Multi-point Coordination in Massive MIMO Systems with Sectorized Antennas

TL;DR

This paper explores advanced multi-point coordination and sectorization in massive MIMO systems, deriving performance bounds and proposing power control strategies to significantly enhance user throughput and mitigate pilot contamination effects.

Contribution

It introduces a comprehensive analysis of sectorized antennas and multi-point coordination in massive MIMO, providing closed-form rate bounds and optimized power control methods.

Findings

Sectorization reduces pilot contamination effects.

Coordination and sectorization improve per-user throughput by over 2.6 times.

Power control strategies enhance fairness and network performance.

Abstract

Non-cooperative cellular massive MIMO, combined with power control, is known to lead to significant improvements in per-user throughput compared with conventional LTE technology. In this paper, we investigate further refinements to massive MIMO, first, in the form of three-fold sectorization, and second, coordinated multi-point operation (with and without sectorization), in which the three base stations cooperate in the joint service of their users. For these scenarios, we analyze the downlink performance for both maximum-ratio and zero-forcing precoding and derive closed-form lower-bound expressions on the achievable rate of the users. These expressions are then used to formulate power optimization problems with two throughput fairness criteria: i) network-wide max-min fairness, and ii) per-cell max-min fairness. Furthermore, we provide centralized and decentralized power control strategies to optimize the transmit powers in the network. We demonstrate that employing sectorized antenna elements mitigates the detrimental effects of pilot contamination by rejecting a portion of interfering pilots in the spatial domain during channel estimation phase. Simulation results with practical sectorized antennas reveal that sectorization and multi-point coordination combined with sectorization lead to more than 1.7x and 2.6x improvements in the 95%-likely per-user throughput, respectively.