Network Throughput Optimization for Random Access Narrowband Cognitive Radio Internet of Things (NB-CR-IoT)

Narrowband Internet of Things (NB-IoT) is a new technology being implemented into the Long-Term Evolution (LTE) standards to support machine-to-machine communications. Applications of IoT are expected to rapidly increase and to expand into many sectors. Increased number of IoT devices trying to access the medium with typically small data packets will overwhelm the network. Cognitive radios coupled with random access strategies can help alleviate excessive collisions in the network by many IoT access attempts. On the other hand, most IoT devices do not have hardware capabilities to perform sophisticated spectrum sensing methods over long durations. Therefore, it is important to reduce the spectrum sensing overhead while maximizing the NB-IoT network level throughput. In this paper, we derive a unique set of optimal sensing parameters which achieve the maximum throughput of a narrowband cognitive radio IoT (NB-CR-IoT) network. We show that accurate sensing from a single device perspective is not always the best solution for maximizing the throughput in an NB-CR-IoT network.