New adaptive iterative turbo decoding with a training sequence over AWGN and fading channels

Abstract

We present a new adaptive approach for iterative turbo decoding using a pseudorandom training sequence with the distribution patterns known by both the transmitter and receiver, which can be adaptively sent to the receiver whenever the channel status changes. The branch detector computes the conditional distributions of extrinsic values produced by both arms at each iterative decoding with the aid of the training sequence. These updated probabilities are obtained instead of the traditional Gaussian law for further iterative decoding of information bits over additive white Gaussian noise and various fading channels. Such an adaptive decoding has considerable gains, investigated by the numerical simulation, over the traditional approach under the same conditions.