Search Results for author:
Found 135 papers, 9 papers with code
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning
Judicious resource allocation can effectively enhance federated learning (FL) training performance in wireless networks by addressing both system and statistical heterogeneity.
Distributed U6G ELAA Communication Systems: Channel Measurement and Small-Scale Fading Characterization
The distributed upper 6 GHz (U6G) extra-large scale antenna array (ELAA) is a key enabler for future wireless communication systems, offering higher throughput and wider coverage, similar to existing ELAA systems, while effectively mitigating unaffordable complexity and hardware overhead.
A Tutorial on MIMO-OFDM ISAC: From Far-Field to Near-Field
Integrated sensing and communication (ISAC) is one of the key usage scenarios for future sixth-generation (6G) mobile communication networks, where communication and sensing (C&S) services are simultaneously provided through shared wireless spectrum, signal processing modules, hardware, and network infrastructure.
Optimal Power Allocation for OFDM-based Ranging Using Random Communication Signals
High-precision ranging plays a crucial role in future 6G Integrated Sensing and Communication (ISAC) systems.
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors
One fundamental problem for CKM-enabled wireless systems lies in how to construct high-quality and complete CKM for all locations of interest, based on only limited and noisy on-site channel knowledge data.
AI for CSI Prediction in 5G-Advanced and Beyond
Artificial intelligence (AI) is pivotal in advancing fifth-generation (5G)-Advanced and sixth-generation systems, capturing substantial research interest.
Sensing With Random Communication Signals
To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality.
Efficient Deployment of Deep MIMO Detection Using Learngene
However, the practical deployment of DL-based detectors is hindered by poor generalization, necessitating costly retraining for different devices and scenarios.
Hybrid Beamforming with Orthogonal delay-Doppler Division Multiplexing Modulation for Terahertz Sensing and Communication
Finally, a sensing-centric hybrid beamforming is proposed to design the sensing combiner by minimizing the Cram\'er-Rao lower bounds (CRLB) of angles.
Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection
The proposed framework fully exploits the dependence of stereo-vision 3D detection on semantic information in images and prioritizes the transmission of this semantic information to reduce total transmission data sizes while ensuring the detection accuracy.
Hybrid Beamforming Design for Bistatic Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC) in millimeter wave is a key enabler for next-generation networks, which leverages large bandwidth and extensive antenna arrays, benefiting both communication and sensing functionalities.
Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception
Stand-alone perception systems in autonomous driving suffer from limited sensing ranges and occlusions at extended distances, potentially resulting in catastrophic outcomes.
Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture
Based on the observation that the overall cascaded MIMO channel can be decomposed into multiple sub-channels, the cascaded channel for a typical user is estimated in Stage II.
Keypoint Detection Empowered Near-Field User Localization and Channel Reconstruction
We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed.
Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition
Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks.
RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation Measurement and Classification for ISAC Applications
This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2. 8 GHz.
Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals
This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance.
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization
Despite impressive capability in learning over graph-structured data, graph neural networks (GNN) suffer from adversarial topology perturbation in both training and inference phases.
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
This paper investigates the use of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks.
A Tensor-Structured Approach to Dynamic Channel Prediction for Massive MIMO Systems with Temporal Non-Stationarity
Furthermore, we model cross-domain correlations of ADD domain channels within each frame, induced by clustered scattering, through the Markov random field and tensor-coupled Gaussian distribution that incorporates high-order neighboring structures.
Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach
In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs).
Position-Aided Semantic Communication for Efficient Image Transmission: Design, Implementation, and Experimental Results
Semantic communication, augmented by knowledge bases (KBs), offers substantial reductions in transmission overhead and resilience to errors.
Structured Connectivity for 6G Reflex Arc: Task-Oriented Virtual User and New Uplink-Downlink Tradeoff
To maintain the task-level connections between the sensor-computing center link and the computing center-actuator link, we propose to treat the sensor and actuator as a virtual user.
Adaptive Wireless Image Semantic Transmission: Design, Simulation, and Prototype Validation
The rapid development of artificial intelligence has significantly advanced semantic communications, particularly in wireless image transmission.
