Semiblind space–time decoding in wireless communications: a maximum likelihood approach

Abstract

It has been recently shown that deploying multiple transmitting and receiving antennae can substantially improve the capacity of multipath wireless channels if the rich time-scattering is properly exploited. Space–time coding (STC) is a novel proposal that combines channel coding techniques suitable for multiple transmitting elements with signal processing algorithms that exploit the spatial and temporal diversity at the receiver. In this paper, we focus on the signal processing perspective and propose a novel space–time semiblind decoding scheme that performs maximum likelihood (ML) based channel estimation and data detection. The multiple input multiple output (MIMO) time-scattering channel is estimated using a block iterative expectation-maximization (EM) algorithm that fully exploits the statistical features of the transmitted signal together with the knowledge of a small number of transmitted symbols, hence the term semiblind. Data detection is efficiently carried out using the Viterbi algorithm. In order to reduce the computational load of the receiver, a modification of the EM algorithm with a potentially lower complexity is also suggested. Computer simulations show that the proposed semiblind decoder clearly outperforms conventional receivers that estimate the channel parameters exclusively from the a priori known transmitted symbols.