Parallel QRD-M encoder for multi-user MIMO systems

Abstract

International audienceIn the context of multi-user precoding, the idea behind vector perturbation (VP) lies in adding an integer vector to the data vector such that the overall transmit power is reduced, where the performance at the users end is consequently improved. In the literature, several techniques have been proposed to nd a quasi-optimum perturbing vector, where this process was represented as an integer lattice search problem. In this paper, we propose a parallel QRD-M encoder (PQRDME) that, besides attaining a quasi-optimum diversity order, leads to tremendous reduction in the latency of the vector perturbation stage. Based on the set grouping, the proposed encoder transforms the full tree-search of the conventional QRDME into partial trees that can be pipelined to increase the encoding throughput. We evaluate the proposed algorithm under several scenarios with both perfect channel state information (PCSI) and imperfect CSI (ICSI) at the transmitter side, where simulation results show robust performance when compared to the optimum encoder

A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

“…As for lattice basis reduction precoding, the number of iterations required to orthogonalize the lattice basis can be as high as theoretically infinite, depending on the conditioning of the channel matrix (i.e., lattice basis). We are proposing several low and fixed computational complexity algorithms that aim to find the best t vector such that the total transmit power is reduced [14][15][16][17]. That is:…”

“…The size of the symmetric set Z from which the elements of t are drawn is decided using simulations (see figures in [14][15][16]). To solve (6) successively, let the transpose of H be factorized into the product of the unitary matrix Q and upper triangular matrix R. Then the search problem in (6) is expanded to:…”

“…To reduce the complexity of the users' receivers, we took into consideration that the channel effect is equalized for at the transmitter side by means of precoding. Moreover, we employed the already explained VP techniques [16,17].…”

“…Therefore, the fixed-complexity sphere encoder (FSE) is introduced, where both its low complexity and high parallelization capabilities are outlined [14]. The parallel QRDM-E (PQRDME) is then introduced, for which the goal is to reduce the computational complexity of the conventional QRDM-E [15,16].…”