A signal processing method and receiver for super-Nyquist direct detection system

Abstract

The present invention discloses a signal processing method and receiver for a super-Nyquist direct detection system, the method comprising: obtaining the output of the post-filter and a training sequence after the signal passes through the post-filter; re-estimating the channel based on the training sequence and the output of the post-filter using the least square method LS to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE; after obtaining the tap coefficients of the MLSE, finding the best path using the MLSE based on the Viterbi algorithm, and after obtaining the final surviving path, the signal on the path is the decision signal. The present invention realizes the optimization of the MLSE tap coefficients in the direct detection technology of the PAM4 signal, which can greatly improve the system performance. And in terms of computational complexity, it does not increase much compared to the original system.

Description

Technical Field

[0001] The present invention relates to the technical field of DDFTN (Direct Detection Fasterthan Nyquist) signal processing technology for short-distance high-speed optical communication, and in particular to a signal processing method and a receiver for a super-Nyquist direct detection system for realizing MLSE (Maximum Likelihood Sequence Estimation) tap coefficient optimization in a PAM (Pulse Amplitude Modulation) signal direct detection technology. Background Art

[0002] The massive growth of intensive applications such as data centers, media services, and cloud storage has led to a significant increase in data traffic in fiber edge networks. Due to the huge market size of such networks, the future development trend of optical transceivers must be economical and efficient. Intensity modulation (IM) / direct detection (DD) optical transmission systems have significant advantages in terms of cost and reliability, and can meet the power consumption and cost-effectiveness required by these cost-sensitive applications, so DD technology is usually considered in short-distance optical communications. In order to increase the transmission capacity in IM-DD systems, high-order modulation technology is usually used. The hot spots of modulation technology research in IM-DD systems mainly include DMT (Discrete Multi-Tone) based on multi-carrier modulation and PAM4 modulation schemes. Among them, DMT technology has high implementation complexity, while PAM4 has the advantages of simple implementation.

[0003] However, there are still some problems to be solved in PAM4 modulation in IM-DD systems, such as demodulation of PAM4 signals in band-limited systems and low-complexity equalization technology. Currently, a signal processing technology based on "linear equalizer + DDFTN" is mainly used to deal with noise and inter-symbol interference. DDFTN consists of a postfilter and MLSE. Postfilter is mainly used to counteract the noise enhanced after linear equalizer processing, and MLSE is used to deal with the remaining inter-symbol interference.

[0004] Prior art 1: a two-tap post-filter plus MLSE. The post-filter has a dual binary mode and can be simply implemented by a two-tap FIR structure, where one tap coefficient is 1 and is fixed, and the other tap coefficient is not fixed, represented by α, and its transfer function is H(z)=1+αz -1, different α corresponds to different post-filter shapes, and different system rates correspond to different optimal parameters. α does not need to be calculated, and the system performance is optimized by adjusting the value of α. Because the post-filter has a fixed partial response mode, the memory length of MLSE corresponds to the Postfilter, so the memory length of MLSE is one at this time, and the tap coefficient is the tap coefficient of the post-filter. Because the memory length is short and no channel estimation is required to calculate the tap coefficient of MLSE, it has a great advantage in computational complexity.

[0005] Prior art 2, multi-tap post-filter plus MLSE. In order to further improve the performance of the system, a multi-tap post-filter is proposed. The tap coefficients of the post-filter are no longer fixed. The tap coefficients of the post-filter are calculated by the autocorrelation function of the noise. The performance of the system is improved by increasing the number of taps of the post-filter. The tap coefficients of MLSE correspond to the previous post-filter, and there is no need to re-calculate the channel estimation to calculate the tap coefficients of MLSE. Compared with the two-tap Postfilter plus MLSE, the multi-tap post-filter plus MLSE has a certain improvement in performance, but the tap coefficients of the post-filter need to be recalculated, and as the tap coefficients increase, the computational complexity of MLSE also increases. Therefore, a trade-off should be made between system performance and computational complexity.

