Access Control in NB-IoT Networks: A Deep Reinforcement Learning Strategy

Abstract

The Internet of Things (IoT) is a key enabler of the digital mutation of our society. Driven by various services and applications, Machine Type Communications (MTC) will become an integral part of our daily life, over the next few years. Meeting the ITU-T requirements, in terms of density, battery longevity, coverage, price, and supported mechanisms and functionalities, Cellular IoT, and particularly Narrowband-IoT (NB-IoT), is identified as a promising candidate to handle massive MTC accesses. However, this massive connectivity would pose a huge challenge for network operators in terms of scalability. Indeed, the connection to the network in cellular IoT passes through a random access procedure and a high concentration of IoT devices would, very quickly, lead to a bottleneck. The latter procedure needs, then, to be enhanced as the connectivity would be considerable. With this in mind, we propose, in this paper, to apply the access class barring (ACB) mechanism to regulate the number of devices competing for the access. In order to derive the blocking factor, we formulated the access problem as a Markov decision process that we were able to solve using one of the most advanced deep reinforcement learning techniques. The evaluation of the proposed access control, through simulations, shows the effectiveness of our approach compared to existing approaches such as the adaptive one and the Proportional Integral Derivative (PID) controller. Indeed, it manages to keep the proportion of access attempts close to the optimum, despite the lack of accurate information on the number of access attempts.

Keywords

• cellular IoT
• massive access
• reinforcement learning
• access control
• congestion control