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Found 60 papers, 18 papers with code
Digital Twins for Supporting AI Research with Autonomous Vehicle Networks
Digital twins (DTs), which are virtual environments that simulate, predict, and optimize the performance of their physical counterparts, are envisioned to be essential technologies for advancing next-generation wireless networks.
Joint and Robust Beamforming Framework for Integrated Sensing and Communication Systems
In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user and multi-target ISAC systems.
Integrated Imaging and Communication with Reconfigurable Intelligent Surfaces
In particular, using the RIS as a wireless imaging device, our system constructs the scene depth map of the environment, including the mobile user.
ISAC with Backscattering RFID Tags: Joint Beamforming Design
Then, we study a joint beamforming design problem with the goal of minimizing the total transmit power while satisfying the tag detection and communication requirements.
Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration
The developed solution is evaluated on a real-world multi-modal mmWave V2V communication dataset comprising co-existing 360 camera and mmWave beam training data.
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.
Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration
Utilizing radar sensing for assisting communication has attracted increasing interest thanks to its potential in dynamic environments.
Real-World Evaluation of Full-Duplex Millimeter Wave Communication Systems
Noteworthy strides continue to be made in the development of full-duplex millimeter wave (mmWave) communication systems, but most of this progress has been built on theoretical models and validated through simulation.
Vision Guided MIMO Radar Beamforming for Enhanced Vital Signs Detection in Crowds
Radar as a remote sensing technology has been used to analyze human activity for decades.
Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond
This article presents a vision where \textit{real-time} digital twins of the physical wireless environments are continuously updated using multi-modal sensing data from the distributed infrastructure and user devices, and are used to make communication and sensing decisions.
Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?
To address this challenge, we propose a novel direction that utilizes digital replicas of the physical world to reduce or even eliminate the MIMO channel acquisition overhead.
Sensing Aided Reconfigurable Intelligent Surfaces for 3GPP 5G Transparent Operation
Can reconfigurable intelligent surfaces (RISs) operate in a standalone mode that is completely transparent to the 3GPP 5G initial access process?
Device-Agnostic Millimeter Wave Beam Selection using Machine Learning
However, this design requires a specific model for each user-device beam codebook, where a model learned for a device with a particular codebook can not be reused for another device with a different codebook.
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
Given this future blockage prediction capability, the paper also shows that the developed solutions can achieve an order of magnitude saving in network latency, which further highlights the potential of the developed blockage prediction solutions for wireless networks.
DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset
This article presents the DeepSense 6G dataset, which is a large-scale dataset based on real-world measurements of co-existing multi-modal sensing and communication data.
Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth Estimation
In this paper, we propose to employ RIS-aided wireless sensing systems for scene depth estimation.
Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction
Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management.
Camera Aided Reconfigurable Intelligent Surfaces: Computer Vision Based Fast Beam Selection
Reconfigurable intelligent surfaces (RISs) have attracted increasing interest due to their ability to improve the coverage, reliability, and energy efficiency of millimeter wave (mmWave) communication systems.
User Identification: A Key Enabler for Multi-User Vision-Aided Communications
In this paper, we define the \textit{user identification} task as a key enabler for realistic vision-aided communication systems that can operate in crowded scenarios and support multi-user applications.
Multi-Modal Beam Prediction Challenge 2022: Towards Generalization
In this paper, along with the detailed descriptions of the problem statement and the development dataset, we provide a baseline solution that utilizes the user position data to predict the optimal beam indices.
Online Beam Learning with Interference Nulling for Millimeter Wave MIMO Systems
Furthermore, a hardware proof-of-concept prototype based on mmWave phased arrays is built and used to implement and evaluate the developed online beam learning solutions in realistic scenarios.
Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles
The article also presents real-world results for some of these machine learning roles based on the large-scale real-world dataset DeepSense 6G, which could be adopted in investigating a wide range of integrated sensing and communication problems.
Computer Vision Aided mmWave Beam Alignment in V2X Communications
Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status.
Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction
Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power.
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
Millimeter-wave (mmWave) communication systems rely on narrow beams for achieving sufficient receive signal power.
Location- and Orientation-aware Millimeter Wave Beam Selection for Multi-Panel Antenna Devices
While initial beam alignment (BA) in millimeter-wave networks has been thoroughly investigated, most research assumes a simplified terminal model based on uniform linear/planar arrays with isotropic antennas.
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
A machine learning (ML) model that leverages the LiDAR sensory data to predict the current and future beams was developed.
Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments
This paper provides the first real-world evaluation of using visual (RGB camera) data and machine learning for proactively predicting millimeter wave (mmWave) dynamic link blockages before they happen.
Autoencoder-based Communications with Reconfigurable Intelligent Surfaces
This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep neural networks (DNNs) to optimize the end-to-end communication performance at the receiver.
Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems
The sensitivity of these high-frequency LOS links to blockages, however, challenges the reliability and latency requirements of these communication networks.
Computer Vision Aided Beam Tracking in A Real-World Millimeter Wave Deployment
Our proposed approach is evaluated on a large-scale real-world dataset, where it achieves an accuracy of $64. 47\%$ (and a normalized receive power of $97. 66\%$) in predicting the future beam.
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
If used for proactive hand-off, the proposed solutions can potentially provide an order of magnitude saving in the network latency, which highlights a promising direction for addressing the blockage challenges in mmWave/sub-THz networks.
Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration
This awareness could be utilized to reduce or even eliminate the beam training overhead in millimeter wave (mmWave) and sub-terahertz (THz) MIMO communication systems, which enables a wide range of highly-mobile low-latency applications.
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.
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets
Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless communication applications requires overcoming the critical challenges associated with the large antenna arrays deployed at these systems.
Design and Evaluation of Reconfigurable Intelligent Surfaces in Real-World Environment
These results, among others, draw useful insights into the design and performance of RIS systems and provide an important proof for their potential gains in real-world far-field wireless communication environments.
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.
Millimeter Wave MIMO based Depth Maps for Wireless Virtual and Augmented Reality
Accounting for the constraints on these systems, we develop a comprehensive framework for constructing accurate and high-resolution depth maps using mmWave systems.
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images
Channel covariance matrix (CCM) is one critical parameter for designing the communications systems.
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems
A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation.
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.
Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction
However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML).
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.
Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems
Specifically, we develop the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained on the data from all previous environments in the manner of classical deep learning and is then fine-tuned for new environments.
3D Scene Based Beam Selection for mmWave Communications
In this paper, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques.
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
Deep Learning Predictive Band Switching in Wireless Networks
Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location.
Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems
For example, for a system of 64 transmit and 64 receive antennas, with 3 RF chains at both sides, the proposed solution needs only 8 or 16 channel training pilots to directly predict the RF beamforming/combining vectors of the hybrid architectures and achieve near-optimal achievable rates.
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
DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications
Second, the DeepMIMO dataset is generic/parameterized as the researcher can adjust a set of system and channel parameters to tailor the generated DeepMIMO dataset for the target machine learning application.
Information Theory Signal Processing Information Theory
Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems
Enabling highly-mobile millimeter wave (mmWave) systems is challenging because of the huge training overhead associated with acquiring the channel knowledge or designing the narrow beams.
Information Theory Information Theory
