Adaptive access class barring for efficient mMTC

Abstract In massive machine-type communications (mMTC), an immense number of wireless devices communicate autonomously to provide users with ubiquitous access to information and services. The current 4G LTE-A cellular system and its Internet of Things (IoT) implementation, the narrowband IoT (NB-IoT), present appealing options for the interconnection of these wireless devices. However, severe congestion may arise whenever a massive number of highly-synchronized access requests occur. Consequently, access control schemes, such as the access class barring (ACB), have become a major research topic. In the latter, the precise selection of the barring parameters in a real-time fashion is needed to maximize performance, but is hindered by numerous characteristics and limitations of the current cellular systems. In this paper, we present a novel ACB configuration (ACBC) scheme that can be directly implemented at the cellular base stations. In our ACBC scheme, we calculate the ratio of idle to total available resources, which then serves as the input to an adaptive filtering algorithm. The main objective of the latter is to enhance the selection of the barring parameters by reducing the effect of the inherent randomness of the system. Results show that our ACBC scheme greatly enhances the performance of the system during periods of high congestion. In addition, the increase in the access delay during periods of light traffic load is minimal.