Deep reinforcement learning based optimal channel selection for cognitive radio vehicular ad-hoc network

Abstract

: Channel selection is a challenging task in cognitive radio vehicular networks. Vehicles have to sense the channels periodically. Due to this, a lot of time is wasted which could have been utilised for transmission of data. Employing road side units (RSUs) in sensing can prove to be useful for this purpose. The RSUs may select the channel and allocate it to the vehicles on demand. However, this sensing should be proactive. RSUs should know in advance the channel to be allocated when requested. For this purpose, a deep reinforcement learning algorithm namely deep reinforcement learning based optimal channel selection is proposed in this study for training the network according to the previously sensed data. Proposed protocol is simulated and results are compared with the existing methods. The packet delivery ratio is increased by 2%, throughput is increased by 1.8%, average delay is decreased by 2% and primary user collision ratio is reduced by 3.2% when compared with similar recent work by varying number of vehicles. On the other hand, when compared with similar recent work by varying channel availability, the packet delivery ratio is increased by 4.5 %, throughput by 4.3%, average delay is decreased by 3% and PU collision ratio by 5.