The Research On MTC Access And Data Aggregation For 5G Network Slicing

With the rapid development of The Fifth Generation(5G)communication technology,the urgent need of users for diverse applications is currently a huge challenge.Specifically,the massive connectivity and the low latency requirement of Machine Type Communication(MTC)in the Internet of Things(IoT)are issues which are urgently need to be solved in current mobile communication networks.For MTC applications,it is widely recognized that traditional cellular network architecture is not adequate to meet the requirements in terms of massive connectivity and low latency.Recently network slicing has been identified as a promising network architectural technology for 5G to address the challenges stemming from a wide range of applications,which provided a new technical approach for the development of 5G.The focus of this paper is to study the access and data aggregation in the scenario of MTC for 5G network slicing.In this MEng thesis we conduct following research from three aspects:First of all,in the MTC access scenario for 5G network slicing,different from conventional Data Aggregation(DA)scheme where data aggregation is performed based on device locations,we perform DA according to latency requirement for MTC devices(MTCDs),with aim to exploit the benefits of network slicing and thus improve network access capacity and decrease access latency.We formulate the DA problem as a 0-1 Linear Programming and propose an efficient Network Slicing Data Aggregation(NSDA)algorithm to aggregate the MTC data for accessing a specific network slice.We examine the performance of our proposed NSDA in typical MTC scenarios via simulations.Numerical results reveal that NSDA significantly outperforms traditional MTC access schemes(without network slicing)in terms of network capacity,access congestion degree,latency,etc.Secondly,in the MTC access scenario for 5G network slicing,we conduct the dynamic access procedure for MTC applications.Specifically,according to MTC service characteristics and network slicing technology,we compute the MTC random access resource allocation method theoretically.The scheme includes RACH resource allocation,access level restriction and priority network slicing setting scheme.We propose the Network Slicing Random Access(NSRA)scheme to dynamically allocate reasonable Resources to different priority MTC devices to meet their QoS.In turn,we set up three network environments to compare the proposed NSRA scheme with the classic MTC access scheme.Simulation results show that the proposed NSRA scheme has good MTC access performance compared to other schemes.Finally,in the MTC access scenarios for 5G network slicing,we propose a dynamic RB resource allocation strategy for MTC applications.This scheme aims to minimize the average access delay of the network according to the QoS requirements of each MTC access slice.We formulate the Resource Block resource allocation problem as MDP model,and solved it using Actor-Critic(AC)algorithm in reinforcement learning.The simulation shows that the proposed RB resource allocation algorithm can reasonably allocate resources for each MTC access slice to ensure the QoS requirements of each MTCDs.Compared with the greedy algorithm and the random access scheme,AC algorithm has the best performance.