A history-aware greedy channel restoration scheme for cognitive radio-based LTE networks

Cognitive radio-based Long Term Evolution (LTE) networks benefit from the powerful features of cognitive Radios (CR), such as learning and reconfigurability, enabling them to perform, if required, channel switching to a channel with higher quality. This process of searching and sensing other channels is a restoration (recovery) process where the objective is to find the best channel in the shortest time. We propose in this paper a history-aware greedy restoration scheme triggered not only when the quality of the current operating channel of the user goes below a threshold, but at regular intervals. Based on the state of the current channel, our scheme computes the optimal number of channels to be sensed in this restoration period and this number is dynamically updated after each channel sensing result. Intrinsic features of learning and history-awareness of CRs are used to create the list of channels to be sensed based on the channels’ background and historical information. The sensing order improves the restoration mechanism by providing a shorter restoration time or a restored channel with a higher quality. We show that the proposed combined scheme provides improvements for the CR-based LTE network’s throughput, compared to other restoration schemes which work based on only greediness or history-awareness.