Admission control and profitability analysis in dynamic spectrum access data networks
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Recent regulations by the Federal Communication Commission (FCC) enable network service providers to lease their spectrum to short-term leased secondary users for opportunistic usage. Driven by earlier studies on spectrum leasing for voice and streaming traffic, this thesis derives optimal policies for the admission control of secondary users carrying data traffic. Additionally, it establishes profitability conditions of spectrum leasing. We consider a processor sharing network where bandwidth is equally shared among all users with no partitioning. We further consider homogeneous and elastic data traffic: All the users have the same traffic characteristics and adjust their access rate to the available bandwidth in the network. The contributions of this thesis are the following: First, we analyze the joint problem of bandwidth allocation and admission control of secondary users. Under the assumption of Poisson session arrivals and balanced bandwidth allocations, we show that the steady state distributions of the number of active users in the network are insensitive to track characteristics beyond their means. This result holds for arbitrary occupancy-based admission control policies. Next, we prove that the optimal occupancy-based admission control policy of secondary users is of threshold type, which means that secondary user arrivals are accepted when the total number of active users in the network is below a certain threshold; otherwise, they are rejected. Finally, under optimal occupancy-based admission control, we characterize profitable prices. We show that profitability is insensitive to the secondary demand function. We identify a price, referred to as the break-even price, that makes opening the network for secondary spectrum access profitable. Thus, we show that admitting secondary users when the network is empty is profitable for any price greater than the break-even price. Remarkably, all of our results hold for realistic data traffic models assuming Poisson session arrivals and general flow size distributions.
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