Reliable and efficient wireless communication channel management using optimization and artificial intelligence

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.

Name thesis:degree_name

Ph.D. (Doctor of Philosophy)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2025

Author dc:creator

• Islam, Md Tajul

Advisor dc:contributor.advisor

• Choi, Baek-Young

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10355/109281

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/109281