An LTE-based optimal resource allocation scheme for delay-sensitive M2M deployments coexistent with H2H users

Machine-to-Machine (M2M) communication is considered one of the essential elements of the Internet of Things (IoT) for enabling objects to communicate over potentially large distances. It is predicted to be the dominant source of traffic in 5G networks. To enable M2M communications on LTE networks, which is considered the main cellular access technology for M2M communications, several issues need to be addressed while managing radio resources. One of the major issues is the scheduling of delay-sensitive M2M applications which require strict delay constraints in the existence of traffic types of other needs such as Human-to-Human (H2H) traffic. In this paper, we propose an optimal resource allocation scheme for delay-sensitive M2M applications that considers their delay requirements with statistical guarantees while not impacting the Quality of Service (QoS) of H2H traffic. We formulate the optimization problem and propose solution techniques that do not require high computational complexity. We also analytically derive the performance metrics of the proposed algorithm and validate them using simulations. The results show that the proposed algorithm exhibits better performance in comparison with other scheduling algorithms from past studies.