LLR Compression for BICM Systems Using Large Constellations

TL;DR

This paper introduces a method to compress log-likelihood ratios in BICM systems with large constellations, significantly reducing de-interleaver memory requirements with minimal complexity increase.

Contribution

It proposes a novel quantization and compression technique for LLRs, designed via a GMI-based approach, to cut down memory size in DVB-C2 systems.

Findings

Memory reduction up to 30% in DVB-C2 scenarios

Negligible increase in computational complexity

Effective LLR compression via GMI-based design

Abstract

Digital video broadcasting (DVB-C2) and other modern communication standards increase diversity by means of a symbol-level interleaver that spans over several codewords. De-interleaving at the receiver requires a large memory, which has a significant impact on the implementation cost. In this paper, we propose a technique that reduces the de-interleaver memory size. By quantizing log-likelihood ratios with bit-specific quantizers and compressing the quantized output, we can significantly reduce the memory size with a negligible increase in computational complexity. Both the quantizer and compressor are designed via a GMI-based maximization procedure. For a typical DVB-C2 scenario, numerical results show that the proposed solution enables a memory saving up to 30%.