Fuzzy Rule-Based Reinforcement Learning for Load Balancing Techniques in Enterprise LTE Femtocells

Mobile-broadband traffic has experienced a large increase over the past few years. Femtocells are envisioned to cope with such a demand of capacity in indoor environments. Since those small cells are low-cost nodes, a thorough deployment is not typically performed, particularly in enterprise scenarios. As a result, the matching between traffic demand and network resources is rarely optimal. In this paper, several load balancing techniques based on self-tuning of femtocell parameters are designed to solve localized congestion problems. In particular, these techniques are implemented by fuzzy logic controllers (FLC) and fuzzy rule-based reinforcement learning systems (FRLSs). Performance assessment is carried out in a dynamic system-level simulator. Results show that the combination of FLC and FRLS produces an increase in performance that is significantly higher than if techniques are implemented alone. Both the response time and the final value of performance indicators are improved.