Author Type

Graduate Student

Date of Award

2024

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Pados, Dimitris

Abstract

The aim of this dissertation is to develop a comprehensive framework for designing optimal AI/ML-driven waveform solutions to achieve autonomous interference avoidance in fixed frequency bands. In the age of advanced wireless communications, minimizing interference is critical for maximizing the signal-to-interference-plus-noise ratio (SINR), particularly in densely occupied frequency environments. The research presented here focuses on developing adaptive MIMO waveform optimization techniques that dynamically adjust to varying interference conditions, enhancing communication reliability and performance for future autonomous machine-to-machine (M2M) networks. In addition to the established adaptive MIMO waveform optimization techniques, this dissertation investigates the implementation of AI-enhanced methods, to improve real-time adaptability in interference-rich environments. By leveraging neural networks, the framework enables the MIMO system to autonomously learn optimal waveform adjustments, providing resilience and efficiency under unpredictable interference conditions. This approach is validated through extensive simulations and experimental setups, demonstrating significant gains in SINR and overall communication reliability, marking a robust advancement toward achieving fully autonomous interference-avoiding communication in 6G and beyond networks. The AI-driven techniques further enhance the adaptability of the MIMO system across diverse interference scenarios, contributing to more consistent performance. These improvements offer a scalable approach for interference avoidance, adaptable to various network configurations.

Extent

100 p.

Comments

Includes bibliography.|Dissertation (PhD)--Florida Atlantic University, 2024.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00014559

Format

pdf

Download

Share

COinS