Optimization of the power-to-velocity ratio in the downlink of vehicular networks
Optimization of the power-to-velocity ratio in the downlink of vehicular networks
Consider a base station (BS) relying on a massive antenna array, which transmits information to multiple vehicles of vehicular networks. In order to jointly consider both the communication resource consumption and the road-traffic efficiency, we define a new metric given by the BS's downlink power normalized by the vehicular velocity. We refer to it as the Power to Velocity Ratio (PVR). The prime objective of this paper is to minimize either the maximum individual vehicle PVR or the entire system's PVR by optimizing the power allocation at the BS, while guaranteeing the information requirements of the vehicles both under the total transmit power constraint and driving velocity constraint. As for the individual vehicle PVR, a closed-form power allocation expression is derived by assuming that the transmit power constraint is non-restrictive. Based on this an optimal power allocation algorithm is proposed for arbitrary finite transmit power constraints. As for the system PVR, the non-convex problem formulated is first transformed into a convex problem and then the optimal solution is found by conceiving an efficient iterative algorithm. Our simulation results show that the proposed algorithms indeed succeed in achieving the optimal individual vehicle or system PVR.
Channel estimation, Cloud computing, Downlink, Information processing, Power demand, Resource management, Transmitting antennas, Vehicular networks, power consumption, traffic efficiency
Zhao, Long
d90dfed2-ac7b-4169-9c1a-851b008545b9
Zhang, Ping
2def4374-679d-41d1-bf3a-483028a73275
Zheng, Kan
1141004c-e359-4b26-a49b-2a821d76edf0
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhao, Long
d90dfed2-ac7b-4169-9c1a-851b008545b9
Zhang, Ping
2def4374-679d-41d1-bf3a-483028a73275
Zheng, Kan
1141004c-e359-4b26-a49b-2a821d76edf0
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhao, Long, Zhang, Ping, Zheng, Kan and Hanzo, Lajos
(2021)
Optimization of the power-to-velocity ratio in the downlink of vehicular networks.
IEEE Transactions on Vehicular Technology.
(doi:10.1109/TVT.2021.3128699).
(In Press)
Abstract
Consider a base station (BS) relying on a massive antenna array, which transmits information to multiple vehicles of vehicular networks. In order to jointly consider both the communication resource consumption and the road-traffic efficiency, we define a new metric given by the BS's downlink power normalized by the vehicular velocity. We refer to it as the Power to Velocity Ratio (PVR). The prime objective of this paper is to minimize either the maximum individual vehicle PVR or the entire system's PVR by optimizing the power allocation at the BS, while guaranteeing the information requirements of the vehicles both under the total transmit power constraint and driving velocity constraint. As for the individual vehicle PVR, a closed-form power allocation expression is derived by assuming that the transmit power constraint is non-restrictive. Based on this an optimal power allocation algorithm is proposed for arbitrary finite transmit power constraints. As for the system PVR, the non-convex problem formulated is first transformed into a convex problem and then the optimal solution is found by conceiving an efficient iterative algorithm. Our simulation results show that the proposed algorithms indeed succeed in achieving the optimal individual vehicle or system PVR.
Text
PVRLong
- Accepted Manuscript
More information
Accepted/In Press date: 15 November 2021
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Channel estimation, Cloud computing, Downlink, Information processing, Power demand, Resource management, Transmitting antennas, Vehicular networks, power consumption, traffic efficiency
Identifiers
Local EPrints ID: 452321
URI: http://eprints.soton.ac.uk/id/eprint/452321
ISSN: 0018-9545
PURE UUID: e798d9c4-edf5-4c1d-b409-ff2cd9ef2cef
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Date deposited: 07 Dec 2021 17:31
Last modified: 17 Mar 2024 02:35
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Contributors
Author:
Long Zhao
Author:
Ping Zhang
Author:
Kan Zheng
Author:
Lajos Hanzo
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