A Sub-Optimal Channel Switching-Aware Spectrum Aggregation Approach for CRNs

We consider a cognitive radio network (CRN) that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In such an opportunistic spectrum access, primary user can reclaim a channel used by a secondary transmission. When the secondary transmission is interrupted by a primary transmission, the secondary network needs to switch the channel of the interrupted transmission, resulting in additional delay. When a secondary user accesses more spectrum to increase its data rate, channel switching could be more frequent. In this context, we formulate a dynamic spectrum aggregation optimisation problem to minimize channel switching delay. While considering multiple users, the problem is formulated as a sum of fractional programming problems. We propose a sub-optimal algorithm that simplifies the fractional programming to linear programming first and solves each linear programming (for each user) using Dinkelbach's algorithm. Simulation results demonstrate that the proposed algorithm can reduce the channel switching delay. When multiple users are served in the network, the proposed algorithm also shows good performance in terms of fairness and total data transmission time.