End-to-End Learning of Beam Probing and RSSI-Based Multi-User Hybrid Precoding Design

This paper presents an end-to-end (E2E) autoencoder learning framework that relies on unsupervised deep learning for the joint design of millimeter wave (mmWave) probing beams and hybrid precoding matrices in multi-user communication systems. Our model utilizes prior channel observations to achieve...

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Bibliographic Details

Published in:	IEEE Global Communications Conference (Online) pp. 1948 - 1953
Main Authors:	Abdallah, Asmaa, Celik, Abdulkadir, Alkhateeb, Ahmed, Eltawil, Ahmed M.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 08.12.2024
Subjects:	Array signal processing Autoencoders beamforming codebook end-to-end (E2E) Interference millimeter wave (mmWave) Millimeter wave communication Millimeter wave technology multi-user hybrid beamforming Neural networks Precoding Predictive models Radio frequency unsupervised learning Vectors
ISSN:	2576-6813
Online Access:	Get full text
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