An Implementation of List Successive Cancellation Decoder with Large List Size for Polar Codes

Polar codes are the first class of forward error correction (FEC) codes with a provably capacity-achieving capability. Using list successive cancellation decoding (LSCD) with a large list size, the error correction performance of polar codes exceeds other well-known FEC codes. However, the hardware complexity of LSCD rapidly increases with the list size, which incurs high usage of the resources on the field programmable gate array (FPGA) and significantly impedes the practical deployment of polar codes. To alleviate the high complexity, in this paper, two low-complexity decoding schemes and the corresponding architectures for LSCD targeting FPGA implementation are proposed. The architecture is implemented in an Altera Stratix V FPGA. Measurement results show that, even with a list size of 32, the architecture is able to decode a codeword of 4096-bit polar code within 150 us, achieving a throughput of 27MbpsComment: 4 pages, 4 figures, 4 tables, Published in 27th International Conference on Field Programmable Logic and Applications (FPL), 201

Fast List Decoding of High-Rate Polar Codes

Due to the ability to provide superior error-correction performance, the successive cancellation list (SCL) algorithm is widely regarded as one of the most promising decoding algorithms for polar codes with short-to-moderate code lengths. However, the application of SCL decoding in low-latency communication scenarios is limited due to its sequential nature. To reduce the decoding latency, developing tailored fast and efficient list decoding algorithms of specific polar substituent codes (special nodes) is a promising solution. Recently, fast list decoding algorithms are proposed by considering special nodes with low code rates. Aiming to further speedup the SCL decoding, this paper presents fast list decoding algorithms for two types of high-rate special nodes, namely single-parity-check (SPC) nodes and sequence rate one or single-parity-check (SR1/SPC) nodes. In particular, we develop two classes of fast list decoding algorithms for these nodes, where the first class uses a sequential decoding procedure to yield decoding latency that is linear with the list size, and the second further parallelizes the decoding process by pre-determining the redundant candidate paths offline. Simulation results show that the proposed list decoding algorithms are able to achieve up to 70.7\% lower decoding latency than state-of-the-art fast SCL decoders, while exhibiting the same error-correction performance.Comment: 13 pages, 8 figure

High Performance Decoder Architectures for Error Correction Codes

Due to the rapid development of the information industry, modern communication and storage systems require much higher data rates and reliability to server various demanding applications. However, these systems suffer from noises from the practical channels. Various error correction codes (ECCs), such as Reed-Solomon (RS) codes, convolutional codes, turbo codes, Low-Density Parity-Check (LDPC) codes and so on, have been adopted in lots of current standards. With the increasing data rate, the research of more advanced ECCs and the corresponding efficient decoders will never stop.Binary LDPC codes have been adopted in lots of modern communication and storage applications due their superior error performance and efficient hardware decoder implementations. Non-binary LDPC (NB-LDPC) codes are an important extension of traditional binary LDPC codes. Compared with its binary counterpart, NB-LDPC codes show better error performance under short to moderate block lengths and higher order modulations. Moreover, NB-LDPC codes have lower error floor than binary LDPC codes. In spite of the excellent error performance, it is hard for current communication and storage systems to adopt NB-LDPC codes due to complex decoding algorithms and decoder architectures. In terms of hardware implementation, current NB-LDPC decoders need much larger area and achieve much lower data throughput.Besides the recently proposed NB-LDPC codes, polar codes, discovered by Ar{\i}kan, appear as a very promising candidate for future communication and storage systems. Polar codes are considered as a major breakthrough in recent coding theory society. Polar codes are proved to be capacity achieving codes over binary input symmetric memoryless channels. Besides, polar codes can be decoded by the successive cancelation (SC) algorithm with of complexity of $\mathcal{O}(N\log_2 N)$, where $N$ is the block length. The main sticking point of polar codes to date is that their error performance under short to moderate block lengths is inferior compared with LDPC codes or turbo codes. The list decoding technique can be used to improve the error performance of SC algorithms at the cost higher computational and memory complexities. Besides, the hardware implementation of current SC based decoders suffer from long decoding latency which is unsuitable for modern high speed communications.ECCs also find their applications in improving the reliability of network coding. Random linear network coding is an efficient technique for disseminating information in networks, but it is highly susceptible to errors. K\ {o}tter-Kschischang (KK) codes and Mahdavifar-Vardy (MV) codes are two important families of subspace codes that provide error control in noncoherent random linear network coding. List decoding has been used to decode MV codes beyond half distance. Existing hardware implementations of the rank metric decoder for KK codes suffer from limited throughput, long latency and high area complexity. The interpolation-based list decoding algorithm for MV codes still has high computational complexity, and its feasibility for hardware implementations has not been investigated.In this exam, we present efficient decoding algorithms and hardware decoder architectures for NB-LDPC codes, polar codes, KK and MV codes. For NB-LDPC codes, an efficient shuffled decoder architecture is presented to reduce the number of average iterations and improve the throughput. Besides, a fully parallel decoder architecture for NB-LDPC codes with short or moderate block lengths is also presented. Our fully parallel decoder architecture achieves much higher throughput and area efficiency compared with the state-of-art NB-LDPC decoders. For polar codes, a memory efficient list decoder architecture is first presented. Based on our reduced latency list decoding algorithm for polar codes, a high throughput list decoder architecture is also presented. At last, we present efficient decoder architectures for both KK and MV codes

Quantized Guessing Random Additive Noise Decoding

We introduce a soft-detection variant of Guessing Random Additive Noise Decoding (GRAND) called Quantized GRAND (QGRAND) that can efficiently decode any moderate redundancy block-code of any length in an algorithm that is suitable for highly parallelized implementation in hardware. QGRAND can avail of any level of quantized soft information, is established to be almost capacity achieving, and is shown to provide near maximum likelihood decoding performance when provided with five or more bits of soft information per received bit

VLSI Architecture for Polar Codes Using Fast Fourier Transform-Like Design

Polar code is a novel and high-performance communication algorithm with the ability to theoretically achieving the Shannon limit, which has attracted increasing attention recently due to its low encoding and decoding complexity. Hardware optimization further reduces the cost and achieves better timing performance enabling real-time applications on resource-constrained devices. This thesis presents an area-efficient architecture for a successive cancellation (SC) polar decoder. Our design applies high-level transformations to reduce the number of Processing Elements (PEs), i.e., only log2 N pre-computed PEs are required in our architecture for an N-bit code. We also propose a customized loop-based shifting register to reduce the consumption of the delay elements further. Our experimental results demonstrate that our architecture reduces 98.90% and 93.38% in the area and area-time product, respectively, compared to prior works