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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.
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.
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.
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.
