Adaptive channel allocation and routing in cognitive radio networks

One of the biggest challenges in multi-hop cognitive radio networking is the dynamic change of channel availability to secondary users which could cause the breaking of routes and thus communications. To address this challenge, we propose an adaptive channel allocation and routing scheme in this paper. Our scheme is flexible so that it can react to the dynamic change of channel availability, and it can maximize the throughput by exploiting network coding opportunities. First, we model the primary users' activity, channel availability and the interference among the secondary users in a cognitive radio network environment, and show how to implement a backpressure algorithm and a network coding scheme in multi-hop cognitive radio networks. Second, we formulate an optimization problem to maximize the throughput of the network. We consider the channel availability constraint, and make use of the network coding opportunity. In order to reduce the computing complexity, we propose a distributed channel allocation and route selection algorithm. Furthermore, we compare the performance of our scheme with existing schemes for different scenarios of channel availability and network load through simulations. Our work brings insights on how to make route selection and channel allocation in multihop cognitive radio networks.