On the study of sustainability and outage of SWIPT-enabled wireless communications

Luo, Yang, Luo, Chunbo, Min, Geyong, Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132 and McClean, Sally L. (2021) On the study of sustainability and outage of SWIPT-enabled wireless communications. IEEE Journal of Selected Topics in Signal Processing, 15 (5). pp. 1159-1168. ISSN 1932-4553

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Abstract

Wireless power transfer technologies such as simultaneous wireless information and power transfer (SWIPT) have shown significant potential to revolutionise the design of future wireless communication systems. When the only energy source is from the wireless signals that are mainly intended for information communications, the sustainability and outage performance of SWIPT systems become critical factors in theoretical evaluation and practical applications. This paper firstly models the energy harvesting and energy consumption of the power splitting protocol based SWIPT systems to investigate the general sustainability condition. We further model the power and information transfer outage probabilities using Markov Chains, which are unique for SWIPT systems since they both could cause communication outage. We further demonstrate how to apply the closed-form expression of the outage to optimise the key parameter of splitting ratio for SWIPT systems. Hardware and numerical experiments demonstrate the validity of the proposed model and outage analysis, and confirm the effectiveness of the solution to calculate the optimal splitting ratios under different signal and channel conditions.

Item Type: Article
Uncontrolled Keywords: swipt,wireless power transfer,outage,wireless communications,signal processing,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1700/1711
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Smart Emerging Technologies
Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Faculty of Science > Research Groups > Data Science and AI
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Depositing User: LivePure Connector
Date Deposited: 07 Sep 2021 00:22
Last Modified: 14 Dec 2024 01:32
URI: https://ueaeprints.uea.ac.uk/id/eprint/81316
DOI: 10.1109/JSTSP.2021.3092136

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