Optimal Channel Selection and Power Allocation for Channel Assembling in Cognitive Radio Networks

Channel assembling (ChA) has been proposed to maximize capacity in wireless communications. This paper determines the optimal channel selection and power distribution for secondary user (SU) transmissions under varying channel conditions, subject to power constraint, minimum quality of service (QoS) requirement for capacity, and collision probability threshold to protect primary user (PU) services. The nature of the optimization problem turns out to be in a form of mixed integer nonlinear programming (MINLP), which is generally nondeterministic in polynomial time. A Lagrangian framework is therefore employed to reformulate the optimization problem, based on which dual decomposition with subgradient and Newton-Raphson methods are used to determine a relaxed optimal solution. Thereafter, accelerated branch-and-bound (BnB) with sequential fixing (SF) is applied to determine an optimal solution to the original MINLP problem. The simulation results presented in this paper demonstrate that adaptive ChA with channel characterization improves performance in terms of channel capacity, outage probability and collision probability.