Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network using Deep Reinforcement Learning

Chukwuemeka Nwufo; Yichuang Sun; Oluyomi Simpson; Pan Cao

doi:10.1109/VTC2023-Spring57618.2023.10199952

Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network using Deep Reinforcement Learning

Chukwuemeka Nwufo, Yichuang Sun, Oluyomi Simpson, Pan Cao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This paper studies the secrecy energy efficiency (SEE) of a simultaneous wireless information and power transfer (SWIPT) network aided by multiple reconfigurable intelligent surfaces (RIS). The SWIPT network comprises several information decoding receivers (IDRs), and energy harvesting receivers (EHR) served by an access point (AP) supported by several distributed RIS. To effectively define the trade-off between the secrecy rate and energy efficiency of the multi-RIS SWIPT system, an optimization problem is formulated to maximize the SEE by optimizing the transmit beamforming at the AP and the phase shift at each RIS while dynamically controlling each RIS's ON/OFF status. The resultant non-convex optimization problem is solved using a deep reinforcement learning (DRL) framework to design the beamforming policy and a control mechanism for the RISs. Simulation results show that the proposed algorithm enhances the SEE compared to other benchmark schemes.

Original language	English
Title of host publication	2023 IEEE 97th Vehicular Technology Conference: VTC2023-Spring, 20-23 June 2023
Place of Publication	Florence, Italy
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	979-8-3503-1113-6
ISBN (Print)	979-8-3503-1114-3
DOIs	https://doi.org/10.1109/VTC2023-Spring57618.2023.10199952
Publication status	Published - 20 Jun 2023

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© 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/VTC2023-Spring57618.2023.10199952
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title = "Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network using Deep Reinforcement Learning",

abstract = "This paper studies the secrecy energy efficiency (SEE) of a simultaneous wireless information and power transfer (SWIPT) network aided by multiple reconfigurable intelligent surfaces (RIS). The SWIPT network comprises several information decoding receivers (IDRs), and energy harvesting receivers (EHR) served by an access point (AP) supported by several distributed RIS. To effectively define the trade-off between the secrecy rate and energy efficiency of the multi-RIS SWIPT system, an optimization problem is formulated to maximize the SEE by optimizing the transmit beamforming at the AP and the phase shift at each RIS while dynamically controlling each RIS's ON/OFF status. The resultant non-convex optimization problem is solved using a deep reinforcement learning (DRL) framework to design the beamforming policy and a control mechanism for the RISs. Simulation results show that the proposed algorithm enhances the SEE compared to other benchmark schemes.",

author = "Chukwuemeka Nwufo and Yichuang Sun and Oluyomi Simpson and Pan Cao",

note = "{\textcopyright} 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/VTC2023-Spring57618.2023.10199952",

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Nwufo, C, Sun, Y , Simpson, O & Cao, P 2023, Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network using Deep Reinforcement Learning. in 2023 IEEE 97th Vehicular Technology Conference: VTC2023-Spring, 20-23 June 2023. Institute of Electrical and Electronics Engineers (IEEE), Florence, Italy. https://doi.org/10.1109/VTC2023-Spring57618.2023.10199952

Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network using Deep Reinforcement Learning. / Nwufo, Chukwuemeka; Sun, Yichuang ; Simpson, Oluyomi et al.
2023 IEEE 97th Vehicular Technology Conference: VTC2023-Spring, 20-23 June 2023. Florence, Italy: Institute of Electrical and Electronics Engineers (IEEE), 2023.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network using Deep Reinforcement Learning

AU - Nwufo, Chukwuemeka

AU - Sun, Yichuang

AU - Simpson, Oluyomi

AU - Cao, Pan

N1 - © 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/VTC2023-Spring57618.2023.10199952

PY - 2023/6/20

Y1 - 2023/6/20

N2 - This paper studies the secrecy energy efficiency (SEE) of a simultaneous wireless information and power transfer (SWIPT) network aided by multiple reconfigurable intelligent surfaces (RIS). The SWIPT network comprises several information decoding receivers (IDRs), and energy harvesting receivers (EHR) served by an access point (AP) supported by several distributed RIS. To effectively define the trade-off between the secrecy rate and energy efficiency of the multi-RIS SWIPT system, an optimization problem is formulated to maximize the SEE by optimizing the transmit beamforming at the AP and the phase shift at each RIS while dynamically controlling each RIS's ON/OFF status. The resultant non-convex optimization problem is solved using a deep reinforcement learning (DRL) framework to design the beamforming policy and a control mechanism for the RISs. Simulation results show that the proposed algorithm enhances the SEE compared to other benchmark schemes.

AB - This paper studies the secrecy energy efficiency (SEE) of a simultaneous wireless information and power transfer (SWIPT) network aided by multiple reconfigurable intelligent surfaces (RIS). The SWIPT network comprises several information decoding receivers (IDRs), and energy harvesting receivers (EHR) served by an access point (AP) supported by several distributed RIS. To effectively define the trade-off between the secrecy rate and energy efficiency of the multi-RIS SWIPT system, an optimization problem is formulated to maximize the SEE by optimizing the transmit beamforming at the AP and the phase shift at each RIS while dynamically controlling each RIS's ON/OFF status. The resultant non-convex optimization problem is solved using a deep reinforcement learning (DRL) framework to design the beamforming policy and a control mechanism for the RISs. Simulation results show that the proposed algorithm enhances the SEE compared to other benchmark schemes.

U2 - 10.1109/VTC2023-Spring57618.2023.10199952

DO - 10.1109/VTC2023-Spring57618.2023.10199952

M3 - Conference contribution

SN - 979-8-3503-1114-3

BT - 2023 IEEE 97th Vehicular Technology Conference: VTC2023-Spring, 20-23 June 2023

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Florence, Italy

ER -

Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network using Deep Reinforcement Learning

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