Federated Learning-Assisted Predictive Beamforming for Extremely Large-Scale Antenna Array Systems With Rate-Splitting Multiple Access

Achieving perfect Channel State Information at the Transmitter (CSIT) is often infeasible in Extremely Large-scale Antenna Array (ELAA) systems due to user mobility and feedback/processing delay. This results in severe multi-user interference. Therefore, how to effectively and efficiently manage int...

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Veröffentlicht in:	IEEE journal of selected topics in signal processing Jg. 19; H. 2; S. 461 - 476
Hauptverfasser:	Zhang, Shengyu, Mao, Yijie, Chen, Zihan, Clerckx, Bruno, Quek, Tony Q.S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Antenna arrays Antennas Array signal processing Beamforming Computational modeling Design optimization Effectiveness Extremely large-scale antenna array (ELAA) Federated learning federated learning (FL) Feedback control systems Information systems Interference Multiple access Optimization Parallel processing patch-mixing Precoding Predictive models Protocols rate-splitting multiple access (RSMA) Segments Simulation Spatial resolution Splitting Training Transmitting antennas
ISSN:	1932-4553, 1941-0484
Online-Zugang:	Volltext
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