期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 71, 期 11, 页码 12291-12303出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2022.3195216
关键词
Dense network; energy harvesting; throughput maximization; wireless powered network
资金
- Joint Fund for Regional Innovationand Development of the National Natural Science Foundation of China [U21A20445, 61671219]
This paper proposes a transmission and resource allocation strategy for multiple co-existing wireless powered vehicle area networks. The strategy enables simultaneous wireless information and power transfer, while addressing issues such as energy harvesting and interference, resulting in improved overall throughput.
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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