Resource-Minimized Channel Assignment for Multi-Transceiver Cognitive Radio Networks

Abstract

The advancement of cognitive radio (CR) has uncovered new dynamics in multi-hop, wireless networking. Given the increased agility of a transceiver's frequency assignment, the network topology can be optimized to address end-to-end networking goals. We propose a channel assignment scheme for cognitive radio networks (CRNs) that balances the need for topology adaptation focusing on flow rate maximization and the need for a stable baseline topology that supports network connectivity. We focus on CRNs in which nodes are equipped with multiple radios or transceivers, each of which can be assigned to a channel. First, our approach assigns channels independently of traffic, to achieve basic network connectivity and support light loads such as control traffic, and second, it dynamically assigns channels to the remaining transceivers in response to traffic demand. In this paper, we focus on the traffic-independent (TI) channel assignment with the goal of dedicating as few transceivers as possible to achieving baseline connectivity. By conserving transceivers in the TI assignment, the network is more able to adapt to any traffic demands in a subsequent traffic-driven (TD) assignment. We formulate the problem as a two-stage mixed integer linear program (MILP), with a TI stage and a TD stage. We propose a centralized greedy approach to TI assignment which performs nearly identically to the optimum obtained from the two-stage MILP in terms of the number of transceivers assigned and flow rate in the evaluated scenarios. Subsequently, we propose a distributed greedy TI approach that performs within 9% of the optimum in terms of the number of transceivers assigned and within 1.5% of the optimum in terms of flow rate.