A Low-Complexity Block Diagonalization Algorithm for MU-MIMO Two-Way Relay Systems with Complex Lattice Reduction

Abstract

We design a scheme of precoding matrices for two-way multiuser relay systems, where a multiantenna relay station (RS) operating in an amplify-and-forward model simultaneously receives information from all multiple-antenna users. Considering the feasibility in mathematical analysis, users are distributed in two symmetrical groups. To reduce the complexity of proposed precoding scheme, we employ the QR decomposition and complex lattice reduction (CLR) transform to replace the two times singular value decomposition (SVD) of conventional BD-based precoding algorithm by introducing a combined channel inversion to eliminate the multiple users interference (MUI). Simulation and performance analysis demonstrate that the proposed LR-MMSE algorithm has not only a better bit error rate (BER) performance, a higher sum-rate, and simple architecture, but also 89.8% and 35.5% less complexity compared to BD-and MMSE-based scheme.

Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms

“…Therefore, using the concept of matrix inversion of [7][8][9], it does not exhibit a significant impact on the computational cost of high definition adaptive filtering [10]. In [11], Xiao et al introduce an LR-MMSE algorithm based on QR decomposition and complex lattice reduction (CLR) which provides 35.5% lesser computational complexity than MMSE based scheme [12]; however, 35.5% lesser computational cost still can not meet the demands of high data rate communications. A low complexity in reduced rank linear interference suppression is proposed in [13,14] which is based on the use of polynomial expansion (PE).…”