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Federated Learning-Based Joint Radar-Communication mmWave Beamtracking with Imperfect CSI for V2X Communications
Abstract
Robust beamforming is used to ensure communication quality, particularly for vehicle-to-everything (V2X) communications. Moreover, millimeter waves (mmWaves) are used in a new era of communication, providing a high data rate and high spectrum efficiency. However, mmWave links are prone to short-range communication because of serious free-space path loss and blockage by obstacles. In addition, owing to constraints on bandwidth and privacy considerations, it is computationally difficult to transmit the entire dataset between vehicle users and road side units (RSUs). As a result, FL-BT, a federated learning (FL)-based joint radar and communication mmWave beamtracking with imperfect channel state information (CSI) for V2X communications, is proposed. FL-BT is a federated module at vehicle user (local clients) and an RSU (main server) consisting of support vector regression (SVR) along with an unscented Kalman filter (UKF) that predicts the distribution of future channel behavior based on a sequence of input signals available. The simulation results demonstrate that the proposed FL-BT approach offers significant improvements in V2X communication performance by incorporating the UKF and SVR in a state-space model for precise future channel-value prediction and a federated learning framework for handling bandwidth and privacy considerations. This leads to a more robust and accurate beamtracking performance compared to traditional beamtracking methods.
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By keeping data on-site, federated learning supports compliance with privacy laws, facilitating broader collaboration across healthcare entities.
“Regulatory Compliance: By keeping data on-site, FL supports compliance with privacy laws, facilitating broader collaboration across healthcare entities [48].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [48]
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Metadata
- Authors
- Bhardwaj, S., & Kim, D.-S.
- Affiliations
- Kumoh National Institute of Technology (KR)
- Venue
- 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)
- Publisher
- IEEE
- Published
- 2023-07-04
- Pages
- 201-206
- OpenAlex
- W4385624566
- Cited by
- 6
- References
- 26
- Citations by year
- 2025: 1 · 2024: 5
- FWCI
- 2.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Ubiquitous and Future Networks (publishedVersion)
- Funders
- Ministry of Education
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Keywords
- federated learning, beam tracking, millimeter-wave communication, vehicle-to-everything communication, imperfect channel state information, roadside units, support vector regression, unscented Kalman filter, channel prediction, privacy preservation, bandwidth constraints, joint radar-communication
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