Electrical engineering

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.

This dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation, driving the adoption of P2P energy markets. It also builds on demand-side payment mechanisms like NRG-X-Change by adapting it to provide fair payment distribution to prosumer coalitions. The interoperable energy storage systems with P2P trading also presented battery chemistry detection using neural network models. A fuzzy inference system is also designed to facilitate prosumers' choice in participating in P2P markets, providing flexibility for energy trading preferences. The simulation results demonstrated the effectiveness of the proposed design schemes.