Multi-IRS assisted multi-Cluster wireless powered IoT networks
Multi-IRS assisted multi-Cluster wireless powered IoT networks
This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, a power station (PS) first broadcasts wireless energy to the distributed IoT devices grouped into multiple clusters. The IoT devices then use the harvested energy to convey their information to an access point (AP), based on a hybrid time- and frequency-division multiple access (TDMA-FDMA) protocol. Furthermore, multiple IRSs are deployed to perform anomalous reflection for energy and information transfer, to improve energy harvesting and data transmission capabilities. Under the constraints of the unit-modulus phase shifts, the transmission time shared among clusters and the bandwidth shared by the devices in each cluster, the considered system is optimized by maximizing its sum throughput. The optimization problem is non-convex and with complicatedly coupled variables. To solve this problem, we propose to first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission scheduling and bandwidth allocation, then the quadratic transformation (QT) and the alternating optimization (AO) algorithm are introduced to solve the downlink and uplink IRS phase shifts, whilst the Majorization-Minimization (MM) and Riemannian Manifold Optimization (RMO) methods are applied to iteratively derive their closed-form solutions. Additionally, we provide a benchmark scheme to facilitate the system design, where each IRS can control its “on/off” state to aid the downlink and uplink transmissions in the condition of at most one activated IRS during one certain time duration. Finally, simulation results are presented to verify the optimality of our proposed scheme and highlight the beneficial role of the IRS.
Intelligent reflecting surface (IRS), fractional energy harvesting, hybrid TDMA-FDMA, majorization-minimization (MM) and riemannian manifold optimization (RMO), wireless powered Internet of Things (WP-IoT) network
4712-4728
Chu, Zheng
2e26321b-20ad-4999-a0d3-7a571c92c0bc
Xiao, Pei
48e5c554-b0e2-41a9-bf48-546628bc4bcc
Mi, De
991e7151-af8e-4d5e-8bf4-53d6f1ec90c1
Hao, Wanming
8ce068aa-1812-4964-81aa-7306e1b7d291
Xiao, Yue
10f13317-e86c-4b9e-bc7a-f7622c37ee01
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
1 July 2023
Chu, Zheng
2e26321b-20ad-4999-a0d3-7a571c92c0bc
Xiao, Pei
48e5c554-b0e2-41a9-bf48-546628bc4bcc
Mi, De
991e7151-af8e-4d5e-8bf4-53d6f1ec90c1
Hao, Wanming
8ce068aa-1812-4964-81aa-7306e1b7d291
Xiao, Yue
10f13317-e86c-4b9e-bc7a-f7622c37ee01
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Chu, Zheng, Xiao, Pei, Mi, De, Hao, Wanming, Xiao, Yue and Yang, Lie-Liang
(2023)
Multi-IRS assisted multi-Cluster wireless powered IoT networks.
IEEE Transactions on Wireless Communications, 22 (7), .
(doi:10.1109/TWC.2022.3228017).
Abstract
This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, a power station (PS) first broadcasts wireless energy to the distributed IoT devices grouped into multiple clusters. The IoT devices then use the harvested energy to convey their information to an access point (AP), based on a hybrid time- and frequency-division multiple access (TDMA-FDMA) protocol. Furthermore, multiple IRSs are deployed to perform anomalous reflection for energy and information transfer, to improve energy harvesting and data transmission capabilities. Under the constraints of the unit-modulus phase shifts, the transmission time shared among clusters and the bandwidth shared by the devices in each cluster, the considered system is optimized by maximizing its sum throughput. The optimization problem is non-convex and with complicatedly coupled variables. To solve this problem, we propose to first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission scheduling and bandwidth allocation, then the quadratic transformation (QT) and the alternating optimization (AO) algorithm are introduced to solve the downlink and uplink IRS phase shifts, whilst the Majorization-Minimization (MM) and Riemannian Manifold Optimization (RMO) methods are applied to iteratively derive their closed-form solutions. Additionally, we provide a benchmark scheme to facilitate the system design, where each IRS can control its “on/off” state to aid the downlink and uplink transmissions in the condition of at most one activated IRS during one certain time duration. Finally, simulation results are presented to verify the optimality of our proposed scheme and highlight the beneficial role of the IRS.
Text
Multi-IRS
- Accepted Manuscript
More information
Accepted/In Press date: 30 November 2022
e-pub ahead of print date: 15 December 2022
Published date: 1 July 2023
Additional Information:
Funding Information:
This work was supported in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/X013162/1. The work of Wanming Hao was supported in part by the China National Natural Science Foundation under Grant 62101499 and in part by the SongShan Laboratory Foundation under Grant YYJC022022003.
Publisher Copyright:
© 2002-2012 IEEE.
Keywords:
Intelligent reflecting surface (IRS), fractional energy harvesting, hybrid TDMA-FDMA, majorization-minimization (MM) and riemannian manifold optimization (RMO), wireless powered Internet of Things (WP-IoT) network
Identifiers
Local EPrints ID: 481466
URI: http://eprints.soton.ac.uk/id/eprint/481466
ISSN: 1536-1276
PURE UUID: 9c81b7d1-c736-4094-869d-08492273a137
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Date deposited: 29 Aug 2023 17:08
Last modified: 18 Mar 2024 02:49
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Contributors
Author:
Zheng Chu
Author:
Pei Xiao
Author:
De Mi
Author:
Wanming Hao
Author:
Yue Xiao
Author:
Lie-Liang Yang
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