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SWIPT Signalling over Complex AWGN Channels with Two Nonlinear Energy Harvester Models

Varasteh, Morteza and Rassouli, Borzoo and Joudeh, Hamdi and Clerckx, Bruno (2018) SWIPT Signalling over Complex AWGN Channels with Two Nonlinear Energy Harvester Models. In: 2018 IEEE International Symposium on Information Theory (ISIT), 2018-06-17 - 2018-06-22, Vail, CO, USA.

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Abstract

Simultaneous Wireless Information and Power Transfer (SWIPT) is subject to nonlinearity at the energy harvester that leads to significant changes to transmit signal designs compared to conventional wireless communications. In this paper, the capacity of a discrete time, memoryless and complex Additive White Gaussian Noise (AWGN) channel in the presence of a nonlinear energy harvester at the receiver is studied. Considering the two common nonlinear energy harvester models introduced in the literature, two sets of constraints are considered. First the capacity is studied under average power (AP), peak amplitude (PA) and receiver delivery power (RDP) constraints. The RDP constraint is modelled as a linear combination of even-moment statistics of the channel input being larger than a threshold. It is shown that the capacity of an AWGN channel under AP and RDP constraints is the same as the capacity of an AWGN channel under an AP constraint, however, depending on the two constraints, it can be either achieved or arbitrarily approached. It is also shown that under AP, PA and RDP constraints, the amplitude of the optimal inputs is discrete with a finite number of mass points. Next, the capacity is studied under AP, PA and output outage probability (OOP) constraints. OOP is modelled as satisfying a certain probability inequality for the amplitude of the received signal being outside of a given interval. Similarly, it is shown that the amplitude of the optimal input is discrete with a finite number of mass points.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2018 IEEE International Symposium on Information Theory (ISIT)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 14 Feb 2020 17:04
Last Modified: 14 Feb 2020 17:15
URI: http://repository.essex.ac.uk/id/eprint/25517

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