A Multi-Channel Sensing Order Optimization Algorithm Based on Markov Prediction

This paper considers the scenario that the cognitive radio network has a wide spectrum band with multiple channels across different frequency band. Limited by the hardware capability, the secondary user can only sense one channel at each time slot. If the channel being sensed is not used by the primary user, the secondary user can access the channel and transmit data; otherwise, the secondary user has to wait until the next slot to choose a channel to sense. In this paper, we propose a multi-channel sensing order optimization algorithm based on Markov prediction, with the knowledge of primary users´ statistical information. According to this algorithm, each secondary user chooses the sensing channel based on Markov prediction, which reduces the sensing times and improve spectrum utilization efficiency. We also suggest a frequency-domain sensing-window adjustment strategy to reduce the probability of collision when there are more than one secondary users in the network. We define sensing efficiency to evaluate how quickly the secondary user can find an available channel for data transmission. Finally, the performance of the proposed optimization algorithm is evaluated through simulation results, and comparison analysis is conducted with the random channel selection strategy.