Abstract
This thesis investigates energy-efficient load balancing in homogeneous multi-band cellular networks through the joint design of user association (UA) and transmit power allocation (PA). The original mixed-integer nonlinear formulation is decomposed into two coupled yet tractable subproblems: a UA stage and a PA stage for high-frequency bands. For UA, a SINR-ratio-based heuristic is proposed to prioritize users that are most sensitive to suboptimal band assignments, and it is benchmarked against a Max- SINR baseline. For PA, the high-band power control problem is addressed using reinforcement learning, where a Proximal Policy Optimization (PPO) agent learns power levels and band-activation decisions under QoS constraints while accounting for dropped users and load balancing via Jain’s fairness index. The framework is evaluated using extensive simulations under both LOS and NLOS propagation, and is further extended to mobility scenarios to study time-varying channels and handover behavior. Results show that PPO-based power control is a primary driver of energy efficiency gains, while the proposed UA provides complementary improvements in fairness and connectivity, with trade-offs that depend on propagation conditions and reward weighting. Sensitivity analyses (including varying user density and the fraction of NLOS segments along trajectories) characterize robustness and reveal regimes where the joint UA+PA design improves energy efficiency while controlling dropped users, fairness degradation, and handover dynamics.
School
School of Sciences and Engineering
Department
Electronics & Communications Engineering Department
Degree Name
MS in Electronics & Communication Engineering
Graduation Date
Summer 6-15-2026
Submission Date
2-11-2026
First Advisor
Karim Seddik
Committee Member 1
Karim Banawan
Committee Member 2
Mohammed Nafie
Extent
88 p.
Document Type
Master's Thesis
Institutional Review Board (IRB) Approval
Not necessary for this item
Disclosure of AI Use
Thesis text drafting; Thesis editing and/or reviewing
Recommended Citation
APA Citation
El Soukkary, A. S.
(2026).Energy Efficient Load Balancing in Multi-band Cellular Networks via Reinforcement Learning [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2743
MLA Citation
El Soukkary, Ahmed Shoukry. Energy Efficient Load Balancing in Multi-band Cellular Networks via Reinforcement Learning. 2026. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2743