NDLI: Energy states aided relay selection and optimal power allocation for cognitive relaying networks

Content Provider	IET Digital Library
Author	Tang, Dong Jiang, Dandan Huang, Gaofei Xia, Minghua
Abstract	Energy harvesting (EH) is a promising technique for cognitive relaying transmission (CRT) where secondary users (SUs) and relaying nodes do not have a constant power supply each. Unlike conventional CRT where the end-to-end data rate is usually maximised without taking into account the energy consumption at the source and relay, in this study, the energy consumption is characterised by means of energy efficiency, defined as the achievable data rate per Joule. In particular, the energy states at each node (either at a SU or a relay) is modelled as a finite-state Markov chain and the transmit power at a node is optimally allocated by jointly accounting for the interference threshold prescribed by primary users (PUs), the maximum allowable transmit power and the harvested energy at the node. To maximise the energy efficiency, a best relay selection criterion is proposed and the subsequent optimal transmit power allocation is initially formulated as a non-linear fractional programming problem and, then, equivalently transformed into a parametric programming problem and, finally, solved analytically by using the classic Karush–Kuhn–Tucker conditions. With extensive Monte-Carlo simulation results, the effectiveness of the proposed relay selection algorithm and corresponding optimal power allocation strategy are corroborated.
Starting Page	1045
Ending Page	1052
Page Count	8
ISSN	17518628
Volume Number	11
e-ISSN	17518636
Issue Number	Issue 7, May (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/11/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2016.0832
Journal	IET Communications
Publisher Date	2017-01-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classic Karush-Kuhn-Tucker Condition Cognitive Relaying Transmission CRT End-to-end Data Rate Energy Consumption Energy Efficiency Energy Harvesting Energy State Aided Relay Selection Extensive Monte-Carlo Simulation Finite-state Markov Chain Interference Threshold Markov Processes Maximum Allowable Transmit Power Monte Carlo Method Nonlinear Fractional Programming Problem Nonlinear Programming Optimisation Technique Parametric Programming Problem Primary User PU Radio Link And Equipment Relay Network Relaying Node Secondary User Subsequent Optimal Transmit Power Allocation Telecommunication
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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