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Found 6 papers, 1 papers with code
Centralized and Decentralized ML-Enabled Integrated Terrestrial and Non-Terrestrial Networks
Non-terrestrial networks (NTNs) are a critical enabler of the persistent connectivity vision of sixth-generation networks, as they can service areas where terrestrial infrastructure falls short.
Identification of Distorted RF Components via Deep Multi-Task Learning
The identification of these components can be done through observation and analysis of constellation diagrams.
Joint Estimation of Multiple RF Impairments Using Deep Multi-Task Learning
Most of the designers rely on simplified closed-form models to estimate these impairments.
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond
In this paper, we propose an algorithm for estimating the composite sparsity lying in multiple domains defined by learned dictionaries.
Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations
Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels.
Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO
This beamspace transformation is equivalent to performing a Fourier transformation of the channel.
