Abstract—This paper presents a simple yet effective method for designing nested families of LDPC codes. Rate compatible codes are essential for many communication applications, e.g. hybrid automatic repeat request (HARQ) systems, and their design is nontrivial due to the difficulty

Abstract—This paper produces a rate-compatible protograph LDPC code at 1k information blocklength with superior perfor-mance in both waterfall and error floor regions. The design of such codes has proved difficult in the past because the constraints imposed by structured design (protographs), rate-compatibility

Abstract — While rate-compatible punctured low-density parity-check (RCP-LDPC) codes offer high flexibility in terms of code rate at a relatively low cost in implementation complexity, they are reported to require more decoding iterations than unpunctured LDPC codes. In this paper 1 we consider

Strong rate-compatible codes is important to achieve high throughput in hybrid automatic repeat request with forward error correction (ARQ/FEC) systems in packet data transmission. This paper focuses on the construction of efficient rate-compatible low density parity check (RC-LDPC) codes over a

Abstract—Distributed arithmetic coding has been shown to be effective for Slepian-Wolf coding with side information. In this letter, we extend it to rate-compatible coding, which is useful in presence of a feedback channel between encoder and decoder. The performance loss with respect

independently so that it does not show that a family of rate-compatible codes can achieve that sequence of error probabilities. Using random code extension, this paper shows achievable frame error rates for rate-compatible channel codes with finite blocklengths. This paper also shows that when a threshold

Abstract not found

Abstract—This letter produces a family of rate-compatible protograph-based LDPC codes approaching the independent and uniformly distributed (i.u.d.) capacity of inter-symbol interfer-ence (ISI) channels. This problem is highly nontrivial due to the joint design of structured (protograph-based) LDPC

of probability distributions — are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, we develop general variational representations of the problems of computing

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