Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation

This paper focuses on proposing a new access barring scheme for internet of things (IoT) devices in long term evolution advanced (LTE/LTE-A) and 5G networks. Massive number of IoT devices communicating simultaneously is one of the hallmarks of the future communication networks such as 5G and beyond. The problem of congestion also comes with this massive communication for which access barring is one of the solutions. So, it is required that sophisticated access barring techniques are designed such that the congestion is avoided and these devices get served in less time. Legacy access barring schemes like access class barring (ACB) and extended access barring (EAB) suffer from high energy consumption and high access delay respectively. However, our proposed scheme provides less energy consumption than ACB while giving less access delay than EAB. The proposed scheme maximizes the success probability while reducing the number of collisions at the same time. The scheme is based on an approximation of the number of IoT devices based on details available to the eNodeB of the number of idle, successful and collided preambles. Extensive Matlab simulations are performed to validate our claims and analysis.