Search Results for author:
Found 45 papers, 8 papers with code
User Activity Detection with Delay-Calibration for Asynchronous Massive Random Access
The objective is to alleviate the problem of massive collision due to the limited number of orthogonal preambles of an access scheme in which user activity detection is performed.
BIFRÖST: 3D-Aware Image compositing with Language Instructions
This paper introduces Bifr\"ost, a novel 3D-aware framework that is built upon diffusion models to perform instruction-based image composition.
Score-Based Variational Inference for Inverse Problems
We show that RMP can be implemented by solving a variational inference problem, which can be further decomposed as minimizing a reverse KL divergence at each reverse step.
Learning-based Block-wise Planar Channel Estimation for Time-Varying MIMO OFDM
In this paper, we propose a learning-based block-wise planar channel estimator (LBPCE) with high accuracy and low complexity to estimate the time-varying frequency-selective channel of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system.
UAV-Enabled Asynchronous Federated Learning
To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML).
Hybrid Vector Message Passing for Generalized Bilinear Factorization
In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem.
Scalable Near-Field Localization Based on Partitioned Large-Scale Antenna Array
We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization.
Integrating Communication, Sensing and Computing in Satellite Internet of Things: Challenges and Opportunities
Satellite Internet of Things (IoT) is to use satellites as the access points for IoT devices to achieve the global coverage of future IoT systems, and is expected to support burgeoning IoT applications, including communication, sensing, and computing.
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization
We conduct a general convergence analysis to quantitatively capture the influence of aggregation weight and communication error on the MIMO OA-DFL performance in \emph{ad hoc} networks.
Multi-Device Task-Oriented Communication via Maximal Coding Rate Reduction
In this paper, we consider a task-oriented multi-device edge inference system over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i. e., feature encoding and classification) and communication (i. e., precoding) modules are designed with the same goal of inference accuracy maximization.
PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs
In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models.
Variational Bayesian Multiuser Tracking for Reconfigurable Intelligent Surface Aided MIMO-OFDM Systems
Reconfigurable intelligent surface (RIS) has attracted enormous interest for its potential advantages in assisting both wireless communication and environmental sensing.
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity
This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links.
Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded Multiplexing Approach
We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model.
Closed-Loop Transcription via Convolutional Sparse Coding
Our method is arguably the first to demonstrate that a concatenation of multiple convolution sparse coding/decoding layers leads to an interpretable and effective autoencoder for modeling the distribution of large-scale natural image datasets.
OFDM-Based Massive Connectivity for LEO Satellite Internet of Things
We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point.
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning
With huge amounts of data explosively increasing in the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and privacy leak risks.
Joint Localization and Information Transfer for Reconfigurable Intelligent Surface Aided Full-Duplex Systems
A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing problem.
Confederated Learning: Federated Learning with Decentralized Edge Servers
Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner.
Hybrid Offline-Online Design for Reconfigurable Intelligent Surface Aided UAV Communication
This letter considers the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication systems in urban areas under the general Rician fading channel.
Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing
To achieve communication-efficient federated multitask learning (FMTL), we propose an over-the-air FMTL (OAFMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES).
Receiver Design for MIMO Unsourced Random Access with SKP Coding
In this letter, we extend the sparse Kronecker-product (SKP) coding scheme, originally designed for the additive white Gaussian noise (AWGN) channel, to multiple input multiple output (MIMO) unsourced random access (URA).
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization
We then conduct an FL convergence analysis to connect the aggregation distortion and the FL convergence performance.
Energy-Efficient UAV-Mounted RIS Assisted Mobile Edge Computing
Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) have been recently applied in the field of mobile edge computing (MEC) to improve the data exchange environment by proactively changing the wireless channels through maneuverable location deployment and intelligent signals reflection, respectively.
Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels
We establish a communication-learning analysis framework for the proposed OA-FMTL scheme by considering the spatial correlation between devices, and formulate an optimization problem for the design of transceiver beamforming and device selection.
Bayesian User Localization and Tracking for Reconfigurable Intelligent Surface Aided MIMO Systems
In this paper, we study the user localization and tracking problem in the reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) system, where a multi-antenna base station (BS) and multiple RISs are deployed to assist the localization and tracking of a multi-antenna user.
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
In particular, we propose to learn a closed-loop transcription between a multi-class multi-dimensional data distribution and a linear discriminative representation (LDR) in the feature space that consists of multiple independent multi-dimensional linear subspaces.
Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning
Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices.
Elevation Angle-Dependent 3D Trajectory Design for Aerial RIS-aided Communication
This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange.
Over-the-Air Federated Multi-Task Learning
In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES).
Full-Dimensional Rate Enhancement for UAV-Enabled Communications via Intelligent Omni-Surface
This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS).
Fully Convolutional Line Parsing
We conduct extensive experiments and show that our method achieves a significantly better trade-off between efficiency and accuracy, resulting in a real-time line detector at up to 73 FPS on a single GPU.
Ranked #1 on
Line Segment Detection
on York Urban Dataset
Frequency Reflection Modulation for Reconfigurable Intelligent Surface Aided OFDM Systems
Reconfigurable intelligent surface (RIS) based reflection modulation has been considered as a promising information delivery mechanism, and has the potential to realize passive information transfer of a RIS without consuming any additional radio frequency chain and time/frequency/energy resources.
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image.
Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning
In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system.
CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface
We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable.
Semi-Blind Cascaded Channel Estimation for Reconfigurable Intelligent Surface Aided Massive MIMO
In a RIS-aided MIMO system, the acquisition of channel state information (CSI) is important for achieving passive beamforming gains of the RIS, but is also challenging due to the cascaded property of the transmitter-RIS-receiver channel and the lack of signal processing capability of the passive RIS elements.
Bayesian Inference
Information Theory
Information Theory
Denoising-based Turbo Message Passing for Compressed Video Background Subtraction
In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.
Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
However, due to the heterogeneity of communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.
Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA
To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm.
Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunities
Reconfigurable intelligent surfaces (RISs) are regarded as a promising emerging hardware technology to improve the spectrum and energy efficiency of wireless networks by artificially reconfiguring the propagation environment of electromagnetic waves.
Information Theory Signal Processing Information Theory
Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design
In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver.
Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access
In this work, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS).
