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The capacity of a complex and discrete-time memoryless additive whiteGaussian noise (AWGN) channel under three constraints, namely, input averagepower, input amplitude and output delivered power is studied. The outputdelivered power constraint is modelled as the average of linear combination ofeven moments of the channel input being larger than a threshold. It is shownthat the capacity of an AWGN channel under transmit average power and receiverdelivered power constraints is the same as the capacity of an AWGN channelunder an average power constraint. However, depending on the two constraints,the capacity can be either achieved by a Gaussian distribution or arbitrarilyapproached by using time-sharing between a Gaussian distribution and On-OffKeying. As an application, a simultaneous wireless information and powertransfer (SWIPT) problem is studied, where an experimentally-validatednonlinear model of the harvester is used. It is shown that the delivered powerdepends on higher order moments of the channel input. Two inner bounds, onebased on complex Gaussian inputs and the other based on further restricting thedelivered power are obtained for the Rate-Power (RP) region. For Gaussianinputs, the optimal inputs are zero mean and a tradeoff between transmittedinformation and delivered power is recognized by considering asymmetric powerallocations between inphase and quadrature subchannels. Through numericalalgorithms, it is observed that input distributions (obtained by restrictingthe delivered power) attain larger RP region compared to Gaussian inputcounterparts.

On Capacity-Achieving Distributions for Complex AWGN Channels Under Nonlinear Power Constraints and their Applications to SWIPT