Enhanced IoT Batteryless D2D Communications Using Reconfigurable Intelligent Surfaces

TL;DR

This paper proposes a RIS-assisted wireless network to enable batteryless IoT sensors to harvest energy from base station signals, maximizing device-to-device communication in urban environments through an optimized iterative algorithm.

Contribution

It introduces a novel RIS-based energy harvesting framework for batteryless IoT sensors, optimizing bit transmission in complex urban multi-path scenarios.

Findings

RIS panel enhances channel performance for IoT systems.

The proposed algorithm improves energy harvesting and data transmission efficiency.

Simulation validates the model's effectiveness in urban microchannel environments.

Abstract

Recent research on reconfigurable intelligent surfaces (RIS) suggests that the RIS panel, containing passive elements, enhances channel performance for the internet of things (IoT) systems by reflecting transmitted signals to the receiving nodes. This paper investigates RIS panel assisted-wireless network to instigate minimal base station (BS) transmit power in the form of energy harvesting for batteryless IoT sensors to maximize bits transmission in the significant multi-path environment, such as urban areas. Batteryless IoT sensors harvest energy through the RIS panel from external sources, such as from nearby BS radio frequency (RF) signal in the first optimal time frame, for a given time frame. The bits transmission among IoT sensors, followed by a device-to-device (D2D) communications protocol, is maximized using harvested energy in the final optimal time frame. The bits transmission is at least equal to the number of bits sampled by the IoT sensor. We formulate a non-convex mixed-integer non-linear problem to maximize the number of communicating bits subject to energy harvesting from BS RF signals, RIS panel energy consumption, and required time. We propose a robust solution by presenting an iterative algorithm. We perform extensive simulation results based on the 3GPP Urban Micro channel model to validate our model.