Intelligence Reflecting Surface-Aided Integrated Data and Energy Networking Coexisting D2D Communications

TL;DR

This paper proposes an IRS-assisted integrated data and energy network with D2D coexistence, optimizing beamforming, power, and reflection to enhance throughput and energy harvesting in next-generation wireless systems.

Contribution

It introduces an IRS-aided framework for joint data and energy transfer with D2D coexistence, formulating and solving a max-min throughput optimization problem.

Findings

The proposed algorithm improves worst-case throughput.

IRS deployment effectively mitigates interference.

Simulation confirms the algorithm's efficiency.

Abstract

In this paper, we consider an integrated data and energy network and D2D communication coexistence (DED2D) system. The DED2D system allows a base station (BS) to transfer data to information-demanded users (IUs) and energy to energy-demanded users (EUs), i.e., using a time-fraction-based information and energy transfer (TFIET) scheme. Furthermore, the DED2D system enables D2D communications to share spectrum with the BS. Therefore, the DED2D system addresses the growth of energy and spectrum demands of the next generation networks. However, the interference caused by the D2D communications and propagation loss of wireless links can significantly degrade the data throughput of IUs. To deal with the issues, we propose to deploy an intelligent reflecting surface (IRS) in the DED2D system. Then, we formulate an optimization problem that aims to optimize the information beamformer for the IUs, energy beamformer for EUs, time fractions of the TFIET, transmit power of D2D transmitters, and reflection coefficients of the IRS to maximize IUs' worse throughput while satisfying the harvested energy requirement of EUs and D2D rate threshold. The max-min throughput optimization problem is computationally intractable, and we develop an alternating descent algorithm to resolve it with low computational complexity. The simulation results demonstrate the effectiveness of the proposed algorithm.