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Simultaneously Wireless InFormation and energy Transfer (SWIFT)

Lead Research Organisation: Queen Mary University of London
Department Name: Sch of Electronic Eng & Computer Science

Abstract

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Related Projects

Project Reference Relationship Related To Start End Award Value
EP/N005651/1 18/01/2016 30/08/2017 £316,814
EP/N005651/2 Transfer EP/N005651/1 31/08/2017 17/01/2019 £158,617
 
Description Wireless power transmission (WPT) is envisioned to be a promising technology for prolonging the lifetime of wireless devices in energy-constrained networks. In this project, a general power beacon (PB) assisted multi-source transmission has been developed, where a practical source selection scheme with information transmission (IT) mode or non-IT mode is developed to maximize the transmission reliability. In the IT mode, a zero forcing (ZF) beamformed signal with no interference to the destination is transmitted at the multi-antenna PB to supply wireless energy for the sources, and bring non-negative effect to the destination. Among multiple sources, the energy-sufficient source with the best channel quality is selected for wireless information transmission (WIT), while the other sources remain for energy harvesting. In the non-IT mode, the equal power transmission is adopted at PB to focus on energy delivery. Using Markov chain theory, the energy arrival and departure of each finite-capacity storage at the source is characterized mathematically, and the comprehensive analytical expressions of the energy outage probability (EOP), the connection outage probability (COP), and the average transmission delay (ATD) are formulated and derived.
Exploitation Route We developed international collaboration through this project. We will take forward the findings to the industries for further exploitation.
Sectors Digital/Communication/Information Technologies (including Software)

Education
 
Description Simultaneous Wireless Information and Power transfer is highly suitable technique in massive IoT system where millions of sensors need to be powered. Our findings in this project have been disseminated in numerous top IEEE journals and IEEE Flagship conferences and attracted the interest of non-academic industries. Industries are specifically interested in modelling the energy arrival and departure of finite-capacity storage. In this project, we demonstrated this using Markov chain theory. We extended this modelling for massive IoT system. More specifically, we used AI (deep reinforcement learning) for maximizing random access success probability which was the unresolved challenge in 5G narrow band IoT system. These findings received industrial attention and received industrial funding to procced further on next generation 6G IoT systems. Research findings in this project used to develop AI enabled resource optimization algorithms for 6G massive Ultra Reliable Low Latency Communications (mURLLC) which will make high impact in the next generation 6G wireless Communications.
First Year Of Impact 2017
Sector Digital/Communication/Information Technologies (including Software)
Impact Types Economic
 
Description Collaborative Research on SWIPT 
Organisation National University of Singapore
Country Singapore 
Sector Academic/University 
PI Contribution We collaborated with international partner of this project at the National University of Singapore and developed new wireless energy transfer algorithms.
Collaborator Contribution Our partner at the National University of Singapore provided valuable comments/suggestions to develop the wireless energy transfer algorithms.
Impact H. Xing, K. Xin, K.K. Wong and A. Nallanathan,'' Optimizing DF Cognitive Radio Networks with Full-Duplex-Enabled Energy Access Points'', Vol. 16, No. 7, pp. 4683-4697, IEEE Trans. on Wireless Communications, July 2017
Start Year 2020