Sequential decoding

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

On some new approaches to practical Slepian-Wolf compression inspired by channel coding

Abstract: We introduce three new innovations for compression using LDPCs for the Slepian-Wolf problem. The first is a general iterative Slepian-Wolf decoding algorithm that incorporates the graphical structure of all the encoders and operates in a 'turbo-like' fashion. The second innovation introduces source-splitting to enable low-complexity pipelined implementations of Slepian-Wolf decoding at rates besides corner points of the Slepian-Wolf region. This innovation can also be applied to single-source block coding for reduced decoder complexity. The third approach is a linear programming relaxation to maximum-likelihood sequence decoding that exhibits the ML-certificate property. This can be used for decoding a single binary block-compressed source as well as decoding at vertex points for the binary Slepian-Wolf problem. All three of these innovations were motivated by recent analogous results in the channel coding domain.

Multi-kernel construction of polar codes

Abstract: We propose a generalized construction for binary polar codes based on mixing multiple kernels of different sizes in order to construct polar codes of block lengths that are not only powers of integers. This results in a multi-kernel polar code with very good performance while the encoding complexity remains low and the decoding follows the same general structure as for the original Arikan polar codes. The construction provides numerous practical advantages as more code lengths can be achieved without puncturing or shortening. We observe numerically that the error-rate performance of our construction outperforms state-of-the-art constructions using puncturing methods.

Feedback and error protection strategies for wireless progressive video transmission

Abstract: Simple but meaningful models for a mobile radio channel are introduced and a channel-coding system based on high-memory rate-compatible punctured convolutional codes with an appropriate sequential decoding algorithm, the far-end error decoder (FEED), are presented. In combination with puncturing, we devise a method for unequal error protection (UEP) and error localization within a progressively coded source message without any additional error detection code. The FEED-based channel-coding system does not aim to minimize the bit or word error probability, but to delay the first error within a data frame as far as possible. This channel-coding scheme and the FEED algorithm can be used efficiently with automatic repeat request (ARQ). We present different ARQ strategies. For all forward error-correction (FEC) schemes, bounds are specified which allow the estimation of the performance and appropriate rate allocation. We briefly discuss an efficient fine granular scalable video compression scheme, the progressive texture video codec (PTVC). The proposed scheme generates an embedded bit-stream for each frame and allows reference frames to be adjusted. These source and channel-coding algorithms are used to design several video communication systems based on FEC and ARQ methods. The resulting systems are presented and compared. Performance estimations based on bounding techniques and optimized rate-allocation algorithms are derived and applied. Experimental results show the improvement potential of the proposed systems compared to standard schemes. Video communication over very low bit-rate mobile channels with varying channel conditions is thus made possible.

Low-Complexity Approaches to Slepian–Wolf Near-Lossless Distributed Data Compression

Abstract: This paper discusses the Slepian-Wolf problem of distributed near-lossless compression of correlated sources. We introduce practical new tools for communicating at all rates in the achievable region. The technique employs a simple "source-splitting" strategy that does not require common sources of randomness at the encoders and decoders. This approach allows for pipelined encoding and decoding so that the system operates with the complexity of a single user encoder and decoder. Moreover, when this splitting approach is used in conjunction with iterative decoding methods, it produces a significant simplification of the decoding process. We demonstrate this approach for synthetically generated data. Finally, we consider the Slepian-Wolf problem when linear codes are used as syndrome-formers and consider a linear programming relaxation to maximum-likelihood (ML) sequence decoding. We note that the fractional vertices of the relaxed polytope compete with the optimal solution in a manner analogous to that observed when the "min-sum" iterative decoding algorithm is applied. This relaxation exhibits the ML-certificate property: if an integral solution is found, it is the ML solution. For symmetric binary joint distributions, we show that selecting easily constructable "expander"-style low-density parity check codes (LDPCs) as syndrome-formers admits a positive error exponent and therefore provably good performance

500 Mb/s soft output Viterbi decoder

Abstract: Two 8-state, 7-bit soft output Viterbi decoders matched to an EPR4 channel and a rate-8/9 convolutional code are implemented in 0.18µm CMOS technology. Architectural transformation of the add-compare-select structures and modification of the register exchange allow a high throughput with small area overhead. The 4mm2chip has been verified to decode at 500Mb/s with 1.8V supply. These decoders are used with Turbo codes, which have been demonstrated to achieve information rates very close to the Shannon limit.