoted as:

[0089]

[0090] Among them, r(t)' odd The waveform is in the form of an ideal Nyquist signal and does not contain inter-symbol interference. It can be used as a reference signal to be subtracted from the total signal at the receiving end in the subsequent interference cancellation process.

[0091] Step S104: Subtract the total signal from the receiving end from the generated regenerated Nyquist odd-number array signal to obtain an even-number array feedback signal.

[0092] In step S104, the original signal r(t) transmitted in step S101 and transmitted to the receiving end through the channel is combined with the Nyquist array regeneration signal r(t)' generated in step S103. odd Perform differential operation to remove the interference component of the odd array on the received signal, thereby extracting the feedback signal containing the even array information The calculation formula is as follows:

[0093]

[0094] Step S105 : performing matched filtering and LDPC soft decoding on the even array feedback signal to obtain even position decision bits and regenerate the even signal portion.

[0095] In step S105, the even group feedback signal obtained in step S104 is The signal is then fed into the matched filtering module, where it undergoes matched filtering based on the Nyquist waveform to maximize the SNR at the sampling point. The even-numbered signals obtained after matched filtering still contain soft information, so they must first pass through the deinterleaving module to ensure that their order is consistent with that before encoding at the transmitter, ensuring the accuracy of subsequent error correction. After deinterleaving, the soft information is fed into the LDPC decoder dedicated to the even-numbered array for soft decoding. Based on bit-level probability information and redundant checksums, the decoder makes bit decisions within the iteration and outputs the decision bit sequence for even-numbered positions. Finally, the resulting even-numbered bit sequence is reinterleaved and mapped into modulation symbols. Waveform synthesis is then performed based on the Nyquist pulse to regenerate the even-numbered signal.

[0096] Step S106 , extracting the odd feedback signal by subtracting the even regenerated signal from the total signal at the receiving end, generating an odd decision based on Nyquist matched filtering and LDPC soft decoding, and iterating the process until the ISI is completely eliminated.

[0097] In step S106, the Nyquist even-numbered regenerated signal generated in step S105 is firstly subjected to a difference operation with the original aliased signal r(t) at the receiving end to eliminate the interference of the even-numbered component on the odd-numbered path, thereby extracting the odd-numbered feedback signal. as follows:

[0098]

[0099] Next, Nyquist matched filtering is applied to the extracted odd-numbered array feedback signal to maximize the recovery of the effective components of the odd-numbered array symbols and suppress residual noise and incompletely eliminated interference. After deinterleaving, the filtered output signal is sent to the LDPC decoder corresponding to the odd-numbered array for soft decoding, and a new odd-numbered position bit decision sequence is output. This decision sequence is then used for bit mapping and pulse shaping of the next round of regenerated odd-numbered array signals. Through the closed-loop iterative process of "odd array regeneration → differential extraction of even array feedback → matched filtering of even array → even array decoding → even array regeneration → differential extraction of odd array feedback → matched filtering of odd array → odd array decoding", inter-symbol interference (ISI) can be gradually eliminated in each round of iteration. As the iteration proceeds, when the odd-numbered array decoding decision and the even-numbered array decoding decision gradually approach the true symbol, the even-numbered array feedback signal The ISI term disappears and the output signal completely approaches the ideal Nyquist form:

[0100]

[0101] Similarly, odd-numbered feedback signals can gradually eliminate interference and approach the ideal Nyquist signal. Ultimately, the system bit error rate will approach the theoretical optimal value, achieving efficient and reliable reception of super-Nyquist signals.

[0102] In summary, combined with Figure 3 , this application divides the symbol sequence at the transmitting end into two sub-streams according to the timing position, and performs LDPC coding with a higher code rate and interleaving respectively to maximize the error correction gain in each group. At the receiving end, the two linearly added FTN signals are first matched filtered and sequence detected to extract the log-likelihood ratio soft information of the odd / even positions respectively; then the soft information of the odd and even arrays are alternately LDPC soft-decoded and the decision signal is regenerated, and the regenerated Nyquist sub-signal is subtracted from the original received signal to eliminate the interference of the corresponding group to form a feedback signal; the feedback signal is then matched filtered and decoded, and this cycle is repeated to achieve continuous iterative interference elimination until convergence. The closed-loop iterative structure based on FEC-regeneration-feedback-decoding does not rely on accurate channel estimation and does not require a large amount of training data; continuous iterations can gradually weaken the residual ISI until it is eliminated, and the system bit error rate eventually approaches the theoretical Nyquist limit. The odd and even sub-streams are alternately subjected to precise regeneration and differential interference cancellation, ensuring that each sub-stream can approximate the Nyquist signal after removing the other group of ISI, thereby achieving a near-optimal signal-to-noise ratio in the matched filtering link.

