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IEEE International Symposium on Information Theory (ISIT)

Optimized quantization and scaling of layered LDPC scaled min-sum decoder

Ahmed A. Emran, Maha Mohamed Elsabrouty, Osamu Muta, Hiroshi Furukawa

Published Jun 1, 2015• 0 citations
Publisher

Abstract

In this paper, we apply an efficient scaling strategy on layered scaled min-sum LDPC decoder. In addition, we propose a joint optimization strategy for the quantization and scaling parameters of layered scaled min-sum LDPC decoder. The study of our optimization results, for DVB-S2 LDPC codes with different constellation sizes and code rates, shows that each constellation size code rate pair has different optimal scaling and quantization parameters. In order to maximize the achievable performance, we propose using the different scaling and quantization parameters for each constellation size code rate pair. The simulation results show the performance improvement of separately using optimal scaling parameters or optimal quantization parameters, and the overall performance enhancement of using both optimal scaling and quantization parameters.

DOI
10.1109/isit.2015.7282940

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Min-Sum decoding is widely used for decoding LDPC codes in many modern digital video broadcasting decoding due to its relative low complexity and robustness against quantization error. However, the suboptimal performance of the Min-Sum affects the integrated performance of wireless receivers. In this paper, we present the idea of adapting the scaling factor of the Min-Sum decoder with iterations through a simple approximation. For the ease of implementation the scaling factor can be changed in a staircase fashion. The stair step is designed to optimize the decoder performance and the required storage for its different values. The variable scaling factor proposed algorithm produces a nontrivial improvement of the performance of the Min-Sum decoding as verified by simulation results.
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Min sum algorithm (MSA) is widely used for decoding Low Density Parity Check (LDPC) codes in many modern Digital Video Broadcasting (DVB) as a simplification for Sum Product Algorithm (SPA) which greatly reduce the implementation complexity. This paper presents a design of modified variable scaling min sum LDPC decoding algorithm for DVB-S2/S2X/T2. The proposed design is a combination between the variable Normalized Min Sum Algorithm (NMSA) and the Offset Min Sum Algorithm (OMSA) which provides a trade-off between decoding complexity and error correcting performance. It improves the use of the Min Sum Algorithm (MSA) by 39%. A proposal for how to implement the proposed algorithm using FPGA is also introduced. The structure used is partially parallel layered decoder architecture.
In this paper, we present adaptive quantization schemes in the normalized min-sum decoding algorithm considering scaling effects to improve the performance of irregular low-density parity-check (LDPC) decoder for WirelessMAN (IEEE 802.16e) applications. We discuss the finite precision effects on the performance of irregular LDPC codes and develop optimal finite word lengths of variables over an SNR. For floating point simulation, it is known that in the normalized min-sum or offset min-sum algorithms the performance of a min-sum based decoder is not sensitive to scaling in the log-likelihood ratio (LLR) values. However, when considering the finite precision for hardware implementation, the scaling affects the dynamic range of the LLR values. The proposed adaptive quantization approach provides the optimal performance in selecting suitable input LLR values to the decoder as far as the tradeoffs between error performance and hardware complexity are concerned.