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Found 18 papers, 9 papers with code
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios
The solutions developed so far, however, have mainly considered single-candidate scenarios, i. e., scenarios with a single candidate user in the visual scene, and were evaluated using synthetic datasets.
Progressive with Purpose: Guiding Progressive Inpainting DNNs through Context and Structure
Stacking GLE modules enables the network to extract image features from different image frequency components.
Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration
In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line-of-sight (LOS) link blockages.
Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence
To demonstrate the potential of the proposed framework, a wireless network scenario with two coexisting URLL and eMBB services is considered, and two deep learning algorithms are designed to utilize RGB video frames and predict incoming service type and its request time.
Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction
To integrate RISs into THz drone communications, we propose a novel deep learning solution based on a recurrent neural network, namely the Gated Recurrent Unit (GRU), that proactively predicts the serving base station/RIS and the serving beam for each drone based on the prior observations of drone location/beam trajectories.
Information Theory Networking and Internet Architecture Information Theory
Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff
This paper presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by RGB cameras deployed at the base stations.
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems.
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
To evaluate our proposed approach, we build a mmWave communication setup with a moving blockage and collect a dataset of received power sequences.
Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams that Adapt to Deployment and Hardware
To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics.
Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements.
Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO
This leads to high beam training overhead and loss in the achievable beamforming gains.
Information Theory Signal Processing Information Theory
ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions
These capabilities have the potential of reliably supporting highly-mobile applications such as vehicular/drone communications and wireless virtual/augmented reality in mmWave and terahertz systems.
ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications
It is developed to be a parametric, systematic, and scalable data generation framework.
Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction
This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges.
Information Theory Signal Processing Information Theory
Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots
This leads to the interesting, and \textit{counter-intuitive}, observation that when more antennas are employed by the massive MIMO base station, our proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length.
Information Theory Signal Processing Information Theory
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels
Prior work, however, has focused on extracting spatial channel characteristics at the sub-6GHz band first and then use them to reduce the mmWave beam training overhead.
Information Theory Signal Processing Information Theory
Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning
We show that the achievable rates of the proposed compressive sensing and deep learning solutions approach the upper bound, that assumes perfect channel knowledge, with negligible training overhead and with less than 1% of the elements being active.
Information Theory Signal Processing Information Theory
Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis.
Ranked #18 on
Image Dehazing
on SOTS Outdoor
