Matching Theory for Backhaul Management in Small Cell Networks with mmWave Capabilities

TL;DR

This paper introduces a novel matching theory-based approach for managing spectral resources in small cell networks with mmWave capabilities, optimizing backhaul performance through a joint consideration of wireless channel and economic factors.

Contribution

It proposes a new model and distributed algorithm for resource allocation in heterogeneous small cell backhaul networks using carrier aggregation of mmWave and conventional bands.

Findings

Achieves up to 30% performance improvement over conventional methods.

Provides a stable and convergent solution for resource allocation.

Effectively manages spectral resources considering both channel and economic factors.

Abstract

Designing cost-effective and scalable backhaul solutions is one of the main challenges for emerging wireless small cell networks (SCNs). In this regard, millimeter wave (mmW) communication technologies have recently emerged as an attractive solution to realize the vision of a high-speed and reliable wireless small cell backhaul network (SCBN). In this paper, a novel approach is proposed for managing the spectral resources of a heterogeneous SCBN that can exploit simultaneously mmW and conventional frequency bands via carrier aggregation. In particular, a new SCBN model is proposed in which small cell base stations (SCBSs) equipped with broadband fiber backhaul allocate their frequency resources to SCBSs with wireless backhaul, by using aggregated bands. One unique feature of the studied model is that it jointly accounts for both wireless channel characteristics and economic factors during resource allocation. The problem is then formulated as a one-to-many matching game and a distributed algorithm is proposed to find a stable outcome of the game. The convergence of the algorithm is proven and the properties of the resulting matching are studied. Simulation results show that under the constraints of wireless backhauling, the proposed approach achieves substantial performance gains, reaching up to $30 \%$ compared to a conventional best-effort approach.