Search Results for author:
Found 40 papers, 4 papers with code
Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments
Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i. e., the subflow size.
AI-Empowered RIS-Assisted Networks: CV-Enabled RIS Selection and DNN-Enabled Transmission
This paper investigates artificial intelligence (AI) empowered schemes for reconfigurable intelligent surface (RIS) assisted networks from the perspective of fast implementation.
Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks.
Building Semantic Communication System via Molecules: An End-to-End Training Approach
Furthermore, we propose a channel network to enable the E2E learning over the non-differentiable molecular channel.
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision
Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable.
Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems
For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization.
Channel Measurements and Modeling for Dynamic Vehicular ISAC Scenarios at 28 GHz
Integrated sensing and communication (ISAC) is a promising technology for 6G, with the goal of providing end-to-end information processing and inherent perception capabilities for future communication systems.
Characterization of Wireless Channel Semantics: A New Paradigm
Recently, deep learning enabled semantic communications have been developed to understand transmission content from semantic level, which realize effective and accurate information transfer.
Non-stationarity Characteristics in Dynamic Vehicular ISAC Channels at 28 GHz
It is necessary to carry out measurement, characterization, and modeling for vehicular ISAC channels as the basic theoretical support for system design.
A Cluster-Based Statistical Channel Model for Integrated Sensing and Communication Channels
However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment.
Novel 3D Geometry-Based Stochastic Models for Non-Isotropic MIMO Vehicle-to-Vehicle Channels
Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator.
GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks
This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users' individual data rate requirements and the power budget of the base station.
Invariance is Key to Generalization: Examining the Role of Representation in Sim-to-Real Transfer for Visual Navigation
The data-driven approach to robot control has been gathering pace rapidly, yet generalization to unseen task domains remains a critical challenge.
Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach
Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions.
A Comprehensive Study of PAPR Reduction Techniques for Deep Joint Source Channel Coding in OFDM Systems
Recently, deep joint source channel coding (DJSCC) techniques have been extensively studied and have shown significant performance with limited bandwidth and low signal to noise ratio.
Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts
The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages.
RIS-assisted High-Speed Railway Integrated Sensing and Communication System
One technology that has the potential to improve wireless communications in years to come is integrated sensing and communication (ISAC).
Deep Plug-and-Play Prior for Multitask Channel Reconstruction in Massive MIMO Systems
The proposed method corresponding to these three channel reconstruction tasks employs a common DL model, which greatly reduces the overhead of model training and storage.
Deep-Learning-Aided Alternating Least Squares for Tensor CP Decomposition and Its Application to Massive MIMO Channel Estimation
To achieve accurate and low-latency channel estimation, good and fast CP decomposition algorithms are desired.
Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization
Additionally, structured pruning is incorporated to reduce the dimension of the transmitted features.
Deep Learning for Asynchronous Massive Access with Data Frame Length Diversity
Grant-free non-orthogonal multiple access has been regarded as a viable approach to accommodate access for a massive number of machine-type devices with small data packets.
Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities
Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy.
CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback
The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP).
Whodunit? Learning to Contrast for Authorship Attribution
To the best of our knowledge, we are the first to integrate contrastive learning with pre-trained language model fine-tuning for authorship attribution.
Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing
In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized.
5G for Railways: the Next Generation Railway Dedicated Communications
It is thus necessary for railways to replace the current 2G-based technology with the next generation railway dedicated communication system with improved capacity and capability, and the 5G for railways (5G-R) technology is a promising solution for further intelligent railways.
Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization
To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques.
Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling
This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels.
Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems
In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed.
Deep Visual Navigation under Partial Observability
Importantly, we integrate the multiple neural network modules into a unified controller that achieves robust performance for visual navigation in complex, partially observable environments.
Cluster-based Characterization and Modeling for UAV Air-to-Ground Time-Varying Channels
With the deep integration between the unmanned aerial vehicle (UAV) and wireless communication, UAV-based air-to-ground (AG) propagation channels need more detailed descriptions and accurate models.
Applications of Reconfigurable Intelligent Surface in Smart High Speed Train Communications
Reconfigurable intelligent surface (RIS) is one of the most promising technologies for 5G-Adv and 6G.
Coalition Game Based Full-duplex Popular Content Distribution in mmWave Vehicular Networks
We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks.
Fairness
Information Theory
Networking and Internet Architecture
Information Theory
Channel Modeling for UAV Communications: State of the Art, Case Studies, and Future Directions
As essential aerial platforms, unmanned aerial vehicles (UAVs) play an increasingly important role in broad wireless connectivity and high-data-rate transmission for future communication systems.
Physical Layer Security Enhancement With Reconfigurable Intelligent Surface-Aided Networks
Reconfigurable intelligent surface (RIS)-aided wireless communications have drawn significant attention recently.
Information Theory Signal Processing 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.
Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital.
Satellite-Terrestrial Channel Characterization in High-Speed Railway Environment at 22.6 GHz
In this paper, the satellite-terrestrial channel at 22. 6 GHz is characterized for a typical high-speed railway (HSR) environment.
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.
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine
Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms.