[0006] In summary, in the prior art, the tap coefficients of MLSE correspond to the tap coefficients of Postfilter. Although this saves the need to recalculate the MLSE tap coefficients, there is a significant loss in performance. Although the performance can be improved by increasing the number of postfilter taps, the effect is not obvious. In addition, the channel response is shortened according to the memory length set by Postfilter and the transmission channel response outside the memory length is discarded, which causes the MLSE algorithm to lose part of the channel response in the process of compensating for signal damage, thereby affecting the overall performance of the algorithm. Therefore, it is necessary to further improve the prior art. Summary of the invention

[0007] The present invention provides a super-Nyquist direct detection system signal processing method and receiver to solve the technical problem in the prior art that, since the tap coefficients of MLSE correspond to the tap coefficients of a postfilter, although the recalculation of the MLSE tap coefficients is omitted, there is a great loss in performance.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] On the one hand, the present invention provides a super-Nyquist direct detection system signal processing method, which is used at a receiving end in a super-Nyquist direct detection system. The signal processing method comprises:

[0010] After the signal passes through the post-filter, the output of the post-filter and the training sequence are obtained;

[0011] Based on the training sequence and the output of the post-filter, the channel is re-estimated using the least squares method LS to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE;

[0012] After obtaining the tap coefficients of MLSE, the best path is found by using MLSE based on the Viterbi algorithm. After obtaining the final surviving path, the signal on the path is the decision signal.

[0013] Furthermore, the channel estimation is re-performed based on the training sequence and the output of the post-filter using the least square method LS to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE, including:

[0014] When performing least squares estimation, first take out a part of the training sequence as the least squares training sequence; then, based on the taken out least squares training sequence and the output of the post-filter, use the least squares method LS to re-estimate the channel and obtain the tap coefficients of the maximum likelihood sequence estimation MLSE.

[0015] Furthermore, the channel estimation is performed again based on the training sequence and the output of the post-filter using the least square method LS to obtain the formula for the tap coefficients of the maximum likelihood sequence estimation MLSE:

[0016] w=(X T X) -1 X T Y

[0017] Wherein, w represents the tap coefficient of MLSE calculated by the least square method LS, Y represents the output of the post-filter, and X represents the training sequence of the least square method.

[0018] Further, the number of MLSE points does not exceed the original channel response.

[0019] On the other hand, the present invention also provides a super-Nyquist direct detection receiver, comprising:

[0020] The MLSE tap coefficient calculation module is used to obtain the output of the post-filter and the training sequence after the signal passes through the post-filter; based on the training sequence and the output of the post-filter, the least square method LS is used to re-estimate the channel to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE;

[0021] The MLSE module is used to find the best path by using MLSE based on the Viterbi algorithm after obtaining the tap coefficients of the MLSE. After obtaining the final surviving path, the signal on the path is the decision signal.

[0022] Furthermore, the MLSE tap coefficient calculation module is specifically used for:

[0023] When performing least squares estimation, first take out a part of the training sequence as the least squares training sequence; then, based on the taken out least squares training sequence and the output of the post-filter, use the least squares method LS to re-estimate the channel and obtain the tap coefficients of the maximum likelihood sequence estimation MLSE.

[0024] Furthermore, the channel estimation is performed again based on the training sequence and the output of the post-filter using the least square method LS to obtain the formula for the tap coefficients of the maximum likelihood sequence estimation MLSE:

[0025] w=(X T X) -1 X T Y

[0026] Wherein, w represents the tap coefficient of MLSE calculated by the least square method LS, Y represents the output of the post-filter, and X represents the training sequence of the least square method.

[0027] Furthermore, when the best path is found by using MLSE based on the Viterbi algorithm, the number of MLSE points does not exceed the original channel response.

[0028] On the other hand, the present invention further provides an electronic device, comprising a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above method.

[0029] In yet another aspect, the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the above method.

[0030] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0031] The present invention proposes a new improved DDFTN, which re-performs the channel estimation through the least square method after the Postfilter to obtain the tap coefficient of the MLSE, so that the tap coefficient of the MLSE is closer to the original channel response. As the number of MLSE points increases, the performance of the system will become better and better, but the number of points cannot exceed the original channel response. The technical solution of the present invention realizes the optimization of the MLSE tap coefficient in the direct detection technology of the PAM4 signal. Compared with the previous method of directly using the tap coefficient of the Postfilter as the tap coefficient of the MLSE, the performance of the system is greatly improved after recalculating the tap coefficient of the MLSE. At the same time, since the LS algorithm is the basis of the channel estimation algorithm, and has low complexity, simple structure, stable performance, and easy implementation, the technical solution of the present invention does not increase much in terms of computational complexity compared to the original system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 is a block diagram of a super-Nyquist direct detection system provided by an embodiment of the present invention;

[0034] Figure 2 It is a simplified model diagram of the Postfilter provided by an embodiment of the present invention;

[0035] Figure 3 is a least squares fitting image provided by an embodiment of the present invention;

[0036] Figure 4 Schematic diagram of an MLSE decision path based on the Viterbi algorithm provided in an embodiment of the present invention;

[0037] Figure 5 It is a digital signal processing flow chart of the receiving end provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] First embodiment

[0040] This embodiment provides a super-Nyquist direct detection system signal processing method, such as Figure 1As shown in the figure, in the super-Nyquist direct detection system, at the transmitting end, the pseudo-random binary sequence is mapped into the PAM4 format. After pre-emphasis, the PAM4 signal is uploaded to the arbitrary waveform generator (AWG). Then a power amplifier is used to enhance the electrical signal to drive the Mach-Zehnder modulator (MZM) with an ECL laser, and the output optical signal is launched into a standard single-mode fiber (SSMF).

