LISS Algorithm with Modified Length Bias Term in Turbo Equalization

Abstract

For iterative detection/decoding turbo schemes List Sequential (LISS) detection is an effective technique which contrary to a posteriori probability (APP) equalization offers a much smaller complexity almost independent of the number of states. It uses a metric containing a priori and channel values, a metric length bias term for speeding up the tree-search, a soft extension of paths without increasing the stack size and soft weighting to obtain a soft-output. Using a length bias term calculated via an auxiliary stack has been shown to substantially narrow the tree search and thus reduce detection complexity. In this paper we propose a novel approach to determine an approximation of the bias term. It is based on the information available during the tree search in the main stack of the LISS detector. This approach further reduces the detection computational load without significant loss of performances.