Information and Energy Transfer with Experimentally-Sampled Harvesting Functions
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This paper considers the problem of simultaneous information and energy transfer (SIET), where the energy harvesting function is only known experimentally at sample points, e.g., due to nonlinearities and parameter uncertainties in harvesting circuits. We investigate the performance loss due to this partial knowledge of the harvesting function in terms of transferred energy and information. In particular, we consider two cases, where experimental samples are either taken noiselessly or in the presence of noise. Using function approximation methods for noiseless samples and non-parametric regression methods for noisy samples, we show that the loss in energy transmission vanishes asymptotically as the number of samples increases. However, the loss in information rate does not always vanish. We show additional conditions for which the information loss vanishes: either operating in the interior of the energy domain or when continuity of the capacity curve at end-point is guaranteed. We further show the same principle applies in multicast settings such as medium access in the Wi-Fi protocol. We also consider the end-to-end source-channel communication problem under source distortion constraint and channel energy requirement, where distortion and harvesting functions both are known only at samples.
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