[0103] In order to verify the technical effect of this application, the proposed joint serial interference cancellation and packet forward error correction (FEC) method was verified on a digital simulation platform. The simulation signal adopts the polarization diversity multiplexing quadrature phase shift keying (PDM-QPSK) format and is input to the transmitter after passing through a first-order Gaussian narrowband filter with a 3dB bandwidth of 45GHz (corresponding to a symbol rate of 1 / 8). Figure 2 As shown in the figure, the original bit stream is grouped into odd and even positions, mapped to generate QPSK symbols, and pulse shaping is performed using root raised cosine (RRC) filtering with a roll-off factor of β = 0.5 and an acceleration factor of τ = 0.5. The forward error correction coding module uses LDPC code. The receiving end follows Figure 3 The signal is processed using the matched filtering, sequence detection, alternating SIC iteration, and packet FEC decoding process shown in FIG, and a relationship curve between the bit error rate (BER) and the energy ratio (Eb / N0) is calculated.

[0104] Reference Figure 4 , shows the BER curves for different EbN0s using a root-raised cosine (RRC) waveform with a roll-off factor of β = 0.5 and an acceleration factor of τ = 0.5. As can be seen, the proposed scheme achieves a significant reduction in the receiver BER when using 3 / 4 rate LPDC coding with 10 SIC iterations compared to the BER obtained by directly applying the same FEC to the FTN signal, with a 0.5 dB improvement at the threshold. When the number of iterations exceeds 65, the proposed system achieves a 0.9 dB improvement at the threshold.

[0105] Reference Figure 5 , showing the relationship between BER and EbN0 obtained by further simulating the data by grouping and independently encoding it by position, where odd3 / 4even9 / 10_10th indicates the use of 3 / 4 code rate LDPC forward error correction coding for the odd-numbered data arrays and 9 / 10 code rate LDPC forward error correction coding for the even-numbered data arrays. The BER at the receiving end obtained by the proposed solution using 10 SIC iterations of the above-mentioned odd and even-numbered error coding is significantly lower than the BER obtained by directly performing full 3 / 4 code rate FEC on the FTN signal, with an improvement of 0.6 dB at the threshold. In addition, the system redundancy is reduced from 33.33% to 21.21%. While improving performance, the spectrum efficiency is also increased from 2bps / Hz to 2.2bps / Hz according to Formula 11.

[0106] The significant performance improvement demonstrated by this solution demonstrates the effectiveness of improving the robustness of the FTN system against severe inter-symbol interference (ISI). It significantly enhances the transmission reliability and system applicability of FTN signals, providing key technical support for its practical application in high-speed communication scenarios.

[0107] Figure 6 This is a block diagram of a super-Nyquist signal iterative interference cancellation system based on symbol grouping and FEC, provided by one embodiment of the present application. The device includes at least the following modules:

[0108] The symbol grouping module is used to obtain the upper layer original bit stream, group the symbols according to the odd and even positions and independently perform FEC coding. Based on RRC pulse shaping, the parity and even sub-signals are generated and superimposed to generate the FTN signal.

[0109] Soft information extraction module, used to obtain FTN signals and perform matched filtering and BCJR sequence detection to extract LLR soft information at odd and even positions;

[0110] An odd signal regeneration module is used to perform LDPC soft decoding on the LLR soft information at odd positions and generate Nyquist odd-number array signals based on bit decisions;

[0111] an even feedback signal generating module, configured to obtain an even feedback signal by subtracting the generated regenerated Nyquist odd-number array signal from the total signal at the receiving end;

[0112] The even signal processing module is used to perform matched filtering and LDPC soft decoding on the even array feedback signal, obtain the even position decision bits, and regenerate the even signal part;

[0113] The loop iteration module is used to extract the odd feedback signal by subtracting the even regenerated signal from the total signal at the receiving end, generate the odd decision based on Nyquist matched filtering and LDPC soft decoding, and loop iterate until the ISI is completely eliminated.

