A Low Complexity Signal-to-Total Variance Precoding Scheme for Downlink Multi-Stream MU-MIMO Systems

Abstract

Multi-stream multiuser multiple input multiple output (MS-MU-MIMO) downlink systems are an emerging topic in wireless communications as they can achieve very high data rates by spatially multiplexing independent data streams to multiple users. However, their performance is degraded due to multi-user interference in addition to noise in wireless channels. To overcome this problem, base station (BS) uses channel state information to apply precoding schemes. In this paper, we propose a novel linear variance-based approach that reduces the computational complexity compared to the regularized block diagonalization (RBD) and signal-to-leakage-and-noise ratio (SLNR) precoding schemes. The proposed scheme is based on maximization problem of signal to total variance ratio (STVR). It minimizes the variance of signal power leakage to other users while keeping the maximum energy for the signal directed towards the intended user. This problem is solved by simultaneous reduction of a generalized eigenvalue decomposition. The proposed solution requires low-order eigenvector decomposition to get the precoding vectors for all users, Simulation results and computational complexity analysis in terms of flops show that the performance of proposed STVR is comparable with classic linear precoding schemes while achieving significantly low computational overhead.