Delay Alignment Modulation with Hybrid Analog/Digital Beamforming for Millimeter Wave and Terahertz Communications
For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed.
Electromagnetic Property Sensing and Channel Reconstruction Based on Diffusion Schrödinger Bridge in ISAC
The autoencoder compresses the sensing channel into a latent space that retains essential features, which incorporates positional embeddings to process spatial context.
Efficient Deep Learning-based Cascaded Channel Feedback in RIS-Assisted Communications
In the realm of reconfigurable intelligent surface (RIS)-assisted communication systems, the connection between a base station (BS) and user equipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UE channels.
Semantic Communication for Cooperative Perception using HARQ
Cooperative perception, offering a wider field of view than standalone perception, is becoming increasingly crucial in autonomous driving.
Meta-Learning Empowered Graph Neural Networks for Radio Resource Management
In this paper, we consider a radio resource management (RRM) problem in the dynamic wireless networks, comprising multiple communication links that share the same spectrum resource.
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
We utilize the Lyapunov method to decouple the formulated problem into a series of one-slot optimization problems and develop a two-stage optimization algorithm to achieve the optimal transmission power control and IoT device scheduling strategies.
GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning
Device-to-device (D2D) spectrum sharing in wireless communications is a challenging non-convex combinatorial optimization problem, involving entangled link scheduling and power control in a large-scale network.
Beam Prediction based on Large Language Models
Millimeter-wave (mmWave) communication is promising for next-generation wireless networks but suffers from significant path loss, requiring extensive antenna arrays and frequent beam training.
Prototyping and Experimental Results for ISAC-based Channel Knowledge Map
The system consists of an ISAC base station (BS), a user equipment (UE), and a server.
Enhancing Spatial Multiplexing and Interference Suppression for Near- and Far-Field Communications with Sparse MIMO
Given the same number of array elements, the performance of sparse MIMO is compared with conventional MIMO.
6D Motion Parameters Estimation in Monostatic Integrated Sensing and Communications System
Since the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the dynamic target's radial velocity, horizontal angular velocity, and pitch angular velocity from these virtual velocities.
Deep Learning-based CSI Feedback in Wi-Fi Systems
In Wi-Fi systems, channel state information (CSI) plays a crucial role in enabling access points to execute beamforming operations.
Near-Optimal MIMO Detection Using Gradient-Based MCMC in Discrete Spaces
The discrete nature of transmitted symbols poses challenges for achieving optimal detection in multiple-input multiple-output (MIMO) systems associated with a large number of antennas.
Electromagnetic Property Sensing Based on Diffusion Model in ISAC System
In this paper, we develop a novel ISAC scheme that utilizes the diffusion model to sense the electromagnetic (EM) property of the target in a predetermined sensing area.
Interference Cancellation Based Neural Receiver for Superimposed Pilot in Multi-Layer Transmission
In this paper, an interference cancellation based neural receiver for superimposed pilot (SIP) in multi-layer transmission is proposed, where the data and pilot are non-orthogonally superimposed in the same time-frequency resource.
Rethinking Waveform for 6G: Harnessing Delay-Doppler Alignment Modulation
Waveform design has served as a cornerstone for each generation of mobile communication systems.
Towards Unified AI Models for MU-MIMO Communications: A Tensor Equivariance Framework
In this paper, we propose a unified framework based on equivariance for the design of artificial intelligence (AI)-assisted technologies in multi-user multiple-input-multiple-output (MU-MIMO) systems.
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE
While Hyperbolic Graph Neural Network (HGNN) has recently emerged as a powerful tool dealing with hierarchical graph data, the limitations of scalability and efficiency hinder itself from generalizing to deep models.
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server.
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based consensus mechanism, wherein the DTs maintained at the gateway side execute DNN inference tasks using the data collected from their associated IIoT devices.
Adaptive Wireless Image Semantic Transmission and Over-The-Air Testing
Semantic communication has undergone considerable evolution due to the recent rapid development of artificial intelligence (AI), significantly enhancing both communication robustness and efficiency.
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
To evaluate the learning performance of T-SFL, we provide an upper bound on the global loss function.
Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis
Based on the EM wave propagation model, we propose an EM property sensing method, in which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the EM property vector.
Decision Transformers for Wireless Communications: A New Paradigm of Resource Management
By leveraging the power of DT models learned over offline datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in new scenarios with different state and action spaces, compared with DRL.
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients.
Robust Symbol-Level Precoding for Massive MIMO Communication Under Channel Aging
With the signal model in the presence of channel aging, we formulate the signal-to-noise-plus-interference ratio (SINR) balancing and minimum mean square error (MMSE) problems for robust SLP design.
Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication
Specifically, we first establish an end-to-end EM propagation model by means of Maxwell equations, where the EM property of the target is captured by a closed-form expression of the ISAC channel, incorporating the Lippmann-Schwinger equation and the method of moments (MOM) for discretization.
6D Radar Sensing and Tracking in Monostatic Integrated Sensing and Communications System
Unlike most existing ISAC studies believing that only the radial velocity of far-field dynamic target can be measured based on one single base station (BS), we find that the sensing echo channel of MIMO-ISAC system actually includes the distance, horizontal angle, pitch angle, radial velocity, horizontal angular velocity, and pitch angular velocity of the dynamic target.
Reshaping the ISAC Tradeoff Under OFDM Signaling: A Probabilistic Constellation Shaping Approach
To cope with this issue, we characterize the random AF of OFDM communication signals, and demonstrate that the AF variance is determined by the fourth-moment of the constellation amplitudes.
Beam-Delay Domain Channel Estimation for mmWave XL-MIMO Systems
This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth.
Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback
This paper proposes using neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures, thereby maximizing the potential of deep learning in exclusive environments.
Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning
Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal.
Two-stage Synthetic Supervising and Multi-view Consistency Self-supervising based Animal 3D Reconstruction by Single Image
To address this challenge, we propose the combination of two-stage supervised and self-supervised training to address the challenge of obtaining animal cooperation for 3D scanning.
Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges
Reconfigurable intelligent surfaces (RISs) have flexible and exceptional performance in manipulating electromagnetic waves and customizing wireless channels.
Knowledge-driven Meta-learning for CSI Feedback
In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase.
Enhancing Energy Efficiency for Reconfigurable Intelligent Surfaces with Practical Power Models
To address this issue, in this paper, we first utilize a practical power model for a RIS-assisted multi-user multiple-input single-output (MU-MISO) communication system, which takes into account the difference in power dissipation caused by ON-OFF states of RIS's PIN diodes.
Soft Demodulator for Symbol-Level Precoding in Coded Multiuser MISO Systems
It is observed that the received SLP signals do not always follow Gaussian distribution, rendering the conventional soft demodulation with the Gaussian assumption unsuitable for the coded SLP systems.
Semantic Communications using Foundation Models: Design Approaches and Open Issues
Foundation models (FMs), including large language models, have become increasingly popular due to their wide-ranging applicability and ability to understand human-like semantics.
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical.
Reducing Channel Estimation and Feedback Overhead in IRS-Aided Downlink System: A Quantize-then-Estimate Approach
Recently, [1] revealed a strong correlation in different users' cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication.
RIS-Enhanced Semantic Communications Adaptive to User Requirements
Simulation results demonstrate the adaptability and efficiency of the RIS-SC framework across diverse channel conditions and user requirements.
Capturing the Diffusive Behavior of the Multiscale Linear Transport Equations by Asymptotic-Preserving Convolutional DeepONets
In this paper, we introduce two types of novel Asymptotic-Preserving Convolutional Deep Operator Networks (APCONs) designed to address the multiscale time-dependent linear transport problem.
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation.
Two-Bit RIS-Aided Communications at 3.5GHz: Some Insights from the Measurement Results Under Multiple Practical Scenes
In particular, we adopt a self-developed broadband intelligent communication system 40MHz-Net (BICT-40N) terminal in order to fully acquire the channel information.
Near Field Computational Imaging with RIS Generated Virtual Masks
We then provide a RIS design scheme for virtual EM masks by employing a regularization technique.
Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback
Additionally, since the heterogeneity of CSI datasets in different UEs can degrade the performance of the FEEL-based framework, we introduce a personalization strategy to improve feedback performance.
