HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning

Hu J, Chen Z, Zheng T, Schober R, Luo J (2023)

Publication Type: Journal article

Publication year: 2023

Journal

IEEE Journal on Selected Areas in Communications Institute of Electrical and Electronics Engineers (IEEE)

Pages Range: 1-1

DOI: 10.1109/JSAC.2023.3322788

Abstract

Positioning is an essential service for various applications and is expected to be integrated with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular base stations (BSs) can be used to support this integration, the resulting precision is unsatisfactory due to the lack of precise control of the wireless signals. Recently, BSs adopting reconfigurable holographic surfaces (RHSs) have been advocated for positioning as RHSs’ large number of antenna elements enable generation of arbitrary and highly-focused signal beam patterns. However, existing designs face two major challenges: i) RHSs only have limited operating bandwidth, and ii) the positioning methods cannot adapt to the diverse environments encountered in practice. To overcome these challenges, we present HoloFed, a system providing high-precision environment-adaptive user positioning services by exploiting multi-band (MB)-RHS and federated learning (FL). For improving the positioning performance, a lower bound on the error variance is obtained and utilized for guiding MB-RHS’s digital and analog beamforming design. For better adaptability while preserving privacy, an FL framework is proposed for users to collaboratively train a position estimator, where we exploit the transfer learning technique to handle the lack of position labels of the users. Moreover, a scheduling algorithm for the BS to select which users train the position estimator is designed, jointly considering the convergence and efficiency of FL. Our performance evaluation based on simulations confirms that HoloFed achieves a 57%lower positioning error variance compared to a beam-scanning baseline and can effectively adapt to diverse environments.

Authors with CRIS profile

Robert Schober Lehrstuhl für Digitale Übertragung

Involved external institutions

Nanyang Technological University (NTU) SG Singapore (SG) Fudan University / 复旦大学 CN China (CN)

How to cite

APA:

Hu, J., Chen, Z., Zheng, T., Schober, R., & Luo, J. (2023). HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning. IEEE Journal on Selected Areas in Communications, 1-1. https://dx.doi.org/10.1109/JSAC.2023.3322788

MLA:

Hu, Jingzhi, et al. "HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning." IEEE Journal on Selected Areas in Communications (2023): 1-1.

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