Online algorithm for leasing wireless channels in a three-tier spectrum sharing framework
Saha, Gourav; Abouzeid, Alhussein A.; Matinmikko-Blue, Marja (2018-11-09)
G. Saha, A. A. Abouzeid and M. Matinmikko-Blue, "Online Algorithm for Leasing Wireless Channels in a Three-Tier Spectrum Sharing Framework," in IEEE/ACM Transactions on Networking, vol. 26, no. 6, pp. 2623-2636, Dec. 2018. doi: 10.1109/TNET.2018.2877184
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The three-tier spectrum sharing framework (3-TSF) is a spectrum sharing model adopted by the Federal Communications Commission. According to this model, under-utilized federal spectrum like the Citizens Broadband Radio Service band is released for shared use where the highest preference is given to Tier-1 followed by Tier-2 (T2) and then Tier-3 (T3). In this paper, we study how a wireless operator, who is interested in maximizing its profit, can strategically operate as a T2 and/or a T3 user. T2 is characterized by paid but ”almost” guaranteed and interference-free channel access while T3 access is free but has the lesser guarantee and also faces channel interference. So the operator has to optimally decide between paid but better channel quality and free but uncertain channel quality. Also, the operator has to make these decisions without knowing future market variables like customer demand or channel availability. The main contribution of this paper is a deterministic online algorithm for leasing channels that has finite competitive ratio, low time complexity, and that does not rely on the knowledge of market statistics. Such algorithms are desirable in the early stages of the deployment of 3-TSF because the knowledge of market statistics may be rather inaccurate. We use tools from the ski-rental literature to design the online algorithm. The online optimization problem for leasing channels is a novel generalization of the ski-rental problem. We, therefore, make fundamental contributions to the ski-rental literature, the applications of which extend beyond this paper. We also conduct simulations using synthetic traces to compare our online algorithm with the benchmark and state-of-the-art algorithms.
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