Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks

TL;DR

This paper introduces a backhaul-aware interference management method for small cell networks that improves uplink performance by enabling macrocell users to optimize their strategies through a distributed learning algorithm, considering heterogeneous backhaul constraints.

Contribution

It proposes a novel game-theoretic, distributed learning approach for interference management that accounts for heterogeneous backhaul limitations in small cell networks.

Findings

Significant throughput improvements over benchmark algorithms.

Effective delay reduction for macrocell user equipments.

Convergence and stability of the proposed learning algorithm.

Abstract

The design of distributed mechanisms for interference management is one of the key challenges in emerging wireless small cell networks whose backhaul is capacity limited and heterogeneous (wired, wireless and a mix thereof). In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed. The proposed approach enables macrocell user equipments (MUEs) to optimize their uplink performance, by exploiting the presence of neighboring small cell base stations. The problem is formulated as a noncooperative game among the MUEs that seek to optimize their delay-rate tradeoff, given the conditions of both the radio access network and the -- possibly heterogeneous -- backhaul. To solve this game, a novel, distributed learning algorithm is proposed using which the MUEs autonomously choose their optimal uplink transmission strategies, given a limited amount of available information. The convergence of the proposed algorithm is shown and its properties are studied. Simulation results show that, under various types of backhauls, the proposed approach yields significant performance gains, in terms of both average throughput and delay for the MUEs, when compared to existing benchmark algorithms.