Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing
Zachary J. Grey, Susanna Mosleh, Jake Rezac, Yao Ma, Jason Coder, Andrew Dienstfrey
In order 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 networks' key performance indicators (KPIs) simultaneously via dimension reduction and multi-criteria optimization. These KPIs are network throughput as a function of medium access control protocols and physical layer parameters. We perform an exploratory analysis of network behavior by approximating active subspaces to identify low-dimensional structure of the optimization criteria, i.e., few linear combinations of parameters for simultaneously maximizing LTE-LAA throughput and Wi-Fi throughput. We take advantage of an aggregate low-dimensional subspace parametrized by approximated active subspaces of both throughputs to enable this multi-criteria optimization. The low-dimensional subspace approximations enable visualizations suggesting a predominantly convex set of KPIs over active coordinates leading to an analytic Pareto trace of near-optimal solutions.
, Mosleh, S.
, Rezac, J.
, Ma, Y.
, Coder, J.
and Dienstfrey, A.
Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing, Signal Processing for Communications Symposium, Montreal, CA, [online], https://doi.org/10.1109/ICC42927.2021.9500533
(Accessed August 7, 2022)