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Found 10 papers, 1 papers with code
Theoretical Analysis of Deep Neural Networks in Physical Layer Communication
In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity.
A Low Complexity Learning-based Channel Estimation for OFDM Systems with Online Training
Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission.
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks
With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Opening the Black Box of Deep Neural Networks in Physical Layer Communication
Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems.
Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks
Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks.
Numerology Selection for OFDM Systems Based on Deep Neural Networks
In this letter, we propose a deep neutral network (DNN) approach for numerology selection of OFDM systems.
Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach
Multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is a key technology component in the evolution towards cognitive radio (CR) in next-generation communication in which the accuracy of timing and frequency synchronization significantly impacts the overall system performance.
A Cyber Physical System Framework for UAV Communications
Diverse applications have witnessed the prevalence of unmanned aerial vehicles (UAVs) due to their agility and versatility.
Performance Analysis on Machine Learning-Based Channel Estimation
In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.
Deep Neural Network Aided Scenario Identification in Wireless Multi-path Fading Channels
This letter illustrates our preliminary works in deep nerual network (DNN) for wireless communication scenario identification in wireless multi-path fading channels.
