Context-Aware Dynamic Resource Allocation for Cellular M2M Communications

While the huge number of machine-to-machine (M2M) devices connects to the LTE system, the bursty random access attempts from them are potential to cause severe random access collisions and degrade the successful probability as well as delay of network connection establishment. To address this issue, current LTE spec has specified access class barring (ACB) and enhanced access barring (EAB), but both methods lack a contention detection method to decide when to activate it as well as a proper way to dynamically adjust the parameter. In this work, we first proposed a contention detection method that can work with current LTE standard and is easy to be implemented. To further improve the random access performance in dynamic world, we proposed CADRA, a context-aware dynamic resource allocation mechanism, which is a two-phase method to resolve random access contention: phase I for the estimation of random access attempts, and phase II for resource allocation. By CADRA, high resource efficiency and low random access delay can be achieved without prior knowledge about random access arrival traffic, and thus this approach is competent in diverse applications and scenarios of Internet of Things (IoT). Simulation results show that the proposed contention detection method works great with ACB and EAB. Our proposed CADRA have good performance in resource efficiency and delay while satisfying success probability.