Open AccessTime allocation optimisation for multi‐antenna wireless information and power transfer with training and feedback
Author(s) -
Wu Yating,
Wang Tao,
Sun Yanzan,
Xu Chongbin
Publication year - 2017
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2016.0425
Subject(s) - channel state information , computer science , base station , transmission (telecommunications) , wireless , channel (broadcasting) , antenna (radio) , energy (signal processing) , maximum power transfer theorem , information transfer , power (physics) , transmitter power output , wireless power transfer , control theory (sociology) , mathematical optimization , phase (matter) , telecommunications , mathematics , statistics , transmitter , artificial intelligence , control (management) , physics , organic chemistry , chemistry , quantum mechanics
Time allocation optimisation for a multi‐antenna wireless energy and information transfer system is studied in this study. The terminal first harvests the wireless energy from the base station (BS) during phase I, and then uses the harvested energy to power its information transmission during phase II. Realistic channel state information (CSI) assumptions are made where the CSI at the BS is obtained via training and feedback and is subject to channel estimation error and quantisation error. Therefore, in addition to the tradeoff between information and power transfer, there is also a tradeoff in determining the overheads of training and feedback. To gain insight into this problem, the authors characterise the asymptotic behaviour of the parameters in the large system limit where the number of transmit antennas N t and transmission block length L tot tend to infinity with fixed ratio L tot / N t . The optimal overheads of training and feedback within each phase are first derived to maximise the harvested power and the information transmission rate, respectively. The optimised time allocation between phase I and phase II is then obtained by maximising the bounds of the effective average information transmission rate. Finally, numerical results are presented to verify the proposed theorems.