Research on channel selection algorithms in cognitive radio networks

To address the secondary users channel selection issue in cognitive radio network, a novel channel selection strategy is proposed. Four typical channel selection models under auction mechanism, machine learning scheme, channel prediction scheme and optimization scheme are compared and analyzed. Based on the optimization theory, the selfish channel selection algorithm and the cooperative channel selection algorithm are proposed in view of the heterogeneity of the channel. The selfish algorithm selects the channel which provides the maximum transmission rate for the secondary users (SU), while the cooperative algorithm selects the channel that benefits overall system throughput. Simulations compare proposed algorithms with random channel selection algorithm, and suggest proposed algorithms outperform random channel selection algorithm in terms of system average throughput, channel utilization, average handoff time and average transmission time.