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Found 18 papers, 6 papers with code
Green Edge AI: A Contemporary Survey
Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
Multi-Device Task-Oriented Communication via Maximal Coding Rate Reduction
In this paper, we consider a task-oriented multi-device edge inference system over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i. e., feature encoding and classification) and communication (i. e., precoding) modules are designed with the same goal of inference accuracy maximization.
Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels
Consequently, relying solely on communication noise, as done in the multiple-input single-output system, cannot meet high privacy requirements, and a device-side privacy-preserving mechanism is necessary for optimal DP design.
Deployment Optimization of Dual-functional UAVs for Integrated Localization and Communication
In emergency scenarios, unmanned aerial vehicles (UAVs) can be deployed to assist localization and communication services for ground terminals.
CFLIT: Coexisting Federated Learning and Information Transfer
We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system.
Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning
Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices.
Relay-Assisted Cooperative Federated Learning
Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large.
Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inference
In practice, the DNN model is re-trained and updated periodically at the edge server.
Dynamic Trajectory and Offloading Control of UAV-enabled MEC under User Mobility
In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals.
Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning
In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system.
CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface
We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable.
Privacy-Preserving Distributed Optimal Power Flow with Partially Homomorphic Encryption
In this way, the dual update of ADMM can be encrypted by PHE.
Semi-Blind Cascaded Channel Estimation for Reconfigurable Intelligent Surface Aided Massive MIMO
In a RIS-aided MIMO system, the acquisition of channel state information (CSI) is important for achieving passive beamforming gains of the RIS, but is also challenging due to the cascaded property of the transmitter-RIS-receiver channel and the lack of signal processing capability of the passive RIS elements.
Bayesian Inference
Information Theory
Information Theory
Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
However, due to the heterogeneity of communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance.
Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks
In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints.
Edge-computing
Networking and Internet Architecture
Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunities
Reconfigurable intelligent surfaces (RISs) are regarded as a promising emerging hardware technology to improve the spectrum and energy efficiency of wireless networks by artificially reconfiguring the propagation environment of electromagnetic waves.
Information Theory Signal Processing Information Theory
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.
Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks
To tackle this problem, we propose in this paper a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network to generate offloading decisions.
Networking and Internet Architecture
