Indrajit Kumar Paul*, Mitali Halder
In a femtocell network , which is configured as open access, a user from a neighboring cell preferably from a different type of cell eg.(Macro, Pico or micro cell), can make handover to the femtocell network through handover for better coverage and enhance there channel capabilities for better user experience. To avoid any disruption of service for users, which can happen because of ping-pong HO ( handover) it is mandatory to have a effective cell selection method should be in place. In Traditional approach this cell selection method uses RSSI /RSRP value obtained by measurement report, cell load, channel quality etc. to make decision for cell selection for HO. However problem with traditional based approach is that present measured performance does not necessarily reflect the future performance, thus the need for some kind of smart cell selection that can predict the horizon. Subsequently, we present in this paper a reinforcement learning (RL), i.e Q-learning algorithm, as a generic solution for the cell selection problem in a femtocell network.
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Telecommunications System & Management received 109 citations as per Google Scholar report
