Efficient joint resource allocation for cognitive internet of vehicles networks based on asymmetric relay transmission

In the internet of vehicles (IoV) networks, a direct connection from the source end to the destination end may not be established due to the fast vehicle speed, long distance between vehicles, variable vehicle density, serious channel fading etc. In this paper, a joint resource allocation (RA) in th...

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Bibliographic Details
Published in:IET communications 2021-08, Vol.15 (13), p.1655-1664
Main Authors: Song, Xiaoqin, Wang, Kuiyu, Xu, Lei, Tan, Yazhu, Miao, Juanjuan
Format: Article
Language:English
Subjects:
Algorithms
Asymmetry
Bandwidths
Communication
Complexity
Energy consumption
Energy efficiency
Fading
Internet of Vehicles
Licenses
Linear programming
Mobile radio systems
Modulation and coding methods
Networks
Optimisation techniques
Protocol
Quality of service
Relay
Resource allocation
Spectrum allocation
Subcarriers
Traffic speed
Vehicles
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Summary:In the internet of vehicles (IoV) networks, a direct connection from the source end to the destination end may not be established due to the fast vehicle speed, long distance between vehicles, variable vehicle density, serious channel fading etc. In this paper, a joint resource allocation (RA) in the relay‐aided IoV networks is modelled as a mixed binary integer non‐linear programming (MBINP), which maximises the throughput of cognitive IoV networks among different subcarriers and relays. To further reduce the computational complexity, a suboptimal scheme is presented. First, the appropriate relay and subcarrier pairs are obtained by averaging the power allocation among the cognitive sources and relays. Second, an alternative optimisation mechanism is proposed to the power allocation. Simulation results show that, different from the symmetric time‐slot relay transmission, the asymmetric one can significantly increase the degree of freedom for transmission. Therefore, it is more robust to the impact of the relay node location on the throughput. Moreover, the proposed suboptimal RA algorithm not only can obtain the system capacity close to the optimal one, but also can reduce the computational complexity. At the same time, unacceptable degradation caused by severe channel fading is avoided.
ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.12178