[0114] For relevant details, please refer to the above method embodiment.

[0115] Figure 7 4 is a block diagram of an electronic device provided in one embodiment of the present application. The device includes at least a processor 401 and a memory 402.

[0116] The processor 401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 401 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 401 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0117] Memory 402 may include one or more computer-readable storage media, which may be non-transitory. Memory 402 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in memory 402 is used to store at least one instruction, which is executed by processor 401 to implement the super-Nyquist signal iterative interference cancellation method based on symbol grouping and FEC provided in the method embodiment of the present application.

[0118] In some embodiments, the electronic device may optionally include a peripheral device interface and at least one peripheral device. The processor 401, memory 402, and peripheral device interface may be connected via a bus or signal lines. Each peripheral device may be connected to the peripheral device interface via a bus, signal lines, or circuit boards. Illustratively, the peripheral devices include, but are not limited to, a radio frequency circuit, a touchscreen display, an audio circuit, and a power supply.

[0119] Of course, the electronic device may also include fewer or more components, which is not limited in this embodiment.

[0120] Optionally, the present application also provides a computer-readable storage medium, in which a program is stored. The program is loaded and executed by a processor to implement the super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC of the above method embodiment.

[0121] Optionally, the present application also provides a computer product, which includes a computer-readable storage medium, in which a program is stored. The program is loaded and executed by a processor to implement the super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC of the above-mentioned method embodiment.

[0122] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The above embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A super-Nyquist signal iterative interference cancellation method based on symbol grouping and FEC, characterized in that: The method comprises: Obtain the upper layer original bit stream, group the symbols by odd and even positions and perform FEC coding independently, generate and superimpose the odd and even sub-signals based on RRC pulse shaping to generate the FTN signal; Acquire the FTN signal and perform matched filtering and BCJR sequence detection to extract the LLR soft information of the odd and even positions; Perform LDPC soft decoding on the LLR soft information at odd positions and generate Nyquist odd-number array signals based on bit decisions; The even array feedback signal is obtained by subtracting the total signal at the receiving end from the generated regenerated Nyquist odd array signal; Perform matched filtering and LDPC soft decoding on the even array feedback signal to obtain even position decision bits and regenerate the even signal part; The odd feedback signal is extracted by subtracting the even regenerated signal from the total signal at the receiving end. The odd decision is generated based on Nyquist matched filtering and LDPC soft decoding, and the process is iterated until the ISI is completely eliminated.

2. The super-Nyquist signal iterative interference cancellation method based on symbol grouping and FEC according to claim 1, characterized in that: The grouping of symbols according to odd and even positions comprises: Mapping the original bit stream into a symbol sequence {a n }, based on the position attribute of the symbols in the time series, the symbols are grouped into data groups with odd and even positions, and the symbols are divided into two categories:

3. The method for iterative interference cancellation of super-Nyquist signals based on symbol grouping and FEC according to claim 2, characterized in that: Generating and superimposing the odd and even sub-signals based on RRC pulse shaping to generate the FTN signal includes: Each coded symbol is pulse-shaped based on the raised cosine pulse c(t), and the symbol interval is compressed to τT using the acceleration factor τ. The overall transmitted waveform s(t) can be expressed as: Among them, a n Indicates the symbol value of the nth position, τ represents the acceleration factor, E s is the average power of the transmitted signal, and T represents the traditional Nyquist symbol period. Combined with the symbol position offset after parity grouping, the above formula can be expanded to the sum of two sub-signals with different time domain offsets as follows: Among them, s(t) odd and s(t) even They represent the sub-signals generated by the odd and even arrays respectively. The two sub-signals are superimposed and output as the final super-Nyquist signal s(t) to the receiving end.

4. The method for iterative interference cancellation of super-Nyquist signals based on symbol grouping and FEC according to claim 3, characterized in that: The acquiring of the FTN signal and performing matched filtering and BCJR sequence detection to extract LLR soft information of the parity position includes: The receiver receives the generated super-Nyquist signal s(t), which is then narrow-band filtered by the front end and then enters the coherent detection module, converting the optical carrier into an RF baseband signal. The baseband signal is then filtered by the analog-to-digital converter to generate a discrete time sequence. The filtered signal is then input into the sequence receiving module based on the BCJR algorithm, which performs maximum a posteriori probability estimation on the symbols at odd and even positions, obtaining the LLR soft information for each symbol at the corresponding position. Under ideal linear additive conditions, the total signal at the receiving end can be expressed as follows: Where g(t) is the impulse response of the matched filter, and w(t) represents Gaussian additive white noise. The above equation can be expanded into the sum of two sub-signals with different time domain offsets as follows: Among them, r(t) odd and r(t) even Represent the parts of the received signal generated by odd-indexed symbols and even-indexed symbols, respectively.

