An optimization approach to the economics of efficient multi-tiered spectrum sharing

Authors
Saha, Gourav
ORCID
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Other Contributors
Abouzeid, Alhussein A.
Kar, Koushik
Tajer, Ali
Mitchell, John E.
Berry, Randall
Issue Date
2020-08
Keywords
Computer Systems engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Chapter 4 of our thesis is motivated by how the 3.5 GHz band for 3-TSF is partitioned into channels and how those channels are further partitioned for licensed and unlicensed use. Motivated by 3-TSF, we consider the problem of partitioning an entire bandwidth into M channels of equal bandwidth and then further partitioning these M channels into P<=M licensed channels and M-P unlicensed channels. Licensed channels can be accessed both for licensed use and opportunistic use while unlicensed channels can be accessed only for opportunistic use. The access to licensed channels follow a tiered structure where licensed use has higher priority than opportunistic use. We address the following question in this thesis. Given a market setup, what value of M and P maximizes the net spectrum utilization of the entire bandwidth? This problem is highly relevant in context of partitioning the recently proposed Citizens Broadband Radio Service band. If M is too high or too low, it may decrease spectrum utilization due to limited channel capacity or due to wastage of channel capacity respectively. If P is too high (low), it will not incentivize the wireless operators who are primarily interested in licensed channels (unlicensed channels) to join the market. These tradeoffs are captured in our optimization problem which manifests itself as a two-stage Stackelberg game consisting of the regulator and the wireless operators. We design an algorithm to solve the Stackelberg game in order to find the optimal M and P. The algorithm design also involves an efficient Monte Carlo integrator to evaluate expected value of the involved random variables like spectrum utilization and operators revenue. We use this algorithm to obtain interesting numerical results which suggest how the optimal value of M and P changes with different market settings.
Description
August 2020
School of Engineering
Department
Dept. of Electrical, Computer, and Systems Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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