
Robust beamforming and power splitting design in MISO SWIPT downlink system
Author(s) -
Chu Zheng,
Zhu Zhengyu,
Xiang Weichen,
Hussein Jamal
Publication year - 2016
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.2015.0475
Subject(s) - beamforming , computer science , telecommunications link , mathematical optimization , transmitter power output , channel state information , maximum power transfer theorem , convex optimization , covariance , wireless , fading , mimo , transmitter , control theory (sociology) , channel (broadcasting) , mathematics , power (physics) , regular polygon , telecommunications , statistics , physics , quantum mechanics , geometry , control (management) , artificial intelligence
In this study, the authors consider simultaneous wireless information and power transfer (SWIPT) in a multiple‐input‐single‐output (MISO) downlink system, where power splitting scheme is considered for each user of this system. Since channel state information of each user cannot be available at the transmitter, robust beamforming for SWIPT in the MISO downlink system is presented by incorporating with different types of channel uncertainty models. The authors first formulate the robust power minimisation problem subject to the signal‐to‐inference‐plus‐noise ratio (SINR) and energy harvesting (EH) constraints by incorporating two Gaussian channel uncertainties. The original problem is not convex in terms of channel uncertainties, and cannot be solved efficiently. The authors employ the well‐known Bernstein‐type inequality and Gaussian error function to make probability based constraints tractable, respectively, in order to recast the original problem as the convex form. Moreover, the robust power minimisation problem with the probability based SINR and EH constraints is formulated by incorporating random distribution with known error mean and covariance matrix. By exploiting conditional value‐at‐risk functional and semi‐definite relaxation, this optimisation problem is relaxed as the convex form. Finally, numerical results are provided to validate the performance of these proposed robust schemes.