Soft-decision decoding of convolutional codes with square-law detectors

In this study, the authors investigate the performance of soft-decision decoding of convolutional codes in receivers that employ square-law detection. Traditionally, soft-decision decoding has been considered only in coherent or differentially-coherent receivers. Over the past few years, the emergence of ultra-wideband (UWB) communication has brought energy detectors to prominence. In this study, the authors derive low-complexity approximations for the log-likelihood ratio (LLR) with a class of square-law detectors in UWB radios. The authors then show that performance improvements, similar to those achievable in coherent detectors, can be obtained even with energy detectors when soft-decisions are employed in a maximum-likelihood decoding algorithm. The authors also investigate the complexity and accuracy of the proposed approximations when the LLR is computed using fixed point arithmetic. An expression for the bit error probability with soft-decision decoding is derived. Several simulation results, including the error rate performance of hard- and soft-decision decoding schemes with the exact and approximate LLR values, are presented.