Spreading sequence estimation algorithms based on ML detector in DSSS communication systems

Abstract

In this study, the authors address the spreading sequence estimation in direct-sequence spread-spectrum signals. At first, the maximum-likelihood (ML) estimator is derived. In order to alleviate the greater computational complexity of the ML estimator, an innovative algorithm based on the ML method is proposed. The authors' proposed algorithm uses an initial estimation with low complexity and low estimation accuracy as a result. In the second step, the estimation accuracy increases using the ML decision rule. They analyse their proposed algorithm and they derive an analytical approximation for the error probability of the proposed suboptimal algorithm. Simulation and analytical results show great performance and acceptable complexity of the proposed method.