1 citations · 1 across the 6 of their papers we have counts for
7 papers
From Common Information to Canonical Correlation Analysis and Beyond: Bridging Information Theory, Signal Processing, and Machine Learning
Paris A. Karakasis, Walid Saad
Many problems in signal processing, machine learning, and neuroimaging provide several jointly observed measurements of one process, each mixing a component common to all of them w…
Latent Semantic State Estimation for Reliable Swarming of UAVs under Intermittent Connectivity
Paris A. Karakasis, Walid Saad
Cooperative multi-unmanned aerial vehicle (UAV) reconnaissance is often hindered by intermittent air-to-air communications where link dropouts lead to uncoordinated exploration and…
MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks
Yixin Zhang, Zhuohui Yao, Wenchi Cheng +1
In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks. For time-critical rescue missions, information freshness…
Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi +3
The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network covera…
Markov Modeling for Licensed and Unlicensed Band Allocation in Underlay and Overlay D2D
Po-Heng Chou, Yen-Ting Liu, Wei-Chang Chen +1
In this paper, a novel analytical model for resource allocation is proposed for a device-to-device (D2D) assisted cellular network. The proposed model can be applied to underlay an…
Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing
Bernd Simon, Andrea Ortiz, Walid Saad +1
The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the avai…
Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?
Minsu Kim, Walid Saad
In continual learning (CL), an AI agent (e.g., autonomous vehicles or robotics) learns from non-stationary data streams under dynamic environments. For the practical deployment of…