Optimal Admission and Eviction Control of Secondary Users at Cognitive Radio HotSpots

Abstract

Cognitive Radio (CR) HotSpot is a typical example of commercialized Dynamic Spectrum Access (D A) in which a Wireless Service Provider (WSP) provides CR end-users access to its local-area network. A WSP temporarily leases licensed channels from the primary licensees via spectrum auction, and subleases them to CR end-users or customers, each demanding a different amount of spectrum, according to its own advertised pricing policy. The CR customers use the subleased channels opportunistically when the channels are not being used by primary/legacy users, and hence, the amount of available spectrum resource varies with time. A WSP should, therefore, maximize its average profit by 'optimally' (in some sense) (1) controlling the admission/rejection of arriving CR end-users, and (2) determining a policy of evicting in-service opportunistic users at the channel to which primary users return. To our best knowledge, this is the first attempt to jointly optimize admission and eviction controls for the dynamic spectrum market.

The WSP's profit maximization problem is formulated with a Semi-Markov Decision Process (SMDP) and its corresponding Linear Programming (LP) setup. This problem is found to become Nonlinear Programming (NLP) subject to two Quality-of-Service (QoS) constraints on request-blocking and user-dropping probabilities, which can fortunately be converted to LP via some manipulation. Using an extensive numerical analysis, we discovered that the derived optimal policy achieves up to 81% more profit than a Complete-Sharing (CS) algorithm in the tested scenario. We also investigate tradeoffs between the two QoS constraints, and consider other important factors affecting WSP's profit maximization such as the number of leased channels, the end-user pricing, and the cost for reimbursing evicted customers.