Sequential decoding

About: Sequential decoding is a research topic. Over the lifetime, 8667 publications have been published within this topic receiving 204271 citations.

A bit-serial approximate min-sum LDPC decoder and FPGA implementation

Abstract: We propose a bit-serial LDPC decoding scheme to reduce interconnect complexity in fully-parallel low-density parity-check decoders. Bit-serial decoding also facilitates efficient implementation of wordlength-programmable LDPC decoding which is essential for gear shift decoding. To simplify the implementation of bit-serial decoding we propose a new approximation to the check update function in the min-sum decoding algorithm. The new check update rule computes only the absolute minimum and applies a correction to outgoing messages if required. We present a 650-Mbps bit-serial (480, 355) RS-based LDPC decoder implemented on a single Altera Stratix EP1S80 FPGA device. To our knowledge, this is the fastest FPGA-based LDPC decoder reported in the literature.

Spread codes and spread decoding in network coding

Abstract: In this paper we introduce the class of spread codes for the use in random network coding. Spread codes are based on the construction of spreads in finite projective geometry. The major contribution of the paper is an efficient decoding algorithm of spread codes up to half the minimum distance.

Iterative source-channel decoder using extrinsic information from softbit-source decoding

Abstract: In digital mobile communications, efficient compression algorithms are needed to encode speech or audio signals. As the determined source parameters are highly sensitive to transmission errors, robust source and channel decoding schemes are required. This contribution deals with an iterative source-channel decoding approach where a simple channel decoder and a softbit-source decoder are concatenated. We mainly focus on softbit-source decoding which can be considered as an error concealment technique. This technique utilizes residual redundancy remaining after source coding. We derive a new formula that shows how the residual redundancy transforms into extrinsic information utilizable for iterative decoding. The derived formula opens several starting points for optimizations, e.g. it helps to find a robust index assignment. Furthermore, it allows the conclusion that softbit-source decoding is the limiting factor if applied to iterative decoding processes. Therefore, no significant gain will be obtainable by more than two iterations. This will be demonstrated by simulation.

Efficient High-Performance Decoding for Overloaded MIMO Antenna Systems

Abstract: The practical challenge of capacity-achieving forward error-correcting codes (e.g., space-time turbo codes) is overcoming the tremendous complexity associated by their optimal joint maximum-likelihood (ML) decoding. For this reason, iterative soft decoding has been studied to approach the optimal ML decoding performance at affordable complexity. In multiple-input multiple-output (MIMO) channels, a judicious decoding strategy consists of two stages: 1) estimate the soft bits using list version of sphere decoding or its variants, and 2) update the soft bits through iterative soft decoding. A promising MIMO decoder is required to produce reliable soft-bit estimates at the first stage before iterative soft decoding is performed. In this paper, we focus on the overloaded (or fat) MIMO antenna systems where the number of receive antennas is less than the number of signals multiplexed in the spatial domain. In this scenario, the original form of sphere decoding is inherently not applicable and our aim is to generalize sphere decoding geometrically to cope with overloaded detection. The so-called slab-sphere decoding (SSD) proposed guarantees to obtain exact-ML hard detection while reducing complexity greatly. With the list-version of SSD, (his paper proposes an efficient MIMO soft decoder, which can generate reliable soft-bit estimates at affordable complexity as inputs for iterative soft decoding for promising performance. A case study in the IEEE 802.16 settings is carried out for performance evaluation

Decoding color codes by projection onto surface codes

Abstract: We propose a general strategy to decode color codes, which is based on the projection of the error onto three surface codes. This provides a method to transform every decoding algorithm of surface codes into a decoding algorithm of color codes. Applying this idea to a family of hexagonal color codes, with the perfect matching decoding algorithm for the three corresponding surface codes, we find a phase error threshold of approximately $8.7%$. Finally, our approach enables us to establish a general lower bound on the error threshold of a family of color codes depending on the threshold of the three corresponding surface codes. These results are based on a chain complex interpretation of surface codes and color codes.