From the 1 of 66 linked papers with an AI index.
9 citations · 16 across the 15 of their papers we have counts for
5 papers · 1 filter
Data-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning
Amin Farajzadeh, Hongzhao Zheng, Sarah Dumoulin +3
Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication ne…
Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction
Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu
Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss prediction…
Investigating Map-Based Path Loss Models: A Study of Feature Representations in Convolutional Neural Networks
Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu
Path loss prediction is a beneficial tool for efficient use of the radio frequency spectrum. Building on prior research on high-resolution map-based path loss models, this paper st…
Federated Learning in NTNs: Design, Architecture and Challenges
Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu
Non-terrestrial networks (NTNs) are emerging as a core component of future 6G communication systems, providing global connectivity and supporting data-intensive applications. In th…
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning
Mustafa Ghaleb, Mohanad Obeed, Muhamad Felemban +2
Federated Learning (FL) has emerged as a significant paradigm for training machine learning models. This is due to its data-privacy-preserving property and its efficient exploitati…