2017/12/04 by Morteza Varasteh, Varasteh, Morteza, Borzoo Rassouli +3
Engineering · #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #Information Theory (cs.IT) #Innovative Energy Harvesting Technologies #Wireless Power Transfer Systems
paper · pdf · doi:10.48550/arxiv.1712.01226
openalex publication_date 2017/12/04 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
The capacity of a complex and discrete-time memoryless additive white\nGaussian noise (AWGN) channel under three constraints, namely, input average\npower, input amplitude and output delivered power is studied. The output\ndelivered power constraint is modelled as the average of linear combination of\neven moments of the channel input being larger than a threshold. It is shown\nthat the capacity of an AWGN channel under transmit average power and receiver\ndelivered power constraints is the same as the capacity of an AWGN channel\nunder an average power constraint. However, depending on the two constraints,\nthe capacity can be either achieved by a Gaussian distribution or arbitrarily\napproached by using time-sharing between a Gaussian distribution and On-Off\nKeying. As an application, a simultaneous wireless information and power\ntransfer (SWIPT) problem is studied, where an experimentally-validated\nnonlinear model of the harvester is used. It is shown that the delivered power\ndepends on higher order moments of the channel input. Two inner bounds, one\nbased on complex Gaussian inputs and the other based on further restricting the\ndelivered power are obtained for the Rate-Power (RP) region. For Gaussian\ninputs, the optimal inputs are zero mean and a tradeoff between transmitted\ninformation and delivered power is recognized by considering asymmetric power\nallocations between inphase and quadrature subchannels. Through numerical\nalgorithms, it is observed that input distributions (obtained by restricting\nthe delivered power) attain larger RP region compared to Gaussian input\ncounterparts.\n