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Outage Analysis of Wireless-Powered Relaying MIMO Systems with Non-Linear Energy Harvesters and Imperfect CSI
Author(s) -
Jiliang Zhang,
Gaofeng Pan
Publication year - 2016
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2016.2617893
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In this paper, we investigate a wireless-powered dual-hop relaying multiple-input multiple-output system, which consists of a multi-antenna source (S) node, a multi-antenna destination (D) node, and $N (N>1)$ single-antenna wireless-powered relaying nodes. At each relay, a power splitting receiver is applied to process the received signal for information decoding and energy harvesting simultaneously, and a decode-and-forward scheme is adopted to forward the processed information. Furthermore, the energy harvester at each relay is assumed to be non-linear with a saturation threshold to limit the power level of the energy. Assuming imperfect channel state information is available both at S and D, outage performance is investigated when S adopts transmit antenna selection in the presence of feedback delay and D performs maximal ratio combining technique to deal with the multiple copies of signals with channel estimation errors. Taking into account a $K$ th best relay selection criterion, which results in the $K$ th best performance in terms of outage probability for the source-relay-destination link, an analytical expression for OP is derived. Monte Carlo simulation results are presented to verify the accuracy of the derived analytical model.

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