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Found 8 papers, 2 papers with code
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
The transformer model attends to the regions that are relevant for path loss prediction and, therefore, scales efficiently to maps of different size.
Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation
More importantly, our proposed framework has the potential to be trained online using real noisy pilot measurements, is not restricted to a specific channel model and can even be utilized for a federated OTA design of a dataset generator from noisy data.
Distributed Proximal Policy Optimization for Contention-Based Spectrum Access
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access that go beyond traditional carrier sensing.
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access.
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning
The use of unlicensed spectrum for cellular systems to mitigate spectrum scarcity has led to the development of intelligent adaptive approaches to spectrum access that improve upon traditional carrier sensing and listen-before-talk methods.
DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems
In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802. 11ax based orthogonal frequency division multiplexing (OFDM) receivers.
High Dimensional Channel Estimation Using Deep Generative Networks
This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network.
Deep Stock Predictions
Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems.