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control
The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks.
Lightweight Neural Network with Knowledge Distillation for CSI Feedback
Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder.
Two-Timescale Transmission Design for Wireless Communication Systems Aided by Active RIS
This paper considers an active reconfigurable intelligent surface (RIS)-aided communication system, where an M-antenna base station (BS) transmits data symbols to a single-antenna user via an N-element active RIS.
Wireless Semantic Transmission via Revising Modules in Conventional Communications
Therefore, the novel semantic-based coding methods and performance metrics have been investigated and the designed semantic systems consist of various modules as in the conventional communications but with improved functions.
Control-Oriented Power Allocation for Integrated Satellite-UAV Networks
This letter presents a sensing-communication-computing-control (SC3) integrated satellite unmanned aerial vehicle (UAV) network, where the UAV is equipped with on-board sensors, mobile edge computing (MEC) servers, base stations and satellite communication module.
RIS-Aided Near-Field Localization and Channel Estimation for the Sub-Terahertz System
This makes localization and channel estimation in the near field of the RIS-aided subTHz system more challenging.
AI for CSI Feedback Enhancement in 5G-Advanced
This article provides a guideline for the standardization study of AI-based CSI feedback enhancement.
Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems
Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user terminal) and feeding back to the transmitter.
AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback
This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AIWork Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided.
ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition
Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next.
Rate-Region Characterization and Channel Estimation for Cell-Free Symbiotic Radio Communications
We first derive the achievable communication rates of the active primary user and passive secondary user under the assumption of perfect channel state information (CSI), based on which the transmit beamforming of the cellfree APs is optimized to characterize the achievable rate-region of cell-free symbiotic communication systems.
Multi-task Learning-based CSI Feedback Design in Multiple Scenarios
One efficient CSI feedback method is the Auto-Encoder (AE) structure based on deep learning, yet facing problems in actual deployments, such as selecting the deployment mode when deploying in a cell with multiple complex scenarios.
Wireless Semantic Communications for Video Conferencing
In this paper, we initially establish a basal semantic video conferencing (SVC) network, which dramatically reduces transmission resources while only losing detailed expressions.
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning
Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.
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.
Low-Latency Federated Learning over Wireless Channels with Differential Privacy
Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach.
AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback
In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group.
Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ
Even if semantic communication has been successfully applied in the sentence transmission to reduce semantic errors, existing architecture is usually fixed in the codeword length and is inefficient and inflexible for the varying sentence length.
Deep Learning-based Implicit CSI Feedback in Massive MIMO
By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks.
Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems
Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect.
Fast Antenna and Beam Switching Method for mmWave Handsets with Hand Blockage
In this method, only one antenna module is used for the reception to predict the best beam of other antenna modules.
On Channel Reciprocity in Reconfigurable Intelligent Surface Assisted Wireless Network
Channel reciprocity greatly facilitates downlink precoding in time-division duplexing (TDD) multiple-input multiple-output (MIMO) communications without the need for channel state information (CSI) feedback.
Information Theory Information Theory
Environment-Aware and Training-Free Beam Alignment for mmWave Massive MIMO via Channel Knowledge Map
Two instances of CKM are proposed for beam alignment in mmWave massive MIMO systems, namely channel path map (CPM) and beam index map (BIM).
Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning
In this letter, we investigate the hybrid beamforming based on deep reinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU) multiple-input-single-output (MISO) system.
Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band
The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size.
Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
In particular, we introduce a hypernetwork to generate the damping factors for GEC-SR.
CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning
The user equipment in the latter one directly feeds back the received pilot signals to the base station.
Computer Vision and Normalizing Flow-Based Defect Detection
In this paper, we overcome these challenges and design a three-stage plug-and-play fully automated unsupervised 360-degree defect detection system.
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test
An unfolded turbo decoding module, called TurboNet, is used for channel decoding.
Sparse Array of Sub-surface Aided Anti-blockage mmWave Communication Systems
An approximated ergodic spectral efficiency of the SAoS aided system is derived and the performance impact of the SAoS design is evaluated.
Information Theory Information Theory
Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth
Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications.
