Open AccessCell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks
Author(s) -
Ammar Abdulrazzak Bathich,
Saiful Izwan Suliman,
H. M. Asri H. Mansor,
Sinan Ghassan Abid Ali,
Raed Abdullah
Publication year - 2021
Publication title -
journal of ict research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.145
H-Index - 11
eISSN - 2338-5499
pISSN - 2337-5787
DOI - 10.5614/itbj.ict.res.appl.2021.15.1.4
Subject(s) - femtocell , computer science , handover , macrocell , computer network , quality of service , throughput , user equipment , lte advanced , cellular network , base station , selection (genetic algorithm) , heterogeneous network , wireless network , telecommunications link , wireless , artificial intelligence , telecommunications
Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased.