IterativeMean Removal Superimposed Training for SISO and MIMO Channel Estimation

This contribution describes a novel iterative radio channel estimation algorithm based on Superimposed Training (ST) estimation technique. The proposed algorithm draws an analogy with the Data Dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receiver's end. The authors first demonstrate that this Mean Removal ST (MRST) applied to estimate a Single-Input Single-Output (SISO) wideband channel results in similar Bit Error Rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, they jointly use the MRST and Alamouti coding to obtain an estimate of the Multiple-Input Multiple-Output (MIMO) narrowband radio channel.