Channel Characteristic Aware Spectrum Aggregation Algorithm in Cognitive Radio Networks

In Cognitive Radio (CR) networks, it is common that the spectrum holes are too narrow to support high-speed communications. Discontinuous Orthogonal Frequency Division Multiplexing (DOFDM) is a good way for a secondary user to access several spectrum fragments simultaneously with one Radio Front (RF). In this paper, a novel Channel Characteristic Aware Spectrum Aggregation (CCASA) algorithm which uses DOFDM to aggregation spectrum fragments with only one radio front is proposed in order to increase the overall throughput of a CR network. By combining Adaptive Modulation and Coding (AMC) and spectrum aggregation, the good subcarriers are assigned to the specific secondary users in CCASA algorithm thus achieving a better channel efficiency. Different bandwidth requirement and aggregation limitation of secondary users are both considered in this algorithm while maintaining a fairly low computational complexity. The simulation results show that CCASA achieves a bigger total throughput than existing aggregation algorithms.