Information and Energy Transmission with Experimentally-Sampled Harvesting Functions

TL;DR

This paper analyzes the impact of experimentally-sampled harvesting functions on simultaneous information and energy transmission, showing that performance loss diminishes with more samples and applies to multicast and source-channel scenarios.

Contribution

It introduces a framework for analyzing SIET with sampled harvesting functions, demonstrating asymptotic vanishing loss and extending to multicast and source-channel problems.

Findings

Worst energy transmission loss vanishes asymptotically with more samples.

Information rate loss diminishes inside the energy domain but not always at maximum energy.

The approach applies to multicast and source-channel communication with sampled functions.

Abstract

This paper considers the problem of simultaneous information and energy transmission (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 transmitted energy and information. In particular, we assume harvesting functions are a subclass of Sobolev space and consider two cases, where experimental samples are either taken noiselessly or in the presence of noise. Using constructive function approximation and regression methods for noiseless and noisy samples respectively, we show that the worst loss in energy transmission vanishes asymptotically as the number of samples increases. Similarly, the worst loss in information rate vanishes in the interior of the energy domain, however, does not always vanish at maximal energy. 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.