On Optimal Scheduling for Joint Spatial Division and Multiplexing Approach in FDD Massive MIMO

TL;DR

This paper addresses the challenge of user scheduling in FDD Massive MIMO systems using joint spatial division and multiplexing, proposing a new clustering method and a polynomial-time sub-optimal scheduling algorithm that improves performance.

Contribution

It introduces a novel similarity measure and clustering procedure for user grouping, and formulates the optimal scheduling problem, proving its NP-hardness, then develops an effective sub-optimal scheduling scheme.

Findings

The proposed clustering improves user grouping accuracy.

The NP-hardness of the scheduling problem is established.

The new scheduling scheme outperforms existing methods in sum-rate and fairness.

Abstract

Massive MIMO is widely considered as a key enabler of the next generation 5G networks. With a large number of antennas at the Base Station, both spectral and energy efficiencies can be enhanced. Unfortunately, the downlink channel estimation overhead scales linearly with the number of antennas. This burden is easily mitigated in TDD systems by the use of the channel reciprocity property. However, this is unfeasible for FDD systems and the method of two-stage beamforming was therefore developed to reduce the amount of channel state information feedback. The performance of this scheme being highly dependent on the users grouping and scheduling mechanims, we introduce in this paper a new similarity measure coupled with a novel clustering procedure to achieve the appropriate users grouping. We also proceed to formulate the optimal users scheduling policy in JSDM and prove that it is NP-hard. This result is of paramount importance since it suggests that, unless P=NP, there are no polynomial time algorithms that solve the general scheduling problem to global optimality and the use of sub-optimal scheduling strategies is more realistic in practice. We therefore use graph theory to develop a sub-optimal users scheduling scheme that runs in polynomial time and outperforms the scheduling schemes previously introduced in the literature for JSDM in both sum-rate and throughput fairness.