Irregular Polar Coding for Complexity-Constrained Lightwave Systems
Toshiaki Koike–Akino, Congzhe Cao, Ye Wang, Stark Christiaan Draper, David S. Millar, Keisuke Kojima, Kieran Parsons, Lídia Galdino, Daniel J. Elson, Domaniç Lavery, Polina Bayvel
Next-generation fiber-optic communications call for ultra-reliable forward error correction codes that are capable of low-power and low-latency decoding. In this paper, we propose a new class of polar codes, whose polarization units are irregularly pruned to reduce computational complexity and decoding latency without sacrificing error correction performance. We then experimentally demonstrate that the proposed irregular polar codes can outperform state-of-the-art low-density parity-check (LDPC) codes, while decoding complexity and latency can be reduced by at least 30% and 70%, respectively, versus regular polar codes, while also obtaining a marginal performance improvement.
Next-generation fiber-optic communications call for ultra-reliable forward error correction codes that are capable of low-power and low-latency decoding. In this paper, we propose a new class of polar codes, whose polarization units are irregularly pruned to reduce computational complexity and decoding latency without sacrificing error correction performance. We then experimentally demonstrate that the proposed irregular polar codes can outperform state-of-the-art low-density parity-check (LDPC) codes, while decoding complexity and latency can be reduced by at least 30% and 70%, respectively, versus regular polar codes, while also obtaining a marginal performance improvement.
The ultra-reliable low-latency communication (URLLC), one of the three main scenarios in the fifth-generation (5G) mobile communication system, is promising to be used in various emerging applications with stringent latency and performance requirements. Recently, the guessing random additive noise decoding (GRAND) is proposed, which can perform well for the URLLC due to its excellent error-correction performance with short code length. Compared to other variants of the GRAND, the ordered reliability bits GRAND (ORBGRAND) can provide a good trade-off between error-correction performance and hardware overhead. In this work, the ORBGRAND with a reduced schedule for polar codes is implemented for the first time, which utilizes the special structure of the parity-check matrix of polar codes to reduce the decoding latency while keeping the error-correction performance unaltered. Besides, the sorter employed in the ORBGRAND decoder is well optimized to reduce the area occupation. At a target frame error rate of 10−5when using a (128, 105) 5G polar code, the average decoding latency of the proposed decoder is only 2.46 ns. Moreover, compared to the state-of-the-art ORBGRAND decoder, the proposed decoder can achieve 15% higher decoding throughput and 71% higher area efficiency without any error-correction performance loss.
Next-generation high-speed optical transport systems require powerful forward error correction (FEC) codes with high error-correcting capabilities to ensure high data reliability. Low density parity check (LDPC) codes are strong candidates for ensuring the high data reliability needed to support data rates of 100 Gb/s, demanded by optical systems. Non-binary LDPC (NB-LDPC) codes provides better performance compared to binary version, as they offer larger coding gains with shorter code lengths and reduced system latency. Despite their advantages, LDPC codes suffer from error floor problem at low bit error rates (BER), with NB-LDPC codes showing error floors at BER around 10-7. High-speed optical transport systems generally require BERs of the order of 10-12. The error floor problem can be mitigated by concatenating LDPC codes with a shorter outer code. Existing works mainly focus on employing Reed Solomon (RS) codes to remove error floor. However, the decoding complexity is high for these codes. This paper proposes concatenated Polar and NB-LDPC codes to address the error floor issue, since polar codes have relatively low encoding and decoding complexity. The channel noise in optical communication systems using optical amplifiers cannot be analyzed using simplified additive white Gaussian noise (AWGN). Hence the channel noise is mathematically derived and proved that chi-square channel model is appropriate than AWGN. Simulation results demonstrate that the proposed concatenated codes over proposed channel model can achieve a net coding gain of around 1.5dB at a BER of 10-12.
In the case of short code, polar code demonstrates superior performance compared with Low Density Parity Check (LDPC) code. Particularly in low coding rate scenario, the complexity of Polar code encoding and decoding is significantly lower than that of LDPC code. The technological advantage of Polar code makes it of great value in special application scenarios such as satellite communication and ocean communication. The polar coding scheme can significantly improve the transmission reliability of the communication system, because the satellite spread spectrum communication system generally adopts short code design.In the research of decoding algorithm, the original Serial Cancellation (SC) decoding algorithm has simple rules, but the decoding effect is not ideal under the limited code length, and the serial decoding mechanism leads to large decoding delay. The Successive Cancellation List (SCL) decoding algorithm improves decoding performance by increasing the number of decoding search path. However, maintaining multiple decoding paths can multiply the decoding complexity. Fast node-based decoding algorithm aims to accelerate decoding and reduce latency by defining and simplifying the decoding process for specific nodes, yet they do not optimize the decoding paths. In view of the above problems, this paper proposes a Simplified Parity Check-based Cyclic Redundancy Check (CRC)-Aided Successive Cancellation List (S-PC-CASCL) decoding algorithm. Simulation results demonstrate that the proposed S-PC-CASCL maintains excellent error correction performance while reducing the average list size by up to 16 % under low Signal Noise Ratio (SNR) conditions, thereby lowering computational complexity and decoding latency compared with other widely used polar decoding methods.
