Search Results for author:
Found 11 papers, 4 papers with code
Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning
Targeting the notion of compact yet robust DNN models, we propose the integration of iterative magnitude pruning (IMP) of the DNN model being trained in an over-the-air FL (OTA-FL) environment for IIoT.
Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach
To this end, we propose and design a hybrid TL-aided approach that leverages the advantages of both policy reuse and distillation TL methods to provide safe and accelerated convergence in DRL-based O-RAN slicing.
How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?
RAN slicing, a critical component of the O-RAN paradigm, enables network resources to be allocated based on the needs of immersive services, creating multiple virtual networks on a single physical infrastructure.
Using Early Exits for Fast Inference in Automatic Modulation Classification
This paper proposes the application of early exiting (EE) techniques for DL models used for AMC to accelerate inference.
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback
The recent advances in machine learning and deep neural networks have made them attractive candidates for wireless communications functions such as channel estimation, decoding, and downlink channel state information (CSI) compression.
The Internet of Senses: Building on Semantic Communications and Edge Intelligence
On the other hand, AI/ML facilitates frugal network resource management by making use of the enormous amount of data generated in IoS edge nodes and devices, as well as by optimizing the IoS performance via intelligent agents.
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks
Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks.
Segmented Learning for Class-of-Service Network Traffic Classification
The commonality of statistical features among the network flow segments motivates us to propose novel segmented learning that includes essential vector representation and a simple-segment method of classification.
Virtual Reality Gaming on the Cloud: A Reality Check
To the best of the authors' knowledge, this is the first measurement study and analysis conducted using a commercial cloud VR gaming platform, and under both fixed and adaptive bitrate streaming.
Structure-aware reinforcement learning for node-overload protection in mobile edge computing
Mobile Edge Computing (MEC) refers to the concept of placing computational capability and applications at the edge of the network, providing benefits such as reduced latency in handling client requests, reduced network congestion, and improved performance of applications.
Delay-Tolerant Constrained OCO with Application to Network Resource Allocation
We consider online convex optimization (OCO) with multi-slot feedback delay, where an agent makes a sequence of online decisions to minimize the accumulation of time-varying convex loss functions, subject to short-term and long-term constraints that are possibly time-varying.
