Design and Analysis of SWIPT with Safety Constraints

TL;DR

This paper develops a comprehensive framework for designing and analyzing SWIPT systems that comply with international safety regulations, using deep learning for robust beamforming and analytical methods for large-scale performance evaluation.

Contribution

It introduces a novel deep learning-based beamforming design for SWIPT under safety constraints and provides a detailed analytical performance analysis of large-scale systems.

Findings

Deep learning improves beamforming robustness under safety constraints.

SWIPT systems can achieve effective information and energy coverage within safety limits.

Insights into optimal SWIPT design considering health and safety regulations.

Abstract

Simultaneous wireless information and power transfer (SWIPT) has long been proposed as a key solution for charging and communicating with low-cost and low-power devices. However, the employment of radio frequency (RF) signals for information/power transfer needs to comply with international health and safety regulations. In this paper, we provide a complete framework for the design and analysis of far-field SWIPT under safety constraints. In particular, we deal with two RF exposure regulations, namely, the specific absorption rate (SAR) and the maximum permissible exposure (MPE). The state-of-the-art regarding SAR and MPE is outlined together with a description as to how these can be modeled in the context of communication networks. We propose a deep learning approach for the design of robust beamforming subject to specific information, energy harvesting and SAR constraints. Furthermore, we present a thorough analytical study for the performance of large-scale SWIPT systems, in terms of information and energy coverage under MPE constraints. This work provides insights with regards to the optimal SWIPT design as well as the potentials from the proper development of SWIPT systems under health and safety restrictions.