Reliable and efficient wireless communication channel management using optimization and artificial intelligence
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Abstract
With the growing ubiquity of mobile devices and wireless connectivity, ensuring efficient and reliable communication across various environments has become increasingly important. In densely deployed Enterprise Wireless LANs (E-WLANs), traditional strongest-signal-based Access Point (AP) selection can lead to network imbalance and suboptimal throughput. To address this, we propose an optimization-based AP selection scheme that considers user demand and AP capacity, leveraging relaxation and rounding techniques to enhance overall throughput, utilization, and fairness. Expanding beyond terrestrial applications, we explore the challenges of wireless communication in extreme space environments, where electromagnetic interference and radiation hinder reliable data transmission. To tackle this, we introduce a Machine Learning (ML)-based multi-stratum channel coordinator as part of the Resilient Internet of Space Things (ResIST), enabling dynamic and trustworthy channel selection through software-defined wireless topologies. Our simulation-based evaluations demonstrate the superior predictive accuracy of Feed- Forward Neural Networks (FFNN) in these conditions. Finally, we present AIR-CAV, an AI-assisted reliable channel selection framework for Connected and Autonomous Vehicles (CAVs), which integrates real-world and simulated data to dynamically predict signal quality across heterogeneous V2V communication links. Among various ML models evaluated, Convolutional Neural Networks (CNN) achieved the best performance in SNR prediction. Collectively, these contributions highlight the potential of intelligent, adaptive wireless communication strategies in both terrestrial and extraterrestrial domains.
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Introduction -- Jointly maximizing throughput and utilization for dense enterprise WLANs -- Machine learning-based multi-stratum channel coordinator for resilient internet of space things -- AIR-CAV: AI-assisted reliable channel selection for connected autonomous vehicles -- Conclusions and future directions
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Ph.D. (Doctor of Philosophy)