Joint Beamforming and Position Optimization for IRS-Aided SWIPT with Movable Antennas

TL;DR

The paper addresses the challenge of enhancing SWIPT performance in IoT networks by integrating intelligent reflecting surfaces (IRS) and movable antennas (MAs) at the base station. It develops a joint optimization framework to maximize the weighted sum-rate of information decoding receivers while enforcing power-harvesting constraints at energy receivers, through BS active beamforming, IRS passive beamforming, and MA positioning. A novel algorithmic stack combining WMMSE, BCD, MM, and PDD, along with a feasibility characterization method, enables tractable solutions to the nonconvex problem. Numerical results show significant gains from jointly optimizing IRS and MA configurations, with IRS phase optimization often delivering larger improvements than MA repositioning in the studied setup. The work provides a practical pathway to deploy IRS and MA technologies for robust, energy-aware wireless networks.

Abstract

Simultaneous wireless information and power transfer (SWIPT) has been envisioned as a promising technology to support ubiquitous connectivity and reliable sustainability in Internet-of-Things (IoT) networks, which, however, generally suffers from severe attenuation caused by long distance propagation, leading to inefficient wireless power transfer (WPT) for energy harvesting receivers (EHRs). This paper proposes to introduce emerging intelligent reflecting surface (IRS) and movable antenna (MA) technologies into SWIPT systems aiming at enhancing information transmission for information decoding receivers (IDRs) and improving receive power of EHRs. We consider to maximize the weighted sum-rate of IDRs via jointly optimizing the active and passive beamforming at the base station (BS) and IRS, respectively, as well as the positions of MAs, while guaranteeing the requirements of all EHRs. To tackle this challenging task due to the non-convexity of associated optimization, we develop an efficient algorithm combining weighted minimal mean square error (WMMSE), block coordinate descent (BCD), majorization-minimization (MM), and penalty duality decomposition (PDD) frameworks. Besides, we present a feasibility characterization method to examine the achievability of EHRs' requirements. Simulation results demonstrate the significant benefits of our proposed solutions. Particularly, the optimized IRS configuration may exhibit higher performance gain than MA counterpart under our considered scenario.

Figures (7)

• Figure : Fig. 1. IRS-aided SWIPT system with MAs.
• Figure : Fig. 2. Convergence behavior of Alg. 2.
• Figure : Fig. 3. Convergence behavior of Alg. 4.
• Figure : Fig. 4. Impact of BS power budget $P_{\mathrm{B}}$ on sum-rate.
• Figure : Fig. 5. Impact of normalized BS array size on sum-rate.