6G Fresnel Spot Beamfocusing using Large-Scale Metasurfaces: A Distributed DRL-Based Approach
Monemi, Mehdi; Fallah, Mohammad Amir; Rasti, Mehdi; Latva-Aho, Matti (2024-05-08)
IEEE
08.05.2024
M. Monemi, M. A. Fallah, M. Rasti and M. Latva-Aho, "6G Fresnel Spot Beamfocusing using Large-Scale Metasurfaces: A Distributed DRL-Based Approach," in IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 11670-11684, Dec. 2024, doi: 10.1109/TMC.2024.3398296
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202406204821
https://urn.fi/URN:NBN:fi:oulu-202406204821
Tiivistelmä
Abstract
We propose a novel approach to smart spot-beamforming (SBF) in the Fresnel zone leveraging extremely large-scale programmable metasurfaces (ELPMs). A smart SBF scheme aims to adaptively concentrate the aperture's radiating power exactly at a desired focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method. This offers numerous advantages for next-generation networks including ultra-high-speed wireless communication, location-based multiple access (LDMA), efficient wireless power transfer (WPT), interference mitigation, and improved information security. SBF necessitates ELPMs with precise channel state information (CSI) for all ELPM elements. However, obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate this by developing a novel CSI-independent ML scheme based on the TD3 deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is well-suited for relatively small-size arrays, the computational complexity is unaffordable for ELPMs. To overcome this limitation, we introduce a modular highly scalable structure composed of multiple sub-arrays, each equipped with a TD3-DRL optimizer. This setup enables collaborative optimization of the radiated power at the DFP, significantly reducing computational complexity while enhancing learning speed. The proposed structure's benefits in terms of 3D spot-like power distribution, convergence rate, and scalability are validated through simulation results.
We propose a novel approach to smart spot-beamforming (SBF) in the Fresnel zone leveraging extremely large-scale programmable metasurfaces (ELPMs). A smart SBF scheme aims to adaptively concentrate the aperture's radiating power exactly at a desired focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method. This offers numerous advantages for next-generation networks including ultra-high-speed wireless communication, location-based multiple access (LDMA), efficient wireless power transfer (WPT), interference mitigation, and improved information security. SBF necessitates ELPMs with precise channel state information (CSI) for all ELPM elements. However, obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate this by developing a novel CSI-independent ML scheme based on the TD3 deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is well-suited for relatively small-size arrays, the computational complexity is unaffordable for ELPMs. To overcome this limitation, we introduce a modular highly scalable structure composed of multiple sub-arrays, each equipped with a TD3-DRL optimizer. This setup enables collaborative optimization of the radiated power at the DFP, significantly reducing computational complexity while enhancing learning speed. The proposed structure's benefits in terms of 3D spot-like power distribution, convergence rate, and scalability are validated through simulation results.
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