TRACKING AREA RECONFIGURATION AND OPTIMIZATION FOR 5G CELLULAR NETWORKS

Abstract

5G is expected to accommodate exceptional services beyond current cellular systems. The three main use cases envisioned for 5G networks are enhanced Mobile Broadband, massive Machine Type Communication and Ultra Reliable and Low Latency Communi- cation. The enormous competition and the increasing number of UEs in the telecom- munication market have garnered attention towards the necessity of optimized and cost- e cient network. Tracking the position of UEs while minimizing the signalling cost is a key challenge that needs to be addressed for the upcoming 5G networks. Tracking area (TA) is a term used for the assemblage of cells in long term evolution (LTE) network. Planned distribution of TAs, depending on the mobility pattern of UEs, is a technique used to optimize the signalling overhead, and plays a vital role in utilizing the resources e ciently. Compared to the earlier generations of cellular networks, 5G networks will have more exible conguration of TAs as it will utilize the concept of TA list, given in 3GPP release 12. One of the objectives of 5G mobile network is to cope up with an ever increasing number of UEs. This thesis discusses TA recon guration for 5G network using game theory, and proposes an optimal solution for reducing the signalling overhead of the network. In order to satisfy the 5G requirements, heterogeneous radio access technologies (RATs) have been proposed. To use the RAT e ciently, massive connectivity of devices has been proposed in 3GPPP Release 15. For the earlier phase, non-standalone architecture has been proposed in which existing 4G LTE infrastructure will be used. To have a full 5G network coverage stand alone architecture has been proposed in 3GPP release 15 for which low-cost and low-power small base-stations (microcells) will be deployed, and these power supplied active nodes will form the clusters as per proposed algorithm. Increasing the number of devices will give rise to signalling overhead issue consisting of TA update (TAU) and paging overhead. This thesis proposes a method to reduce the signalling cost using clustering algorithm and the uni cation of both the layers (4G and 5G).