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Found 28 papers, 4 papers with code
An overview of domain-specific foundation model: key technologies, applications and challenges
The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios.
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network
Extensive research has been conducted in this field, focusing on areas such as gesture recognition, people identification, and fall detection.
Ranked #1 on
Action Classification (zero-shot)
on WiGesture
Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracy
By maximizing the minimum pair-wise discriminant gain instead of its average counterpart, any pair of classes can be better separated in the feature space, and thus leading to a balanced and improved inference accuracy for all classes.
Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications.
Collaborative Edge AI Inference over Cloud-RAN
To realize efficient uplink feature aggregation, we allow each RRH receives local feature vectors from all devices over the same resource blocks simultaneously by leveraging an over-the-air computation (AirComp) technique.
Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm
Based on our analytical results, we then propose a joint communication and computation resource management design to minimize an average squared gradient norm bound, subject to constraints on the transmit power, overall system energy consumption, and training delay.
RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks
Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning.
Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing
Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models.
Ranked #2 on
Person Identification
on WiGesture
Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN
In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications.
Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems
Simulation results demonstrate that the proposed SPEBT scheme is capable of providing precise pose estimation information and accurate beam tracking output, while reducing the proportion of beam training overhead to less than 5% averagely.
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server.
Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI
With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc.
Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning
Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics
The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that offers improved model calibration via uncertainty quantification.
Task-Oriented Over-the-Air Computation for Multi-Device Edge AI
To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy.
Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving
However, wireless channels between the edge server and the autonomous vehicles are time-varying due to the high-mobility of vehicles.
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning
To accelerate the training process, we propose a truncated vertical federated learning (T-VFL) algorithm, where the training latency is highly reduced by integrating the standard VFL algorithm with a channel-aware user scheduling policy.
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge.
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search
To further adapt to both various data distributions and different types of devices with heterogeneous embedded hardware platforms, inspired by meta-learning, a Cluster Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense that each device can learn a tailored deep learning model for its particular data distribution and hardware constraint.
Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication
This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC).
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling
Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem.
Accelerating Edge Intelligence via Integrated Sensing and Communication
Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.
Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis
The novelty of this design lies in exploiting channel noise to accelerate the descent in the region around each saddle point encountered by gradient descent, thereby increasing the convergence speed of over-the-air PCA.
One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme.
Information Theory Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Signal Processing Information Theory
Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)
To leverage the data and resources, a new machine learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users.
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed.
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning.
Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning
We hope to inspire practitioners in different fields to adopt the powerful tool of Grassmannian learning in their research.
