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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Summary: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 two main objectives: designing a compact set of probing beams and predicting off-grid radio frequency (RF) beamforming vectors. The E2E learning framework optimizes probing beams in an unsupervised manner, concentrating sensing power on promising spatial directions based on the environment. To this aim, we develop a neural network architecture respecting RF chain constraints and model received signal strength (RSS) using complex-valued convolutional layers. The autoencoder is trained to directly produce RF beamforming vectors for hybrid architectures based on projected RSS indicators (RSSIs). Once RF beamforming vectors for multi-users are predicted, baseband digital precoders are designed by accounting for multi-user interference. The autoencoder neural network is trained E2E in an unsupervised manner with a customized loss function aimed at maximizing RSS. In a system with 64 antennas, 4 RF chains, and 4 users, our approach requires only 8 probing beams to design RF beamforming vectors, compared to the conventional predefined codebooks with 64 or 128 beams.
ISSN:2576-6813
DOI:10.1109/GLOBECOM52923.2024.10901513