Error correctiong codes (ECC) are widly used in applications to correct errors in data transmission over unreliable or noisy communication channels. Recently, two kinds of promising codes attracted lots of research interest because they provide excellent error correction performance. One is non-binary LDPC codes, and the other is polar codes. This dissertation focuses on efficient decoding algorithms and decoder design for thesetwo types of codes.Non-binary low-density parity-check (LDPC) codes have some advantages over their binary counterparts, but unfortunately their decoding complexity is a significant challenge. The iterative hard- and soft-reliability based majority-logic decoding algorithms are attractive for non-binary LDPC codes, since they involve only finite field additions and multiplications as well as integer operations and hence have significantly lower complexity than other algorithms. We propose two improvements to the majority-logic decoding algorithms. Instead of the accumulation of reliability information in the ex-isting majority-logic decoding algorithms, our first improvement is a new reliability information update. The new update not only results in better error performance and fewer iterations on average, but also further reduces computational complexity. Since existing majority-logic decoding algorithms tend to have a high error floor for codes whose parity check matrices have low column weights, our second improvement is a re-selection scheme, which leads to much lower error floors, at the expense of more finite field operations and integer operations, by identifying periodic points, re-selectingintermediate hard decisions, and changing reliability information.Polar codes are of great interests because they provably achieve the symmetric capacity of discrete memoryless channels with arbitrary input alphabet sizes an explicit construction. Most existing decoding algorithms of polar codes are based on bit-wise hard or soft decisions. We propose symbol-decision successive cancellation (SC) and successive cancellation list (SCL) decoders for polar codes, which use symbol-wise hard or soft decisions for higher throughput or better error performance. Then wepropose to use a recursive channel combination to calculate symbol-wise channel transition probabilities, which lead to symbol decisions. Our proposed recursive channel combination has lower complexity than simply combining bit-wise channel transition probabilities. The similarity between our proposed method and Arıkan's channel transformations also helps to share hardware resources between calculating bit- and symbol-wise channel transition probabilities. To reduce the complexity of the list pruning, atwo-stage list pruning network is proposed to provide a trade-off between the error performance and the complexity of the symbol-decision SCL decoder. Since memory is a significant part of SCL decoders, we also propose a pre-computation memory-saving technique to reduce memory requirement of an SCL decoder.To reduce the complexity of the recursive channel combination further, we propose an approximate ML (AML) decoding unit for SCL decoders. In particular, we investigate the distribution of frozen bits of polar codes designed for both the binary erasure and additive white Gaussian noise channels, and take advantage of the distribution to reduce the complexity of the AML decoding unit, improving the throughput-area efficiency of SCL decoders.Furthermore, to adapt to variable throughput or latency requirements which exist widely in current communication applications, a multi-mode SCL decoder with variable list sizes and parallelism is proposed. If high throughput or small latency is required, the decoder decodes multiple received words in parallel with a small list size. However, if error performance is of higher priority, the multi-mode decoder switches to a serialmode with a bigger list size. Therefore, the multi-mode SCL decoder provides a flexible tradeoff between latency, throughput and error performance at the expense of small overhead.
We construct new families of low-density parity-check codes, which we call irregular codes. When decoded using belief propagation, our codes can correct more errors than previously known low-density codes. Our improved performance comes from using codes based on irregular random bipartite graphs, based on the work of Luby et al. (1997). Previously studied low-density codes have been derived from regular bipartite graphs. Initial experimental results for our irregular codes suggest that, with improvements, irregular codes may be able to match turbo code performance.
Nowadays concatenated codes are actively developed for different applications of error-correcting theory. In this paper we propose a new method for constructing concatenated codes consisting of some outer error-correcting code and a particular designed inner low-density parity-check (LDPC) code. We consider polarization-adjusted convolutional (PAC) code and LDPC code as outer code of suggested construction. A specialized optimization algorithm was developed to generate inner code with particular error-reducing properties. By using the woven codes decoder with this design, the complexity of the decoding procedure is reduced compared to traditional polar and LDPC codes decoders, while still providing error-correcting characteristics. Additionally, we enhance the performance of our system by puncturing the encoded symbols. The resulting concatenated constructions outperform low-rate LDPC code from the 5G standard and polar code. Furthermore, we demonstrate the performance of code with a special paritycheck matrix which consists of parity-check matrices of inner and outer codes from our proposed construction.