Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing

TL;DR

This paper introduces a subspace-based approach to optimize spectrum sharing between LTE-LAA and Wi-Fi in unlicensed bands, enhancing coexistence by identifying low-dimensional structures in performance trade-offs.

Contribution

It proposes a novel method using active subspaces for dimension reduction to improve multi-criteria optimization of coexistence KPIs in unlicensed spectrum sharing.

Findings

Identified low-dimensional structures in throughput optimization.

Developed an analytic Pareto trace of near-optimal solutions.

Enhanced visualization of coexistence performance trade-offs.

Abstract

To meet the ever-growing demands of data throughput for forthcoming and deployed wireless networks, new wireless technologies like Long-Term Evolution License-Assisted Access (LTE-LAA) operate in shared and unlicensed bands. However, the LAA network must co-exist with incumbent IEEE 802.11 Wi-Fi systems. We consider a coexistence scenario where multiple LAA and Wi-Fi links share an unlicensed band. We aim to improve this coexistence by maximizing the key performance indicators (KPIs) of these networks simultaneously via dimension reduction and multi-criteria optimization. These KPIs are network throughputs as a function of medium access control protocols and physical layer parameters. We perform an exploratory analysis of coexistence behavior by approximating active subspaces to identify low-dimensional structure in the optimization criteria, i.e., few linear combinations of parameters for simultaneously maximizing KPIs. We leverage an aggregate low-dimensional subspace parametrized by approximated active subspaces of throughputs to facilitate multi-criteria optimization. The low-dimensional subspace approximations inform visualizations revealing convex KPIs over mixed active coordinates leading to an analytic Pareto trace of near-optimal solutions.