This paper considers protograph-based LDPC codes, and proposes an optimization method to select protographs that minimize the energy consumption of quantized Min-Sum decoders. This method first estimates the average number of iterations required by the decoder, and includes this estimate into two high level models that evaluate the decoder energy consumption. The optimization problem is then formulated as minimizing the energy consumption of the decoder while satisfying a performance criterion on the Frame Error Rate. Finally, an optimization algorithm based on differential evolution is introduced. Protograph optimized for energy consumption shows a gain in energy of approximately 15% compared to a baseline protograph optimized for performance only. I. INTRODUCTION Reducing the energy consumption of telecommunication systems may allow to improve the communication capabilities of systems with limited resources. This energy consumption comes for a large part from the transmission power of the emitter, but the processing power at the receiver is also non-negligible for short-length communications [1]. In addition, error-correction decoders are known to use a large part of this processing power. Therefore, the objective of this paper is to reduce the energy consumption of the error-correction part. It considers Low Density Parity Check (LDPC) codes as a particular family of error-correction codes which were retained in the 5G standardization process. The problem of reducing the energy consumption of LDPC decoders has received increased attention recently. In [2], the energy consumption of hard-decision LDPC decoders is estimated from the number of computation operations realized in the decoder, and from the average length
This paper describes a practical Slepian-Wolf source coding scheme based on Low Density Parity Check (LDPC) codes. It considers the realistic setup where the parameters of the statistical model between the source and the side information are unknown. A novel Self-Corrected Belief-Propagation (SC-BP) algorithm is proposed in order to make the coding scheme robust to incorrect model parameters by introducing some memory inside the LDPC decoder. A Two Dimensional Density Evolution (2D-DE) analysis is then developed to predict the theoretical performance of the SC-BP decoder. Both the 2D-DE analysis and Monte-Carlo simulations confirm the robustness of the SC-BP decoder. The proposed solution allows for an important complexity reduction and shows a performance very close to existing methods which jointly estimate the model parameters and the source sequence.
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