Hybrid Directional CR-MAC based on Q-Learning with Directional Power Control

In this paper, we investigate the Hybrid Directional CR-MAC based on Q-Learning with Directional Power Control in cognitive radio (CR) systems. In CR systems, nodes can switch to heterogeneous non-overlapping channels opportunistically which offer higher achievable throughput. However, the random channel selection policy in existing CR-MAC protocol has problems like delay, packet collisions, and quality of service. The proposed channel selection scheme which is quite different from the traditional scheme is adopted by nodes to achieve context awareness and intelligence for adaptive channel selection. The nodes select a channel based on the results learned by interactions with the other nodes and channels. The directional transmission power control scheme allows the nodes to reuse the channels subject to interference constraints. The simulation results show that nodes using the proposed algorithm can select channels adaptively and optimal transmission power which helps to achieve high throughput and minimized power consumption.