A low complexity user selection algorithm for downlink multi-user MIMO systems

Abstract

Block digitalization (BD) method is well-known to be a suboptimal precoding technique for downlink multiuser multiple-input/multiple-output (MU-MIMO) systems, which perfectly eliminates inter-user interference. In downlink MU-MIMO systems with a large number of users, a user set supported by a base station (BS) may be selected to maximize the total throughput. The optimal user selection algorithm, which requires exhaustive search, is prohibitive due to its high computational complexity in real systems. In this thesis, we propose a user selection algorithm with low complexity, where the product of eigenvalues of effective channels is utilized as a selection metric. For the product of eigenvalues of effective channel matrix at each iteration, we adopt the concept of principal angles between subspaces. Moreover, we extend the proposed algorithm to limited feedback systems. Through computational complexity analysis, we show that the proposed algorithm has low complexity with a little loss in throughput. Contrary to conventional low complex user selection algorithms, the proposed algorithm can be easily applied to proportional fair (PF) scheduling because the selection metric indicates data rate in high SNR regime. Simulation results show that the proposed algorithm achieves almost the same the system throughput of capacity-based algorithm in high SNR regime with considerable reduction in complexity.