State Metric Compression Techniques for Turbo Decoder Architectures

摘要

This papers proposes to compress state metrics in turbo decoder architectures to reduce the decoder area. Two techniques are proposed: the first is based on non-uniform quantization and the second on the Walsh–Hadamard transform followed by non-uniform quantization. The non-uniform quantization technique reduces state metric memory area of about 50% compared with architectures where state metric compression is not performed, at the expense of slightly increasing the error correcting performance floor. On the other hand, the Walsh–Hadamard transform based solution offers a good tradeoff between performance loss and memory complexity reduction, which reaches in the best case 20% of gain with respect to other approaches. Both solutions show lower power consumption than architectures previously proposed to compress state metrics.