In this paper, we propose a joint diversity algorithm for error-rate minimization in multi-user spatial multiplexing (SM) systems with block diagonalization (BD)-precoding. The proposed algorithm adapts or selects the user set, transmit antenna subset, and the number of streams by an exhaustive search over the available resources. The proposed algorithm makes use of the multi-user diversity (MUD) and the spatial diversity gains as well as the array gain through selecting the best set. Exhaustive search, however, imposes a heavy burden in terms of computational complexity which exponentially increases with the size of the total number of users, streams, and transmit antennas. For complexity reduction, we propose two suboptimal algorithms which reduce the search space by first selecting the best user or by both selecting the best user and fixing the number of streams. Simulation results show that the proposed algorithms improve error probability over the conventional algorithm due to their diversity improvement and the signal-to-noise ratio (SNR) gains over the conventional algorithm. We also show that the suboptimal algorithms significantly reduce the computational complexity over exhaustive search with low-SNR loss.

This paper presents a novel threshold-based selection scheme to combine adaptive transmit antenna selection with an adaptive quadrature amplitude modulation (AQAM) for a spatial multiplexing (SM) multiple-input multiple-output (MIMO) system with linear receivers in practical uncorrelated and correlated channel conditions. The proposed scheme aims to maximize the average spectral efficiency (ASE) for a given bit error rate (BER) constraint and also to lower the hardware complexity. Our simulations are run on a general MIMO channel model, under the assumption that the channel state information (CSI) is known at the receiver and the adaptive control signaling can be perfectly fed back to the transmitter. We deploy the low rank-revealing QR (LRRQR) algorithm in transmit antenna subset selection. LRRQR is computationally less expensive than a singular value decomposition (SVD) based algorithm while the two algorithms achieve similar error rate performances. We show that both the conventional AQAM scheme (i.e., without adaptive transmit antenna selection) and the SM scheme perform poorly in a highly correlated channel environment. We demonstrate that our proposed scheme provides a well-behaved trade-off between the ASE and BER under various channel environments. The ASE (i.e., throughput) can be maximized with a proper choice of the channel quality threshold and AQAM mode switching threshold levels for a target BER.