A random batch Ewald method for particle systems with Coulomb interactions
We develop a random batch Ewald (RBE) method for molecular dynamics simulations of particle systems with long-range Coulomb interactions, which achieves an $O(N)$ complexity in each step of simulating the $N$-body systems.
Computational Physics 65C35, 82M37, 65T50
Integrated Communication and Localization in mmWave Systems
As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have been conducted.
The LISE package: solvers for static and time-dependent superfluid local density approximation equations in three dimensions
Nuclear implementation of the density functional theory (DFT) is at present the only microscopic framework applicable to the whole nuclear landscape.
Nuclear Theory
Joint Channel Assignment and Power Allocation for Multi-UAV Communication
Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation.
Aerial Intelligent Reflecting Surface: Joint Placement and Passive Beamforming Design with 3D Beam Flattening
Specifically, we aim to maximize the worst-case signal-to-noise ratio (SNR) over all locations in a target area by jointly optimizing the transmit beamforming for the source node and the placement as well as 3D passive beamforming for the AIRS.
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach
By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data.
Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding
Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power consumption.
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost.
Robot-assisted Backscatter Localization for IoT Applications
Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes.
Angle-Dependent Phase Shifter Model for Reconfigurable Intelligent Surfaces: Does the Angle-Reciprocity Hold?
The existing phase shifter models adopted for reconfigurable intelligent surfaces (RISs) have ignored the electromagnetic (EM) waves propagation behavior, thus cannot reveal practical effects of RIS on wireless communication systems.
Energy Model for UAV Communications: Experimental Validation and Model Generalization
In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible.
Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO
We develop a DL based CSI feedback network in this study to complete the feedback of CSI effectively.
Information Theory Signal Processing Information Theory
Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning
Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future.
Deep Learning-based CSI Feedback for RIS-assisted Multi-user Systems
These enhancements are tasked with the precise retrieval and fusion of shared and individual data.
Information Theory Signal Processing Information Theory
MIMO Transmission through Reconfigurable Intelligent Surface: System Design, Analysis, and Implementation
Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface.
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.
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
Physical Layer Security Enhancement Exploiting Intelligent Reflecting Surface
In this letter, the use of intelligent reflecting surface (IRS) to enhance the physical layer security of downlink wireless communication is investigated.
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.
Bit-level Optimized Neural Network for Multi-antenna Channel Quantization
Quantized channel state information (CSI) plays a critical role in precoding design which helps reap the merits of multiple-input multiple-output (MIMO) technology.
Information Theory Signal Processing Information Theory
Compression and Acceleration of Neural Networks for Communications
Deep learning (DL) has achieved great success in signal processing and communications and has become a promising technology for future wireless communications.
Information Theory Signal Processing Information Theory
PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning
In this letter, we investigate the hybrid beamforming for millimeter wave massive multiple-input multiple-output (MIMO) system based on deep reinforcement learning (DRL).
Model-Driven Deep Learning for MIMO Detection
In this paper, we investigate the model-driven deep learning (DL) for MIMO detection.
Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency.
Signal Processing Information Theory Information Theory
Gridless Variational Bayesian Channel Estimation for Antenna Array Systems with Low Resolution ADCs
Employing low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays at the receivers has drawn considerable interests in the millimeter wave (mm-wave) system.
Signal Processing Information Theory Information Theory
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAMP, called OAMP-Net.
Artificial Intelligence-aided Receiver for A CP-Free OFDM System: Design, Simulation, and Experimental Test
The AI receiver includes a channel estimation neural network (CE-NET) and a signal detection neural network based on orthogonal approximate message passing (OAMP), called OAMP-NET.
Information Theory Information Theory
AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems.
Model-Driven Deep Learning for Physical Layer Communications
Intelligent communication is gradually considered as the mainstream direction in future wireless communications.
Learning to Dodge A Bullet: Concyclic View Morphing via Deep Learning
The bullet-time effect, presented in feature film ``The Matrix", has been widely adopted in feature films and TV commercials to create an amazing stopping-time illusion.
Automatic 3D Indoor Scene Modeling From Single Panorama
The recovered layout is then used to guide shape estimation of the remaining objects using their normal information.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
