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Found 66 papers, 4 papers with code
Cross-Frame OTFS Parameter Estimation Based On Chinese Remainder Theorem
By designing co-prime numbers of subcarriers and time slots in different subframes, the difference in the responses of the subframes for a target can be used to estimate the distance and velocity of an out-of-range target.
Corrections to "Computer Vision Aided mmWave Beam Alignment in V2X Communications"
Then, we revise the simulation results of Fig.
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.
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.
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.
Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios
First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead.
Integrated Sensing and Communications Framework for 6G Networks
In this paper, we propose a novel integrated sensing and communications (ISAC) framework for the sixth generation (6G) mobile networks, in which we decompose the real physical world into static environment, dynamic targets, and various object materials.
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory
Therefore, we next design a transfer learning strategy that fine-tunes the pre-trained model by limited labeled data of the new environment.
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.
Environment Reconstruction based on Multi-User Selection and Multi-Modal Fusion in ISAC
Integrated sensing and communications (ISAC) has been deemed as a key technology for the sixth generation (6G) wireless communications systems.
STAR-RIS Aided Integrated Sensing and Communication over High Mobility Scenario
With the help of sensing results, the phase shifts of the STAR-RIS are delicately designed, which can significantly improve the received signal strength for both the RSUs and the in-vehicle UE, and can finally enhance the sensing and communication performance.
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.
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.
A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems
To address these issues, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems.
Communication-Assisted Sensing in 6G Networks
Exploring the mutual benefit and reciprocity of sensing and communication (S\&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems.
Integrated Sensing and Communications in Clutter Environment
Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel model that includes both static environment and dynamic targets.
Moving Target Sensing for ISAC Systems in Clutter Environment
In this paper, we consider the moving target sensing problem for integrated sensing and communication (ISAC) systems in clutter environment.
Beam Squint Assisted User Localization in Near-Field Integrated Sensing and Communications Systems
In this paper, we find that with the aid of true-time-delay lines (TTDs), the range and trajectory of the beam squint in near-field communications systems can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization.
Radar Sensing via OTFS Signaling
By multiplexing information symbols in the delay-Doppler (DD) domain, orthogonal time frequency space (OTFS) is a promising candidate for future wireless communication in high-mobility scenarios.
YOLO: An Efficient Terahertz Band Integrated Sensing and Communications Scheme with Beam Squint
Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different OFDM symbols to estimate the distance and velocity of dynamic targets.
Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks
In particular, a pair of transmission schemes, namely, separated S&C and dual-functional waveform designs, are proposed to optimize the sensing QoS under the constraints of the rate-distortion and power budget.
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.
Multi-User Matching and Resource Allocation in Vision Aided Communications
Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system.
Millimeter Wave Wireless Communication Assisted Three-Dimensional Simultaneous Localization and Mapping
In this paper, we study the three-dimensional (3D) simultaneous localization and mapping (SLAM) problem in complex outdoor and indoor environments based only on millimeter-wave (mmWave) wireless communication signals.
Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots
To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period.
Deep learning numerical methods for high-dimensional fully nonlinear PIDEs and coupled FBSDEs with jumps
We propose a deep learning algorithm for solving high-dimensional parabolic integro-differential equations (PIDEs) and high-dimensional forward-backward stochastic differential equations with jumps (FBSDEJs), where the jump-diffusion process are derived by a Brownian motion and an independent compensated Poisson random measure.
Radar Sensing via OTFS Signaling: A Delay Doppler Signal Processing Perspective
The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain.
Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection
In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images.
Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction
In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks.
A Generalized Semantic Communication System: from Sources to Channels
This article introduces a framework for generalized semantic communication system, which exploits the semantic information in both the multimodal source and the wireless channel environment.
Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications
Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient.
RIS Design for CRB Optimization in Source Localization with Electromagnetic Interference
Reconfigurable Intelligent Surface (RIS) plays an important role in enhancing source localization accuracy.
