Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto
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- URL: http://arxiv.org/abs/2102.09047v2
- Date: Wed, 24 Feb 2021 00:37:31 GMT
- Title: Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto
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- Authors: Zachary J. Grey and Susanna Mosleh and Jacob D. Rezac and Yao Ma and
Jason B. Coder and Andrew M. Dienstfrey
- Abstract summary: New wireless technologies like Long-Term Evolution License-Assisted Access (LTE-LAA) operate in shared and unlicensed bands.
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.
- Score: 3.379748084011544
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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