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Found 86 papers, 31 papers with code
A Tractable Approach to Massive Communication and Ubiquitous Connectivity in 6G Standardization
The full-scale 6G standardization has attracted considerable recent attention, especially since the first 3GPP-wide 6G workshop held in March 2025.
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems
Integrated sensing and communication (ISAC) systems operating at terahertz (THz) bands are envisioned to enable both ultra-high data-rate communication and precise environmental awareness for next-generation wireless networks.
Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies
To address these challenges, satellite edge AI provides a paradigm shift from ground-based to on-board data processing by leveraging the integrated communication-and-computation capabilities in space computing power networks (Space-CPN), thereby enhancing the timeliness, effectiveness, and trustworthiness for remote sensing downstream tasks.
Multi-Modal Self-Supervised Semantic Communication
Semantic communication is emerging as a promising paradigm that focuses on the extraction and transmission of semantic meanings using deep learning techniques.
PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X
On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions.
AI and Deep Learning for THz Ultra-Massive MIMO: From Model-Driven Approaches to Foundation Models
We propose three research roadmaps for AI algorithms tailored to THz UM-MIMO systems.
Siamese Machine Unlearning with Knowledge Vaporization and Concentration
In this work, we propose the concepts of knowledge vaporization and concentration to selectively erase learned knowledge from specific data points while maintaining representations for the remaining data.
Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment
Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and transmit relevant task information.
DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV
Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL.
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks
Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges.
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning
In this paper, we introduce semantic communication into a cellular vehicle-to-everything (C-V2X)- based autonomous vehicle platoon system for the first time, aiming to achieve efficient management of communication resources in a dynamic environment.
V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario
Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized.
Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles
The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users.
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training.
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data.
A Survey on Integrated Sensing, Communication, and Computation
Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements.
Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU).
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data.
Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network
By integrating DT with VEC, a virtual vehicle DT can be created in the VEC server to monitor the real-time operating status of vehicles.
Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications
In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput.
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints.
Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize
In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, the growing number of base station antennas leads to prohibitive feedback overhead for downlink channel state information (CSI).
Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks
Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments.
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation
In this paper, we study the cooperative resource allocation problem with unknown system dynamics of MRPs.
Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning
This optimization encompasses the optimal link of V2V and V2I sharing strategies, the transmission power for vehicles sending semantic information and the length of transmitted semantic symbols, aiming at maximizing HSSE of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V.
Hybrid Digital-Analog Semantic Communications
Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom.
Federated Learning With Energy Harvesting Devices: An MDP Framework
Furthermore, for unknown channels and harvested energy statistics, we develop a structure-enhanced deep reinforcement learning algorithm that leverages the monotone structure of the optimal policy to improve the training performance.
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective
To enhance privacy, FL can be combined with Differential Privacy (DP), which involves adding Gaussian noise to the model weights.
Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck
Specifically, we propose an invariant feature encoding approach based on the IB principle and IRM framework for domainshift generalization, which aims to find the causal relationship between the input data and task result by minimizing the complexity and domain dependence of the encoded feature.
Semantic MIMO Systems for Speech-to-Text Transmission
Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits.
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.
Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL.
A Robust Semantic Communication System for Image
Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques.
Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy
Based on the proposed method, we further develop a variational representation encoding approach that simultaneously achieves fairness and LDP.
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs.
When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment
AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment.
Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO
To address the joint channel estimation and cooperative localization problem for near-field UM-MIMO systems, we propose a variational Newtonized near-field channel estimation (VNNCE) algorithm and a Gaussian fusion cooperative localization (GFCL) algorithm.
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO
In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments.
Generative AI for Physical Layer Communications: A Survey
The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity.
URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing
Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing.
Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments
Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel.
Over-the-Air Federated Learning and Optimization
We first provide a comprehensive study on the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity.
Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques
Massive multiple-input multiple-output (MIMO) has been a critical enabling technology in 5th generation (5G) wireless networks.
Binary Federated Learning with Client-Level Differential Privacy
To improve communication efficiency and achieve a better privacy-utility trade-off, we propose a communication-efficient FL training algorithm with differential privacy guarantee.
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world.
Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework
Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications.
FedNC: A Secure and Efficient Federated Learning Method with Network Coding
Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency.
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning
By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models.
Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems
As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains.
An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation
For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect.
Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO
In this paper, we propose a lightweight and flexible deep learning-based CSI feedback approach by capitalizing on deep equilibrium models.
Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation
Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance.
Over-the-Air Computation for 6G: Foundations, Technologies, and Applications
The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation.
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling
Then, we formulate the problem of selecting optimal IS weights and obtain the theoretical solutions.
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback
In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming.
Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks
We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee.
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data
In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter.
Graph Neural Networks for Wireless Communications: From Theory to Practice
For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks, which includes graph modeling, neural architecture design, and theory-guided performance enhancement.
Communication-Efficient Federated Distillation with Active Data Sampling
Federated Distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, which achieves orders of magnitude reduction of the communication overhead compared with FedAvg and is flexible to handle heterogeneous models at the clients.
Task-Oriented Multi-User Semantic Communications
For the single-modal multi-user system, we will propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to perform image retrieval and machine translation, respectively.
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning
In this paper, we develop a vertical-horizontal federated learning (VHFL) process, where the global feature is shared with the agents in a procedure similar to that of vertical FL without any extra communication rounds.
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks.
Learn to Communicate with Neural Calibration: Scalability and Generalization
Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models.
How Powerful is Graph Convolution for Recommendation?
In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing.
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Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems.
Over-the-Air Computation via Reconfigurable Intelligent Surface
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels.
Convergence Analysis and System Design for Federated Learning over Wireless Networks
Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets.
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression
Federated learning (FL) is a promising and powerful approach for training deep learning models without sharing the raw data of clients.
Decentralized Statistical Inference with Unrolled Graph Neural Networks
In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples without centralized coordination.
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory
Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients' private data.
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design
Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server's access to many clients' data and the edge servers' closeness to the clients to achieve a high communication efficiency.
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing
To the best of our knowledge, it's the first joint MEC collaboration algorithm that combines the edge federated mode with the multi-agent actor-critic reinforcement learning.
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis
In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis.
Blind Data Detection in Massive MIMO via $\ell_3$-norm Maximization over the Stiefel Manifold
Specifically, we propose a novel $\ell_3$-norm-based formulation to recover the data without channel estimation.
Complete Dictionary Learning via $\ell_p$-norm Maximization
Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics.
Sparse Optimization for Green Edge AI Inference
By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem.
Communication-Efficient Edge AI: Algorithms and Systems
By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
A Graph Neural Network Approach for Scalable Wireless Power Control
Specifically, a $K$-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph.
Mobile Edge Intelligence and Computing for the Internet of Vehicles
The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking.
Networking and Internet Architecture Signal Processing
Client-Edge-Cloud Hierarchical Federated Learning
To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation.
Towards Big data processing in IoT: network management for online edge data processing
In this paper, a MEC-based big data analysis network is discussed.
The Roadmap to 6G -- AI Empowered Wireless Networks
The recent upsurge of diversified mobile applications, especially those supported by Artificial Intelligence (AI), is spurring heated discussions on the future evolution of wireless communications.
Hybrid Beamforming for Millimeter Wave Systems Using the MMSE Criterion
A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to faster convergence.
Information Theory Information Theory
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples
To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples.
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks
A unique advantage of the proposed method is that it can tackle the task mismatch issue with a few additional unlabeled training samples, which is especially important when transferring to large-size problems.
Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
Sample simulation results shall be presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.
Information Theory Information Theory
