Open AccessJoint design of interference alignment and power splitting in SWIPT networks
Author(s) -
Chen Yan,
Liu Chen,
Fu Youhua,
Song Yunchao,
Sun Wenfeng
Publication year - 2020
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.2019.0674
Subject(s) - weighting , maximum power transfer theorem , computer science , interference (communication) , joint (building) , wireless , power (physics) , transmitter power output , signal to interference plus noise ratio , mathematical optimization , signal to noise ratio (imaging) , algorithm , control theory (sociology) , transmitter , mathematics , telecommunications , artificial intelligence , engineering , medicine , architectural engineering , channel (broadcasting) , physics , control (management) , quantum mechanics , radiology
In this study, the authors investigate the joint design problem of interference alignment (IA) and power splitting (PS) in simultaneous wireless information and power transfer (SWIPT) networks. The existing joint design method is generally divided into two steps. First, IA is performed, and then the PS ratio is optimised. To obtain IA and SWIPT, a new joint design multi‐objective alternating optimisation (MOAO) algorithm is proposed. Different from the existing joint design method, the proposed MOAO algorithm not only maximises the sum harvested power after PS, but also maximises the sum signal‐to‐intereference‐plus‐noise ratio (SINR) by jointly designing the transmitting precoding matrices, the receiving interference suppression matrices, and the PS ratio. For this multi‐objective optimisation problem, a weighted optimisation method is proposed, which compromises between maximising the sum harvested power and maximising the sum SINR. Simulation validates that the sum achievable rate and sum harvested power of the proposed MOAO algorithm are significantly superior to those of the current scheme under the reasonable weighting coefficients.