RIS-Assisted Wireless Powered MEC: Multiple Access Design and Resource Allocation

This paper investigates a reconfigurable intelligent surface (RIS)-assisted wireless powered mobile edge computing (MEC) system, where an access point (AP) integrated with an MEC server first transmits energy signals to charge multiple energy-constrained devices in the downlink (DL), and then the devices utilize the harvested energy to perform MEC offloading in the uplink (UL) assisted by an RIS. Specifically, three multiple access protocols for MEC offloading, namely pure non-orthogonal multiple access (NOMA), pure orthogonal multiple access, and hybrid NOMA, are proposed to exploit dynamic RIS beamforming and energy recycling among devices to enhance the efficiencies of energy harvesting and MEC offloading. For each of the three protocols, a joint resource allocation framework of the AP/devices transmit power, the RIS phase shifts, and the DL/UL time allocation is formulated to minimize the energy consumption at the AP. Because of those highly coupled optimization variables, the formulated optimization problems are first shown to be non-convex, and then the intrinsic structure of the problems is exploited to devise computationally-efficient algorithms and solve them iteratively. Numverical results are provided to demonstrate the performance improvement of our proposed designs compared to various benchmark schemes, and reveal the practical significance of the considered multiple access protocols with dynamic RIS beamforming and energy recycling for spectral and energy efficient MEC offloading.

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Publication Details

Journal
IEEE Transactions on Wireless Communications
Published
2024-11-27
DOI
https://doi.org/10.1109/twc.2024.3503582
Citations
16
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
Field-Weighted Citation Impact
3.34
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article

RIS-Assisted Wireless Powered MEC: Multiple Access Design and Resource Allocation

Arumugam Nallanathan, Zhiguo Ding, Long Yang, Lu Lv et al.
16 citations
IEEE Transactions on Wireless Communications
Energy Harvesting in Wireless Networks
3.34
article

RIS-Assisted Wireless Powered MEC: Multiple Access Design and Resource Allocation

Arumugam Nallanathan, Zhiguo Ding, Long Yang, Lu Lv, Naofal Al‐Dhahir, Hao Luo, Jian Chen
article en
16 citations

Abstract

This paper investigates a reconfigurable intelligent surface (RIS)-assisted wireless powered mobile edge computing (MEC) system, where an access point (AP) integrated with an MEC server first transmits energy signals to charge multiple energy-constrained devices in the downlink (DL), and then the devices utilize the harvested energy to perform MEC offloading in the uplink (UL) assisted by an RIS. Specifically, three multiple access protocols for MEC offloading, namely pure non-orthogonal multiple access (NOMA), pure orthogonal multiple access, and hybrid NOMA, are proposed to exploit dynamic RIS beamforming and energy recycling among devices to enhance the efficiencies of energy harvesting and MEC offloading. For each of the three protocols, a joint resource allocation framework of the AP/devices transmit power, the RIS phase shifts, and the DL/UL time allocation is formulated to minimize the energy consumption at the AP. Because of those highly coupled optimization variables, the formulated optimization problems are first shown to be non-convex, and then the intrinsic structure of the problems is exploited to devise computationally-efficient algorithms and solve them iteratively. Numverical results are provided to demonstrate the performance improvement of our proposed designs compared to various benchmark schemes, and reveal the practical significance of the considered multiple access protocols with dynamic RIS beamforming and energy recycling for spectral and energy efficient MEC offloading.

IEEE Transactions on Wireless CommunicationsVol. 24(2)
Xidian University (CN), The University of Texas at Dallas (US), Queen Mary University of London (GB), Khalifa University of Science and Technology (AE)
Decent work and economic growth
Openalex Percentile: Top 7%
Energy Harvesting in Wireless Networks
3.34
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