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Found 8 papers, 0 papers with code
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning
To bridge this gap, we propose a novel method called Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning (DeepSafeMPC).
Model Predictive Control
Multi-agent Reinforcement Learning
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Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance.
Imaging Using Millimeter Wave Communication Networks: A Bonus SAR
In the next generations of cellular communication networks, higher density of base stations and higher frequency bands will be adopted.
Taming and Leveraging Interference in Mobile Radar Networks
Mobile radar networks, such as autonomous driving systems, are subject to the severe challenge of mutual interference.
Joint Communications and Sensing: A Comprehensive Study
The conflict between communications and sensing is analyzed in terms of bandwidth and power.
LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS).
Analysis on the Nonlinear Dynamics of Deep Neural Networks: Topological Entropy and Chaos
The theoretical explanation for deep neural network (DNN) is still an open problem.
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis
However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulation networks at the right scales.
