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Found 5 papers, 2 papers with code
Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems
This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission.
Hierarchical Beam Alignment for Millimeter-Wave Communication Systems: A Deep Learning Approach
Fast and precise beam alignment is crucial for high-quality data transmission in millimeter-wave (mmWave) communication systems, where large-scale antenna arrays are utilized to overcome the severe propagation loss.
Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems
In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner.
LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches
With the rapid development of satellite communication technologies, the space-based access network has been envisioned as a promising complementary part of the future 6G network.
Image Restoration Using Deep Regulated Convolutional Networks
While the depth of convolutional neural networks has attracted substantial attention in the deep learning research, the width of these networks has recently received greater interest.
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Grayscale Image Denoising
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