Joint Channel Estimation and Data Detection for Hybrid RIS aided Millimeter Wave OTFS Systems
For the OTFS transmission, we propose a JCEDD scheme over delay-Doppler domain.
Towards Semantic Communications: Deep Learning-Based Image Semantic Coding
Semantic communications has received growing interest since it can remarkably reduce the amount of data to be transmitted without missing critical information.
Vision-Aided Blockage Avoidance in UAV-assisted V2X Communications
The blockage is a key challenge for millimeter wave communication systems, since these systems mainly work on line-of-sight (LOS) links, and the blockage can degrade the system performance significantly.
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.
Integrated Sensing and Communications with Joint Beam Squint and Beam Split for Massive MIMO
In this paper, instead of alleviating the wideband beam squint effect, we take advantage of joint beam squint and beam split effect and propose a novel user directions sensing method integrated with massive MIMO orthogonal frequency division multiplexing (OFDM) systems.
Multi-Camera View Based Proactive BS Selection and Beam Switching for V2X
In this paper, we proposed a multi-camera view based proactive BS selection and beam switching that can predict the optimal BS of the user in the future frame and switch the corresponding beam pair.
Computer Vision-Aided Reconfigurable Intelligent Surface-Based Beam Tracking: Prototyping and Experimental Results
The vision-aided RIS prototype system is tested in two mobile scenarios: RIS works in near-field conditions as a passive array antenna of the base station; RIS works in far-field conditions to assist the communication between the base station and the user equipment.
Beam Squint Assisted User Localization in Near-Field Communications Systems
In this paper, we find that with the aid of the time-delay lines (TDs), the range and trajectory of the beam squint of a near-field communications system can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization.
Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems
In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems.
Reconfigurable Intelligent Surface for Near Field Communications: Beamforming and Sensing
However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS.
Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning
We first propose a graph attention self-supervised learning algorithm (GASSL) to detect the HUAVs of a single UAV cluster, where the GASSL can fit the IFS at the same time.
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI
To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI).
Federated Dynamic Neural Network for Deep MIMO Detection
In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems.
Resilient UAV Swarm Communications with Graph Convolutional Neural Network
Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs.
Cascaded Channel Estimation for RIS Assisted mmWave MIMO Transmissions
Channel estimation is challenging for the reconfigurable intelligence surface (RIS) assisted millimeter wave (mmWave) communications.
Joint Channel Estimation and Mixed-ADCs Allocation for Massive MIMO via Deep Learning
Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy.
Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming
In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral efficiencies of massive multiple-input-multiple-output (MIMO) downlink systems.
AIRIS: Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications
Reconfigurable intelligent surface (RIS) is an emerging meta-surface that can provide additional communications links through reflecting the signals, and has been recognized as a strong candidate of 6G mobile communications systems.
Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers
Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems.
Deep Learning Based RIS Channel Extrapolation with Element-grouping
Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks.
Understanding Deep MIMO Detection
We demonstrate that data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires enough training samples to converge in time-varying channels.
Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems
To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder.
Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions
With the depletion of spectrum, wireless communication systems turn to exploit large antenna arrays to achieve the degree of freedom in space domain, such as millimeter wave massive multi-input multioutput (MIMO), reconfigurable intelligent surface assisted communications and cell-free massive MIMO.
Information Theory Signal Processing Information Theory
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.
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots
In this paper, we propose a new downlink beamforming strategy for mmWave communications using uplink sub-6GHz channel information and a very few mmWave pilots.
Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication
In this paper, we add signal processing units for a few antennas at RIS to partially acquire the channels.
Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO
In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information, especially in the frequency division duplex mode.
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).
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.
Model-aided Deep Neural Network for Source Number Detection
Specifically, we first propose the eigenvalue based regression network (ERNet) and classification network (ECNet) to estimate the number of non-coherent sources, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the supervise label of the networks, respectively.
Model-Driven Deep Learning for Physical Layer Communications
Intelligent communication is gradually considered as the mainstream direction in future wireless communications.
