User location prediction based cell discovery scheme for user-centric ultra-dense networks

Abstract

Ultra-Dense Network (UDN) is foreseen as one of the promising technologies in 5G to meet the ever-increasing mobile traffic demands. However, with existing cell discovery mechanisms, such massive deployment of access points (APs) would lead to a considerable increase in energy consumption and a significant reduction in offloading opportunity. To solve those problems, a User Location Prediction based Cell Discovery (ULPCD) scheme for User-centric Ultra-Dense Network (UUDN) is proposed. In the scheme, the network deployment information is sent to the user equipment (UE) the first time user enters in the range of cells. Then, with the aid of positioning information, the probability of a user entering into a new cell is calculated. Using the probability, UE can predict whether to send a connection request to activate an idle AP before entering in the AP's cell range. Simulation results demonstrate that our proposed scheme reduces over 60% energy consumption of UE and saves at least 20% working time of APs compared with state-of-the-art schemes. Meanwhile, our scheme provides higher detection probability, shorter access delay, and more offloading opportunity.