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.
Highlights
Two-way relaying (TWR) cooperation has attracted considerable attention due to the high spectral efficiency
Considering a specification of antenna with N = 8, NR = 10, and Nl,k = 2, six users are divided into two symmetrical groups
When the signal to noise ratios (SNRs) is 30 dB, the proposed algorithm has a 3.208 bit/s/Hz gain compared to the Block diagonalization (BD) algorithm
Summary
Two-way relaying (TWR) cooperation has attracted considerable attention due to the high spectral efficiency. When we consider two-way relaying multiuser MIMO (TWR MU-MIMO) systems, the MUI becomes more complicated, since there are two groups of users mixing in the received signals. A systematic scheme is proposed in [7], which imposes a power constraint at the relay node and obtains an approximate result by fixing the transmitting and receiving matrices at the user nodes firstly. In [19], a low complexity precoding algorithm is proposed to reduce the condition number of the effective number by introducing a complex lattice reduction (CLR) transform in one-way relay MU-MIMO systems. We focus on AF TWR MU-MIMO systems and strive to find a lower complexity and better performance precoding scheme. Im×n is a m-by-n identity matrix; 0m is a m-dimensional zero matrix
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