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Found 11 papers, 1 papers with code
Hierarchical Cognitive Spectrum Sharing in Space-Air-Ground Integrated Networks
Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network.
How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection
Extensive experimental results show that existing top OVD models all fail on the new tasks except for simple object types, demonstrating the value of the proposed dataset in pinpointing the weakness of current OVD models and guiding future research.
Ranked #1 on
Negation
on OVDEval
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling
Advertisers play an essential role in many e-commerce platforms like Taobao and Amazon.
Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks
In this paper, a novel framework is proposed for channel charting (CC)-aided localization in millimeter wave networks.
Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks
To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure.
SAR Image Despeckling Based on Convolutional Denoising Autoencoder
In this paper, the limited scale of dataset make a efficient exploration by using convolutioal denoising autoencoder (C-DAE) to reconstruct the speckle-free SAR images.
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV
In this paper, a novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance multi-user downlink transmissions over millimeter wave (mmWave) frequencies.
Distributional Reinforcement Learning
reinforcement-learning
+1
Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks
To exploit the full potential of SR, in this paper, we address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx's RF signals using reconfigurable intelligent surfaces.
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning
Furthermore, under limited knowledge of CSI, simulation results show that the proposed QR-DRL method, which learns a full distribution of the downlink rate, yields a better prediction accuracy and improves the downlink rate by 10% for online deployments, compared with a Q-learning baseline.
Reconfigurable Intelligent Surface Assisted MIMO Symbiotic Radio Networks
In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment.
Saikosaponins with similar structures but different mechanisms lead to combined hepatotoxicity
Previous studies have revealed that saikosaponins are the major types of components that contribute to the hepatotoxicity of Radix Bupleuri.
