Joint transmit antenna selection and user scheduling for Massive MIMO systems

Abstract

It is largely accepted that the innovative technology of large-scale multiantenna systems (named Massive multiple input multiple output (MIMO) systems) will very probably be deployed in the fifth generation of mobile cellular networks. In order to render this technology feasible and efficient, many challenges have to be investigated before. In this paper, we consider the problem of antenna selection and user scheduling in Massive MIMO systems. Our objective is to maximize the sum of broadcasting data rates achieved by all the mobile users in one cell served by a massive MIMO transmitter. The optimal solution of this problem can be obtained through a highly complex exhaustive brute force search (BFS) over all possible combinations of antennas and users. This BFS solution cannot be implemented in practice even for small size systems because of its high computational complexity. Therefore, in this paper, we propose an algorithm that efficiently solves the problem of joint antenna selection and user scheduling. The proposed algorithm aims to maximize the achievable sum-rate and to benefit from both the spatial selectivity gain and multi-user diversity gain offered by the antenna selection and user scheduling, respectively. Compared with the optimal solution obtained by the highly complex BFS, the conducted performance evaluation and complexity analysis show that the proposed algorithm is able to achieve near-optimal performance with low computational complexity.

Massive MIMO Optimization With Compatible Sets

“…Further, the analysis relies on assumptions that we do not require in our work, namely that the ratio of antennas to users is kept constant, and that users are scheduled in co-located groups. Joint antenna and user selection has also been studied for massive MIMO, however using only brute-force search [32], which has very high computation complexity and is thus impractical for all but very small problem instances, and a greedy algorithm [33], which is more efficient but provides only suboptimal solutions in general. Finally, user grouping and scheduling has also been studied in frequency-division duplex (FDD) massive MIMO systems [34], however users were placed in pre-beamforming groups, and only users in the same group could be scheduled together.…”

On the Throughput of Large-but-Finite MIMO Networks Using Schedulers

“…Considering no interference cancellation at the receivers, the achievable rate in each time slot is given by (7) with G = |H| 2 . Here, we consider two cases with N ≥ M and N < M and find the expected achievable rate as given in (19) and (20), respectively.…”

Feedback Energy Reduction in Massive MIMO Systems

“…(12) Remember that w n ij is the cost of selecting arc (i, j) to be in the n th position in the path. Each w n ij is function of the channel gain, the distance between user i and user j and the number of coefficients transmitted from i to j which depends on the position of user i.…”

“…A tight approximation of the mutual information distribution is formulated. A novel scheme of joint AS and user scheduling is proposed in [12]. The basic idea is to successively eliminate both undesired transmit antennas and users which yield a minimum sum rate contribution.…”