Open AccessDeep reinforcement learning based optimal channel selection for cognitive radio vehicular ad‐hoc network
Author(s) -
Pal Raghavendra,
Gupta Nishu,
Prakash Arun,
Tripathi Rajeev,
Rodrigues Joel J. P. C.
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2020.0451
Subject(s) - reinforcement learning , computer science , throughput , channel (broadcasting) , cognitive radio , computer network , network packet , vehicular ad hoc network , collision , packet loss , wireless ad hoc network , real time computing , wireless , artificial intelligence , telecommunications , computer security
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 5.5%.