Code-Aided ML Ambiguity Resolution

This paper deals with code-aided (CA) maximum-likelihood (ML) phase and timing ambiguity resolution. We propose a methodology based on the sum-product algorithm (SPA) to exactly solve this problem with a tractable complexity. In particular, we emphasize that the proposed ML ambiguity-resolution algorithm has a complexity which is at most equal to the complexity of recently-proposed powerful ML-like ambiguity-resolution methods. Finally, we compare through simulation results the ability of CA and conventional data-aided methods to resolve phase ambiguities.