Analysis of spectrum adaptation and spectrum leasing in heterogeneous traffic cognitive radio networks

In this paper, the performance of heterogeneous traffic Cognitive Radio Networks (CRNs) with spectrum adaptation and spectrum leasing is mathematically analyzed and evaluated. Spectrum adaptation and spectrum leasing are used jointly with spectrum handoff, call buffering, and preemptive priority mechanisms to improve system performance. Two different spectrum leasing strategies are proposed and evaluated. Service differentiation between real-time and elastic (data) traffic is considered according to their different delay tolerance characteristics. For given quality of service (QoS) requirements, the Erlang capacity as a function of the maximum allowable number of simultaneously rented resources is evaluated. Additionally, the fraction of time that rented resources are needed is calculated. This quantity is used to estimate the cost of spectrum leasing per capacity Erlang. Numerical results demonstrate that resource leasing is an effective strategy to drastically improve system capacity and avoids the existence of the critical utilization factor of the primary resources (i.e., value of the primary resource utilization factor from which it is no longer possible to guarantee QoS in CRNs). Finally, by prioritizing the use of rented resources for service requests with stringent delay requirement, the cost for spectrum lease can be minimized.