Analytical Evaluation of Network Performance in Energy-Aware 6G Architectures

This thesis investigates the analytical evaluation of network performance in next-generation wireless systems, with a particular focus on energy efficiency and quality of service in 6G scenarios. The work is grounded in stochastic geometry and optimization theory, enabling scalable and tractable modeling of complex network behaviors.
Given the current research work, the focus is on three main axes:
1. Energy-Aware Load Shifting in SWIPT-Enabled IoT Networks: A novel framework is developed to optimize load distribution in networks where devices harvest energy via Simultaneous Wireless Information and Power Transfer (SWIPT). The model accounts for nonlinear energy dynamics and device duty cycling, enabling adaptive provisioning strategies that reduce energy consumption while maintaining QoS for both communication and energy harvesting.
2. Hybrid Network Sharing Strategies for Multi-Operator Cellular Systems: Back to more classical mobile networks, the work provides an analytical model to assess the benefits of collaborative network sharing among mobile network operators. By integrating sleep modes and cooperative resource allocation, the framework quantifies energy savings and service resilience under realistic traffic and deployment conditions. Results show that full sharing strategies can yield up to 35% energy savings compared to independent operator management.
3. Moving Networks and Dynamic Infrastructure Optimization: The potential of ground vehicles as mobile base stations is explored through a stochastic geometry-based model that incorporates wireless backhauling and user mobility. An optimization framework is proposed to minimize infrastructure deployment while ensuring QoS. The analysis reveals that moving networks can significantly reduce CAPEX by reusing mobile base stations across regions with complementary traffic patterns.
Across these contributions, the thesis emphasizes the importance of analytical tools for evaluating and optimizing network performance in energy-constrained environments. The proposed models offer insights into the trade-offs between infrastructure cost, energy consumption, and service quality, paving the way for sustainable and adaptive 6G network design.