ADMM LP Decoding of Non-Binary LDPC Codes in <inline-formula> <tex-math notation="LaTeX">$ \mathbb {F}_{2^{m}}$ </tex-math></inline-formula>


In this paper, we develop efficient decoders for non-binary low-density parity-check codes using the alternating direction method of multipliers (ADMM). We apply ADMM to two decoding problems. The first problem is linear programming (LP) decoding. In order to develop an efficient algorithm, we focus on non-binary codes in fields of characteristic two. This allows us to transform each constraint in F<sub>2</sub>m to a set of constraints in F<sub>2</sub> that has a factor graph representation. Applying ADMM to the LP decoding problem results in two types of non-trivial sub-routines. The first type requires us to solve an unconstrained quadratic program. We solve this problem efficiently by leveraging new results obtained from studying the above factor graphs. The second type requires Euclidean projections onto polytopes that are studied in the literature. Such projections can be solved efficiently using off-the-shelf techniques, which scale linearly in the dimension of the vector to project. ADMM LP decoding scales linearly with block length, linearly with check degree, and quadratically with field size. The second problem we consider is a penalized LP decoding problem. This problem is obtained by incorporating a penalty term into the LP decoding objective. The purpose of the penalty term is to make non-integer solutions (pseudocodewords) more expensive and hence to improve decoding performance. The ADMM algorithm for the penalized LP problem requires Euclidean projection onto a polytope formed by embedding the constraints specified by the non-binary single parity-check code, which can be solved by applying the ADMM technique to the resulting quadratic program. Empirically, this decoder achieves a much reduced error rate than LP decoding at low signal-to-noise ratios.

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@article{Liu2016ADMMLD, title={ADMM LP Decoding of Non-Binary LDPC Codes in \$ \mathbb \{F\}_\{2^\{m\}\}\$ }, author={Xishuo Liu and Stark C. Draper}, journal={IEEE Transactions on Information Theory}, year={2016}, volume={62}, pages={2985-3010} }