Multi-criteria handover management and resource allocation in heterogeneous RF and Optical Wireless Networks

Radio Frequency (RF) technologies face spectrum scarcity and interference in dense beyond-5G IoT deployments. Optical Wireless Communication (OWC) acts as a complementary technology; integrating OWC and RF into a heterogeneous network (HetNet) enhances capacity and reliability, but increases management complexity.

This dissertation introduces intelligent, multi-criteria frameworks to manage OWC/RF HetNets. First, the impact of spatial–temporal discretization is evaluated, showing that Signal-to-Noise Ratio (SNR) and achievable data rate experience different impacts in OWC compared to RF. Next, a multi-objective optimization model and a decentralized multi-attribute scheme are proposed for handover management in hybrid OWC/RF HetNets. Finally, a multi-criteria reinforcement learning (RL) approach is presented to balance joint handover management and resource allocation, outperforming standard baselines in convergence stability and accuracy.