[0041] At the receiving end, the variable optical attenuator (VOA) adjusts the received optical power (ROP) after the SSMF. The signal is then detected by a photodetector (PD) and captured by a real-time sampling oscilloscope (RTO). After being sampled by an analog-to-digital converter, the PAM4 signal is recovered using digital signal processing (DSP). The signal passes through a postfilter, and the simplified structure of the postfilter is as follows: Figure 2 When the training sequence and the output of the post-filter are known, this embodiment uses the least squares method LS to re-estimate the channel. The least squares fitting image is as follows: Figure 3 After obtaining the tap coefficients of MLSE, the Viterbi algorithm is used to find the best path. The MLSE based on the Viterbi algorithm is shown as follows: Figure 4 The receiving end DSP processing flow chart is shown in Figure 5 shown.

[0042] Specifically, the receiving end DSP processing flow includes the following steps:

[0043] After the linear equalizer at the receiving end, most of the inter-symbol interference has been eliminated by the minimum mean square error algorithm LMS, but at the same time the noise is also enhanced. In order to further suppress the enhanced noise, the signal is passed through a two-tap post-filter. The transfer function of the post-filter is:

[0044] H(z)=1+αz -1 (1)

[0045] Among them, α represents the second tap coefficient of Postfilter, and the output of the postfilter can be expressed as:

[0046] Y(i)=r(i)+α*r(i+1) (2)

[0047] Among them, r(i) represents the signal received by the post-filter;

[0048] After obtaining the output of the post-filter, the next step is to use a simple least squares method LS to perform channel estimation. Assume that D = {(x1, y1), (x2, y2), ... (x N ,y N )} is a data set, x i ∈R p ,y i∈R,

[0049]

[0050]

[0051] Among them, Y represents the output of the post-filter, and the linear equation of the least squares can be expressed as:

[0052] f(w)=w T x (5)

[0053] Here, f(w) refers to the value of the training sequence, w refers to the calculated tap coefficient, and the least squares method finds the best function match for the data by minimizing the sum of squares of the error. The least squares estimation method can be used to easily obtain unknown data and minimize the sum of squares of the error between the obtained data and the actual data. The tap coefficients obtained by the least squares method can be expressed as:

[0054] w=(X T X) -1 X T Y (6)

[0055] When performing the least squares estimation, first take out a part of the training sequence as the least squares training sequence. The length of the sequence taken out also has an optimal corresponding value and cannot be too long or too short. After the tap coefficients of MLSE are obtained by the least squares estimation, the Viterbi algorithm starts to find the most likely path after the sequence taken out. The number of MLSE taps based on the Viterbi algorithm is an odd number, and the number of taps is related to the memory length of the signal. The expression is as follows:

[0056] n=2*k+1 (7)

[0057] Among them, n represents the number of MLSE points, k represents the memory length of the signal. Because the signal estimation is related to the symmetrical signals before and after, the number of MLSE taps is an odd number. When k = 1, the number of MLSE points is three. As the number of points increases, the system performance will get better and better. The expression for MLSE estimation is:

[0058] y i =w1x i-1 +w2x i +w3x i+1 (8)

[0059] Among them, y i represents the estimated value at time i, w represents the tap coefficient obtained by least squares, and x represents the possible value of the PAM4 signal. After obtaining the estimated value at time i, the minimum error corresponding to this time can be calculated, that is, the minimum Euclidean distance, which is expressed as:

[0060] d=(y i '-y i ) 2 (9)

[0061] Among them, d represents the Euclidean distance, y i ' indicates the estimated value, y i The output of the post-filter is the input of the MLSE. When the Viterbi algorithm is performed, the number of distances to be calculated for different signals is different. When the signal is PAM4 and the MLSE memory length is 1, the corresponding possible path is 4. 2 =16, because PAM4 has four possible states at each moment (-3, -1, 1, 3), and there is a set of corresponding distances between every two states. The final retained path distance is expressed as:

[0062] d i =min{d i-1 +(w1x i-1 +w2x i +w3x i+1 -y i ) 2} (10)

[0063] After the final surviving path is obtained, the signal on the path is the decision signal.

