adaptive channel estimation based on soft information processing in broadband spatial multiplexing receivers

In this paper we present a novel approach in Multiple-Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) channel estimation technique based on a Decision Directed Recursive Least Squares (RLS) algorithm in which no pilot symbols need to be integrated in the data after a short initial preamble. The novelty and key concept of the proposed technique is the block-wise causal and anti-causal RLS processing that yields two independent processings of RLS along with the associated decisions. Due to the usage of low density parity check (LDPC) channel code, the receiver operates with soft information, which enables us to introduce a new modification of the Turbo principle as well as a simple information combining approach based on approximated aposteriori log-likelihood ratios (LLRs). Although the computational complexity is increased by both of our approaches, the latter is relatively less complex than the former. Simulation results show that these implementations outperform the simple RLS-DDCE algorithm and yield lower bit error rates (BER) and more accurate channel estimates.