For the extended target case, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form.
In this paper, we consider an airborne integrated sensing and communications (ISAC) system where a UAV, which acts both as a communication BS and a mono-static radar, flies over a given area to transmit downlink signal to a ground communication user.
In this paper, we show a representation grouping effect during this process: the InfoNCE objective indirectly groups semantically similar representations together via randomly emerged within-modal anchors.
Specifically, training of FloodDAN includes two stages: in the first stage, we train a rainfall encoder and a prediction head to learn general transferable hydrological knowledge on large-scale source domain data; in the second stage, we transfer the knowledge in the pretrained encoder into the rainfall encoder of target domain through adversarial domain alignment.
In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors.
In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries.
Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the DoA estimation error in terms of Cram\'er-Rao lower bound (CRLB).
Compared with the Monte-Carlo method and other method based on covariance matrix, the proposed method uses more complete error model, considers the interaction effect of error sources and can be easily realized with less computation.
In particular, we adopt a disentangled representation technique to ensure the features of different factors in each modality are independent to each other.
Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services.
This experimental work focuses on a dual-functional radar sensing and communication framework where a single radiation waveform, either omnidirectional or directional, can realize both radar sensing and communication functions.
The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors.
The first stage is exploited for ISAC transmission, where a wide beam is adopted for both communication and sensing.
In this paper, we explore a dual-functional radar-communication (DFRC) system for achieving integrated sensing and communications (ISAC).
As the standardization of 5G is being solidified, researchers are speculating what 6G will be.
In this demo, we present VirtualConductor, a system that can generate conducting video from any given music and a single user's image.
Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.
Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction.
Integrated sensing and communication (ISAC) is a promising technology to improve the band-utilization efficiency via spectrum sharing or hardware sharing between radar and communication systems.
At the same time, the sensing capability incorporated in the ISAC transmission offers unique opportunities to design secure ISAC techniques.
We study security solutions for dual-functional radar communication (DFRC) systems, which detect the radar target and communicate with downlink cellular users in millimeter-wave (mmWave) wireless networks simultaneously.
Although adversarial training (AT) methods such as Adversarial Query (AQ) can improve the adversarially robust performance of meta-learning models, AT is still computationally expensive training.
Meanwhile, triggered by ISAC, we are also witnessing a paradigm shift in the ubiquitous IoT architecture, in which the sensing and communication layers are tending to converge into a new layer, namely, the signaling layer.
In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems.
Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, sharing a majority of hardware and signal processing modules and, in a typical case, sharing a single transmitted signal.
To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and feature-level attention for neural ICF models.
To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure.
We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios.
In this paper, we propose an energy-efficient radar beampattern design framework for a Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming structure.
1 code implementation • 4 Nov 2020 • Sven Buder, Sanjib Sharma, Janez Kos, Anish M. Amarsi, Thomas Nordlander, Karin Lind, Sarah L. Martell, Martin Asplund, Joss Bland-Hawthorn, Andrew R. Casey, Gayandhi M. De Silva, Valentina D'Orazi, Ken C. Freeman, Michael R. Hayden, Geraint F. Lewis, Jane Lin, Katharine. J. Schlesinger, Jeffrey D. Simpson, Dennis Stello, Daniel B. Zucker, Tomaz Zwitter, Kevin L. Beeson, Tobias Buck, Luca Casagrande, Jake T. Clark, Klemen Cotar, Gary S. Da Costa, Richard de Grijs, Diane Feuillet, Jonathan Horner, Shourya Khanna, Prajwal R. Kafle, Fan Liu, Benjamin T. Montet, Govind Nandakumar, David M. Nataf, Melissa K. Ness, Lorenzo Spina, Gregor Traven, Thor Trepper-Garcia, Yuan-Sen Ting, Rok Vogrincic, Robert A. Wittenmyer, Marusa Zerjal, the GALAH collaboration
Based on kinematics, 4% are halo stars.
Astrophysics of Galaxies Solar and Stellar Astrophysics
Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models.
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically.
To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation.
In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible.
Considering the fact that for different users, the attributes of an item have different influence on their preference for this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information.
To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items.
It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as an improved approximation to the rank of a matrix.
The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios.
Currently, low-rank tensor completion has gained cumulative attention in recovering incomplete visual data whose partial elements are missing.