SDN-enabled Congestion Control Coordination and Coverage Class Adaptation in 5G NB-IoT Networks

In massive and densely deployed 5G NB-IoT networks, centralized managements are computationally prohibitive and limit the network scalability. As a result, the auto-integration and self-management capabilities are required in the 5G NB-IoT architectural to realize their full potential. In this paper, we construct a novel analytical model to estimate the random access performance of 5G NB-IoT network from an individual perspective. Then, based on the individual-based analytical model, we propose to decouple random access control management in 5G NB-IoT networks into coverage class adaptation and congestion control coordination, so that the critical issues of traffic burstiness and network scalability can be tackled. In particular, the random access control is progressively coordinated through a centralized SDN controller, and the coverage class adaptation can be achieved gradually through IoT devices under the guidance of the SDN controller. As a result, bursty traffics are equally dispersed among coverage classes, and the requirement of network control on complexity can be substantially alleviated, simplifying access control management and enhancing network scalability in a massive deployed NB-IoT networks. Case studies show that our SDN-enabled network control is effective for timely load balance.