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

Content Provider	IET Digital Library
Author	Pal, Raghavendra Gupta, Nishu Prakash, Arun Tripathi, Rajeev Rodrigues, Joel J. P. C.
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 5.5%.
Starting Page	3464
Ending Page	3471
Page Count	8
ISSN	17518628
Volume Number	14
e-ISSN	17518636
Issue Number	Issue 19, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/14/19
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2020.0451
Journal	IET Communications
Publisher Date	2020-09-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Channel Availability Cognitive Radio Communication Computing Control Application in Radio And Radar Data Handling Data Handling Technique Data Transmission Deep Reinforcement Learning Algorithm Learning in AI Mobile Radio System Neural Computing Technique Neural Nets Optimal Channel Selection Packet Delivery Ratio Primary User Collision Ratio Proposed Protocol Protocol PU Collision Ratio Radio Spectrum Management Resource Allocation Road Side Units RSU Sensed Data Telecommunication Computing Telecommunication Congestion Control Vehicular Ad Hoc Network Vehicular Ad-hoc Network
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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