Derivative-Free Optimization-Empowered Wireless Channel Reconfiguration for 6G

TL;DR

This paper proposes using derivative-free optimization techniques to directly reconfigure wireless channels in 6G systems, bypassing complex modeling and estimation of environmental parameters.

Contribution

It introduces a zeroth-order optimization approach for real-time reconfiguration of reconfigurable antennas, demonstrating its advantages over traditional methods.

Findings

ZO optimization outperforms state-of-the-art techniques in RIS systems.

The approach simplifies channel reconfiguration without explicit environmental modeling.

Case studies validate the effectiveness of the proposed method.

Abstract

Reconfigurable antennas, including reconfigurable intelligent surface (RIS), movable antenna (MA), fluid antenna (FA), and other advanced antenna techniques, have been studied extensively in the context of reshaping wireless propagation environments for 6G and beyond wireless communications. Nevertheless, how to reconfigure/optimize the real-time controllable coefficients to achieve a favorable end-to-end wireless channel remains a substantial challenge, as it usually requires accurate modeling of the complex interaction between the reconfigurable devices and the electromagnetic waves, as well as knowledge of implicit channel propagation parameters. In this paper, we introduce a derivative-free optimization (a.k.a., zeroth-order (ZO) optimization) technique to directly optimize reconfigurable coefficients to shape the wireless end-to-end channel, without the need of channel modeling and estimation of the implicit environmental propagation parameters. We present the fundamental principles of ZO optimization and discuss its potential advantages in wireless channel reconfiguration. Two case studies for RIS and movable antenna-enabled single-input single-output (SISO) systems are provided to show the superiority of ZO-based methods as compared to state-of-the-art techniques. Finally, we outline promising future research directions and offer concluding insights on derivative-free optimization for reconfigurable antenna technologies.