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Found 34 papers, 6 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.
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.
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.
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.
Sensing-aided Near-Field Secure Communications with Mobile Eavesdroppers
The additional degree of freedom (DoF) in the distance domain of near-field communication offers new opportunities for physical layer security (PLS) design.
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
In this work, we introduce a holistic approach by joint uplink and downlink adaptive quantization to reduce the communication overhead.
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design
One of the most critical challenges for deploying distributed learning solutions, such as federated learning (FL), in wireless networks is the limited battery capacity of mobile clients.
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).
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.
Sensing-assisted Robust SWIPT for Mobile Energy Harvesting Receivers in Networked ISAC Systems
Simultaneous wireless information and power transfer (SWIPT) has been proposed to offer communication services and transfer power to the energy harvesting receiver (EHR) concurrently.
Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection
Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are critical.
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning
In particular, the teacher model in FedSDD is an ensemble built by a small group of aggregated (global) models, instead of all client models, such that the computation cost will not scale with the number of clients.
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.
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.
How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels
It is also shown that the uplink communication in FL can tolerate a higher bit error rate (BER) than downlink communication, and this difference is quantified by a proposed formula.
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.
Power Allocation for Device-to-Device Interference Channel Using Truncated Graph Transformers
Power control for the device-to-device interference channel with single-antenna transceivers has been widely analyzed with both model-based methods and learning-based approaches.
Fairness-aware Federated Minimax Optimization with Convergence Guarantee
Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature.
Local SGD Accelerates Convergence by Exploiting Second Order Information of the Loss Function
However, existing analysis failed to explain why the multiple local updates with small mini-batches of data (L-SGD) can not be replaced by the update with one big batch of data and a larger learning rate (SGD).
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.
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication
Different from conventional radar, the cellular network in the integrated sensing and communication (ISAC) system enables collaborative sensing by multiple sensing nodes, e. g., base stations (BSs).
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.
Iterative Sparse Recovery based Passive Localization in Perceptive Mobile Networks
As a result, most existing methods require a large number of data samples to achieve an accurate estimate of the covariance matrix for the received signals, based on which a power spectrum is constructed for localization purposes.
SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling
Downsampling is widely adopted to achieve a good trade-off between accuracy and latency for visual recognition.
FedDQ: Communication-Efficient Federated Learning with Descending Quantization
Experimental results show that the proposed descending quantization scheme can save up to 65. 2% of the communicated bit volume and up to 68% of the communication rounds, when compared with existing schemes.
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.
