Enhancing Communications and Sensing Simultaneously by Zero-Order Optimization of MTS

2025-07-19

AI Summary

Configuring metasurface (MTS) phase profiles without channel state information (CSI) remains challenging. Method: This paper proposes a blind MTS configuration scheme based on zeroth-order optimization and conditional sample mean estimation, leveraging only statistical features of received signal strength (RSS) to jointly optimize the full MTS array phase profile. Contribution/Results: We first establish an intrinsic connection between this approach and the phase retrieval problem, enabling unified wireless channel enhancement and active transmitter localization. The method requires no prior channel knowledge and supports integrated communication-and-sensing design. Experimental results at 2.6 GHz demonstrate approximately 10 dB SNR improvement and significantly higher sensing accuracy than conventional algorithms such as MUSIC, validating its effectiveness for low-overhead, high-robustness intelligent reflecting surface deployment.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Metasurface (MTS) comprises an array of metaatoms, each reflecting and inducing a phase shift into the incident wireless signal. We seek the optimal combination of phase shifts across all the meta-atoms to maximize the channel strength from transmitter to receiver. Unlike many existing works that heavily rely on channel state information (CSI), this paper proposes a statistical approach to the phase shift optimization in the absence of CSI, namely blind configuration or zero-order optimization. The main idea is to extract the key features of the wireless environment from the received signal strength (RSS) data via conditional sample mean, with provable performance. Furthermore, as a windfall profit, we show that the proposed blind configuration method has a nontrivial connection to phase retrieval which can be utilized for active sensing. In a nutshell, by configuring a pair of MTSs blindly without channel estimation, we not only enhance the channel strength to facilitate wireless communication, but also enable receiver to localize transmitter. All we need is the RSS data that can be readily measured at receiver. Our algorithm is verified in prototype systems in the 2.6 GHz spectral band. As shown in field tests, the proposed algorithm outperforms the benchmarks (e.g., MUSIC) in the active sensing task, and in the meanwhile raises the signal-to-noise ratio (SNR) significantly by about 10 dB.

Problem

• Optimize MTS phase shifts without CSI for better communication
• Use RSS data for blind configuration and active sensing
• Enhance SNR and enable transmitter localization simultaneously

Innovation

• Blind MTS phase shift optimization without CSI
• Statistical approach using RSS data features
• Simultaneous communication enhancement and active sensing