Backscatter then Forward: A Relaying Scheme for Batteryless IoT Networks

Lyu, B; Hoang, DT; Yang, Z

Backscatter then Forward: A Relaying Scheme for Batteryless IoT Networks

Lyu, B Hoang, DT Yang, Z

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Wireless Communications Letters, 2020, 9, (4), pp. 562-566
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Lyu, B
dc.contributor.author	Hoang, DT
dc.contributor.author	Yang, Z
dc.date.accessioned	2020-11-01T21:02:35Z
dc.date.available	2020-11-01T21:02:35Z
dc.date.issued	2020
dc.identifier.citation	IEEE Wireless Communications Letters, 2020, 9, (4), pp. 562-566
dc.identifier.issn	2162-2337
dc.identifier.issn	2162-2345
dc.identifier.uri	http://hdl.handle.net/10453/143676
dc.description.abstract	IEEE In this paper, we introduce a novel relaying scheme together with a joint energy beamforming (EB) and time allocation optimization to meet requirements about energy efficiency and hardware constraints of batteryless IoT networks. First, we propose an intelligent relaying scheme using RF-powered gateways as relay nodes to deliver information from batteryless IoT devices to a hybrid access point (HAP). The HAP can also transfer energy to the gateways and batteryless devices using EB techniques. The energy from HAP will be then used to supply power for gateways and as a communications means to transmit data for batteryless devices. We then formulate a sum-rate maximization problem by jointly optimizing the EB vectors, time scheduling, and power allocation. Since the optimization problem is non-convex, we exploit EB characteristics for data backscattering and employ variable substitutions and semidefinite relaxation techniques to transform it into a convex one. After that, a low-complexity method is proposed to obtain the optimal solution in a closed-form. Simulation results confirm that the proposed scheme can achieve significant sum-rate gain.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Wireless Communications Letters
dc.relation.isbasedon	10.1109/LWC.2019.2962415
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.title	Backscatter then Forward: A Relaying Scheme for Batteryless IoT Networks
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2020-11-01T21:02:32Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	4

Abstract:

IEEE In this paper, we introduce a novel relaying scheme together with a joint energy beamforming (EB) and time allocation optimization to meet requirements about energy efficiency and hardware constraints of batteryless IoT networks. First, we propose an intelligent relaying scheme using RF-powered gateways as relay nodes to deliver information from batteryless IoT devices to a hybrid access point (HAP). The HAP can also transfer energy to the gateways and batteryless devices using EB techniques. The energy from HAP will be then used to supply power for gateways and as a communications means to transmit data for batteryless devices. We then formulate a sum-rate maximization problem by jointly optimizing the EB vectors, time scheduling, and power allocation. Since the optimization problem is non-convex, we exploit EB characteristics for data backscattering and employ variable substitutions and semidefinite relaxation techniques to transform it into a convex one. After that, a low-complexity method is proposed to obtain the optimal solution in a closed-form. Simulation results confirm that the proposed scheme can achieve significant sum-rate gain.

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http://hdl.handle.net/10453/143676