Design and Evaluation of Information Bottleneck LDPC Decoders for Software Defined Radios

Abstract

The Information Bottleneck method allows to construct information-optimum message passing decoders for low-density parity-check codes. In such decoders lookup tables replace the classical node operations of the variable and the check nodes. These lookup tables are designed using the Information Bottleneck principle of maximizing the relevant information. Unlike state-of-the-art decoders which use real valued log-likelihood ratios for decoding, the considered decoders do not process any real values, but only quantization indices. Nevertheless, they have performance extremely close to belief propagation decoding. Since hardware representation of unsigned integers is efficient and lookup table implementations have low complexity, it is reasonable to assume that the designed decoders offer advantages over their conventional counterparts in practice. In this paper, we evaluate, quantify and discuss these advantages in a practical experiment. Our focus lies on a software defined radio application, where the channel decoder is implemented on a digital signal processor. We present several implementations of the considered decoders and compare them with state-of-the-art decoders. Our results show considerable gains of the Information Bottleneck decoders in terms of bit error rate performance and net decoding throughput.