[0064] In summary, the technical solution of this embodiment further optimizes the tap coefficients of MLSE on the basis of the existing DDFTN, so that the tap coefficients of MLSE are closer to the original channel response, the bit error rate is reduced, and the system performance is improved. In addition, this embodiment uses LS (Least Squares Method) to re-estimate the channel after the two-tap Postfilter. The least squares algorithm is extremely simple compared to other channel estimation algorithms, but it is easily affected by noise. Because the previous Postfilter has filtered out most of the noise, there is no need to worry about this problem. Although a certain amount of computational complexity is added, the performance of the system can be greatly improved. When the system performance is significantly improved, the corresponding increase in computational complexity is acceptable, and the problem of the MLSE decision signal being not accurate enough can be effectively solved.

[0065] Second embodiment

[0066] This embodiment provides a super-Nyquist direct detection receiver, which includes:

[0067] The MLSE tap coefficient calculation module is used to obtain the output of the post-filter and the training sequence after the signal passes through the post-filter; based on the training sequence and the output of the post-filter, the least square method LS is used to re-estimate the channel to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE;

[0068] The MLSE module is used to find the best path by using MLSE based on the Viterbi algorithm after obtaining the tap coefficients of the MLSE. After obtaining the final surviving path, the signal on the path is the decision signal.

[0069] The super-Nyquist direct detection receiver of the present embodiment corresponds to the super-Nyquist direct detection system signal processing method of the above-Nyquist first embodiment; wherein, the functions implemented by each functional module in the super-Nyquist direct detection receiver of the present embodiment correspond one-to-one to each process step in the super-Nyquist direct detection system signal processing method of the above-Nyquist first embodiment; therefore, they will not be repeated here.

[0070] Third embodiment

[0071] This embodiment provides an electronic device, which includes a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method of the first embodiment.

[0072] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) and one or more memories, wherein the memory stores at least one instruction, and the instruction is loaded by the processor to execute the above method.

[0073] Fourth embodiment

[0074] This embodiment provides a computer-readable storage medium, which stores at least one instruction, and the instruction is loaded and executed by a processor to implement the method of the first embodiment. The computer-readable storage medium may be a ROM, a random access memory, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. The instructions stored therein may be loaded by a processor in a terminal to execute the method.

[0075] In addition, it should be noted that the present invention can be provided as a method, an apparatus or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0076] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] It should also be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0079] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A super-Nyquist direct detection system signal processing method, used for a receiving end in a super-Nyquist direct detection system, characterized in that: The super-Nyquist direct detection system signal processing method comprises: After the signal passes through the post-filter, the output of the post-filter and the training sequence are obtained; Based on the training sequence and the output of the post-filter, the channel is re-estimated using the least squares method LS to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE; After obtaining the tap coefficients of MLSE, the best path is found by using MLSE based on the Viterbi algorithm. After obtaining the final surviving path, the signal on the path is the decision signal. The method of re-estimating the channel based on the output of the training sequence and the post-filter using the least square method LS to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE includes: When performing the least squares estimation, firstly, a part of the training sequence is taken out as the least squares training sequence; then, according to the taken out least squares training sequence and the output of the post-filter, the least squares method LS is used to re-perform the channel estimation to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE; The formula for re-estimating the channel based on the training sequence and the output of the post-filter using the least square method LS to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE is: w=(X T X) -1 X T Y Wherein, w represents the tap coefficient of MLSE calculated by the least squares method LS, Y represents the output of the post-filter, and X represents the training sequence of the least squares method; The number of MLSE points does not exceed the original channel response.

2. A super-Nyquist direct detection receiver, characterized in that: include: The MLSE tap coefficient calculation module is used to obtain the output of the post-filter and the training sequence after the signal passes through the post-filter; Based on the training sequence and the output of the post-filter, the channel is re-estimated using the least squares method LS to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE; The MLSE module is used to find the best path by using the MLSE based on the Viterbi algorithm after obtaining the tap coefficients of the MLSE. After obtaining the final surviving path, the signal on the path is the decision signal; The MLSE tap coefficient calculation module is specifically used for: When performing the least squares estimation, firstly, a part of the training sequence is taken out as the least squares training sequence; then, according to the taken out least squares training sequence and the output of the post-filter, the least squares method LS is used to re-perform the channel estimation to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE; The formula for re-estimating the channel based on the training sequence and the output of the post-filter using the least square method LS to obtain the tap coefficients of the maximum likelihood sequence estimation MLSE is: w=(X T X) -1 X T Y Wherein, w represents the tap coefficient of MLSE calculated by the least squares method LS, Y represents the output of the post-filter, and X represents the training sequence of the least squares method; When the best path is found by using MLSE based on the Viterbi algorithm, the number of MLSE points does not exceed the original channel response.

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