Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach

TL;DR

This paper presents a virtual layer-based approach for interference mitigation in wireless networks, enabling autonomous power control and scheduling to improve network utility with both centralized and distributed algorithms.

Contribution

It introduces a novel virtual layer framework for interference management, along with distributed algorithms and a centralized benchmark for optimality, applicable to SDMA wireless networks.

Findings

Distributed algorithms outperform strict power control by leveraging opportunistic scheduling.

Average power constraints yield higher gains than strict power limits.

Centralized algorithm converges quickly and serves as a performance benchmark.

Abstract

Interference mitigation techniques are essential for improving the performance of interference limited wireless networks. In this paper, we introduce novel interference mitigation schemes for wireless cellular networks with space division multiple access (SDMA). The schemes are based on a virtual layer that captures and simplifies the complicated interference situation in the network and that is used for power control. We show how optimization in this virtual layer generates gradually adapting power control settings that lead to autonomous interference minimization. Thereby, the granularity of control ranges from controlling frequency sub-band power via controlling the power on a per-beam basis, to a granularity of only enforcing average power constraints per beam. In conjunction with suitable short-term scheduling, our algorithms gradually steer the network towards a higher utility. We use extensive system-level simulations to compare three distributed algorithms and evaluate their applicability for different user mobility assumptions. In particular, it turns out that larger gains can be achieved by imposing average power constraints and allowing opportunistic scheduling instantaneously, rather than controlling the power in a strict way. Furthermore, we introduce a centralized algorithm, which directly solves the underlying optimization and shows fast convergence, as a performance benchmark for the distributed solutions. Moreover, we investigate the deviation from global optimality by comparing to a branch-and-bound-based solution.