A minimax robust decoding algorithm

In this correspondence we study the decoding problem in an uncertain noise environment. If the receiver knows the noise probability density function ( pdf) at each time slot or its a priori probability the standard Viterbi alg orithm (VA) or the a posteriori probability (APP) algorithm can achieve opt imal performance. However, if the actual noise distribution differs From th e noise model used to design the receiver, there can be significant perform ance degradation due to the model mismatch. The minimax concept is used to minimize the worst possible error performance over a family of possible cha nnel noise pdf's. We show that the optimal robust scheme is difficult to de rive; therefore, alternative, practically feasible, robust decoding schemes are presented and implemented on VA decoder and two-way APP decoder. Perfo rmance analysis and numerical results show our robust decoders have a perfo rmance advantage over standard decoders in uncertain noise channels, with n o or little computational overhead. Our robust decoding approach can also e xplain why for turbo decoding overestimating the noise variance gives bette r results than underestimating it.