Adaptive Recursive Spatial Multiplexing (RSM) in interference-limited scenarios

Recursive Spatial Multiplexing (RSM) is a closed loop multiple-input multiple-output (MIMO) structure for achieving the capacity offered by MIMO channels with a low-complexity detector. We investigate how to make RSM able to deal with different interference scenarios. The interference at the receiver side is considered as a vector-valued stochastic process characterized by a covariance matrix which is to be estimated and used subsequently for defining the retransmission subspace identifier to be fed back to the transmitter. We consider both the sample covariance matrix (SCM) estimator and an empirical Bayesian (EB) scheme as well as different probability distribution functions and correlation properties of the interference vectors. It turns out that the proposed RSM modification substantially improves the bit-error rate performance in the presence of interference where EB and SCM perform comparably due to the conditions for the covariance matrix estimation in RSM with limited frame length. Moreover, adaptive RSM leads to a performance being independent of the correlation coefficient of the interference vector.