Joint ML channel estimation and data detection for STBC via novel sphere decoding algorithms

Space-time block codes (STBC) are very effective and simple schemes to utilize the diversity gain in multiple-input multiple-output (MIMO) wireless systems. In previous research on STBC data detection, channel state information is usually assumed to be known at the receiver side, which enables simple maximum-likelihood (ML) decoding at the receiver. We consider the problem of joint ML channel estimation and data detection for STBC systems when the channel state information is not available at the receiver. It is shown that the joint ML channel estimation and data detection for complex STBC system is an integer least-square optimization problem for constant-modulus modulation. We propose novel sphere decoders which provide a low-complexity exact optimal solution to this optimization problem. The proposed sphere decoder visits the minimum number of tree nodes in finding the ML solution. In addition, by modifying the novel sphere decoder, we provide efficient exact ML joint channel estimation and data detection for STBC wireless systems employing non-constant modulus modulation schemes. Considerable performance gain and complexity reduction are achieved with these methods.