5. The method for iterative interference cancellation of super-Nyquist signals based on symbol grouping and FEC according to claim 4, characterized in that: The performing LDPC soft decoding on the LLR soft information at odd positions and generating a Nyquist odd-number array signal based on bit decision includes: All LLR values at odd positions are input into the LDPC soft decoder, and the decoder outputs the decision value sequence of the odd position bits, which is recorded as: {a 2k-1 '|k=1,2,…} Among them, a 2k-1 ' is the hard decision bit of the 2k-1th symbol. After soft decoding, the same raised cosine impulse response function as the matched filter at the receiving end is used to perform pulse shaping on each decision bit. The regenerated signal is recorded as:

6. The method for iterative interference cancellation of super-Nyquist signals based on symbol grouping and FEC according to claim 5, characterized in that: The step of subtracting the generated regenerated Nyquist odd-number array signal from the total signal at the receiving end to obtain the even-number array feedback signal comprises: The original signal r(t) is combined with the generated Nyquist array to regenerate the signal r(t)' odd Perform differential operation to remove the interference component of the odd array on the received signal and extract the feedback signal containing the even array information The calculation formula is as follows:

7. The method for iterative interference cancellation of super-Nyquist signals based on symbol grouping and FEC according to claim 6, characterized in that: The method of extracting an odd feedback signal by subtracting an even regenerated signal from a total signal at the receiving end, generating an odd decision based on Nyquist matched filtering and LDPC soft decoding, and iterating until the ISI is completely eliminated includes: Perform a difference operation on the generated Nyquist even array regeneration signal and the original aliased signal r(t) at the receiving end to extract the odd array feedback signal as follows: Nyquist matched filtering is applied to the extracted odd-numbered feedback signal. The filtered output signal is deinterleaved and then sent to the LDPC decoder corresponding to the odd-numbered group for soft decoding, outputting a new odd-numbered position bit decision sequence. This decision sequence is used for bit mapping and pulse shaping of the next round of regenerated odd-number signals. By repeating the closed-loop iterative process, inter-symbol interference is gradually eliminated in each round of iteration. As the iteration proceeds, when the decoding decisions of the odd and even groups gradually approach the true symbols, the feedback signal of the even group The ISI term disappears and the output signal completely approaches the ideal Nyquist form: Similarly, the odd-numbered feedback signals can gradually eliminate interference and approach the ideal Nyquist signal.

8. A super-Nyquist signal iterative interference cancellation system based on symbol grouping and FEC, characterized in that: include: The symbol grouping module is used to obtain the upper layer original bit stream, group the symbols according to the odd and even positions and independently perform FEC coding. Based on RRC pulse shaping, the parity and even sub-signals are generated and superimposed to generate the FTN signal. Soft information extraction module, used to obtain FTN signals and perform matched filtering and BCJR sequence detection to extract LLR soft information at odd and even positions; An odd signal regeneration module is used to perform LDPC soft decoding on the LLR soft information at odd positions and generate Nyquist odd-number array signals based on bit decisions; an even feedback signal generating module, configured to obtain an even feedback signal by subtracting the generated regenerated Nyquist odd-number array signal from the total signal at the receiving end; The even signal processing module is used to perform matched filtering and LDPC soft decoding on the even array feedback signal, obtain the even position decision bits, and regenerate the even signal part; The loop iteration module is used to extract the odd feedback signal by subtracting the even regenerated signal from the total signal at the receiving end, generate the odd decision based on Nyquist matched filtering and LDPC soft decoding, and iterate until the ISI is completely eliminated.

9. An electronic device, characterized in that: The device includes a processor and a memory; the memory stores a program, and the program is loaded and executed by the processor to implement the super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The storage medium stores a program, which, when executed by a processor, is used to implement the super-Nyquist signal iterative interference elimination method based on symbol grouping and FEC as described in any one of claims 1 to 7.

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