Smart Channel Switching in Cognitive Radio Networks

Prediction of future spectrum availability of different channels based on history information helps the cognitive radio (CR) switch proactively to the best channel before the appearance of primary user. Most existing proposals of proactive channel access serve as the guidelines of switching channels according to the environmental changes. However, few of them takes account of the drastic cost of channel switching. The frequent channel switching raise up the packet-loss-ratio and aggravate the delay. On account of this, we propose an intelligence channel selection algorithm. In this algorithm, the secondary users decide whether to switch the channel or not based on channel prediction and switching overhead. Simulations show that the proposed method can reduce the number of channel switching by up to 20%, and minimize disruptions to primary users, especially when traffic load of primary user is heavy.