Optimized Rate-Adaptive Protograph-Based LDPC Codes for Source Coding With Side Information

Abstract

This paper considers the problem of source coding with side information at the decoder, also called Slepian-Wolf source coding scheme. In practical applications of this coding scheme, the statistical relation between the source and the side information can vary from one data transmission to another, and there is a need to adapt the coding rate depending on the current statistical relation. In this paper, we propose a novel rate-adaptive code construction based on LDPC codes for the Slepian-Wolf source coding scheme. The proposed code design method allows to optimize the code degree distributions at all the considered rates, while minimizing the amount of short cycles in the parity check matrices at all rates. Simulation results show that the proposed method greatly reduces the source coding rate compared to the standard LDPCA solution.

Energy optimization of quantized min-sum decoders for protograph-based LDPC codes

“…August 31, 2020 DRAFT In order to apply this method to our problem, we start from the optimization problem (14). As a criterion for protograph selection at each iteration, we consider the function g defined as…”

“…(L) N (ξ) and the average number of iterations L N (ξ) do not have explicit analytical expressions, although they can be evaluated numerically. Therefore, a common solution for protograph optimization [14] consists of adapting a genetic algorithm called Differential Evolution [9] and initially proposed for continuous optimization. However, while previous works [14] consider protograph optimization for performance only, the main difficulty in our case is to address the tradeoff between energy consumption and performance.…”

“…Therefore, a common solution for protograph optimization [14] consists of adapting a genetic algorithm called Differential Evolution [9] and initially proposed for continuous optimization. However, while previous works [14] consider protograph optimization for performance only, the main difficulty in our case is to address the tradeoff between energy consumption and performance. In particular, Differential Evolution is not well-suited for constrained optimization, as the algorithm should be initialized inside the feasible set, and in our case, the feasible set is the set of protographs that satisfy the constraint…”

“…This formulation shows that the two key criterion for energy optimization are the average VN degreed v and the FER performance at successive iterations. In the next section, we use the equivalent formulation (14) to propose an efficient optimization algorithm based on Differential Evolution.…”

A geometry-aware compression of 3D mesh texture with random access

“…as if the navigation was known by the offline encoder. In practice, we use LDPC codes [14] applied on the quantized and binarized coefficients of the graph-based transform.…”