共 46 条
Transmission Optimization and Resource Allocation for Wireless Powered Dense Vehicle Area Network With Energy Recycling
被引:6
作者:
Jin, Chi
[1
]
Hu, Fengye
[1
]
Ling, Zhuang
[1
]
Mao, Zhi
[1
]
Chang, Zheng
[2
]
Li, Cheng
[3
]
机构:
[1] Jilin Univ, Coll Commun Engn, Changchun 130012, Peoples R China
[2] Univ Jyvaskyla, Fac Informat Technol, FIN-40014 Jyvaskyla, Finland
[3] Memorial Univ, Fac Engn & Appl Sci, St John, NL A1B 3X5, Canada
基金:
中国国家自然科学基金;
关键词:
Dense network;
energy harvesting;
throughput maximization;
wireless powered network;
SUM-RATE MAXIMIZATION;
COMMUNICATION-NETWORKS;
TIME ALLOCATION;
MULTI-ANTENNA;
INFORMATION;
IOT;
THROUGHPUT;
INTERNET;
PERFORMANCE;
MECHANISM;
D O I:
10.1109/TVT.2022.3195216
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages.
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页码:12291 / 12303
页数:13
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