Performance analysis of cognitive-based radio resource allocation in multi-channel LTE-A networks with M2M/H2H coexistence

Efficient radio access strategies are necessary and critical to manage an long-term-evolution (LTE) network system where machine-to-machine (M2M) devices and human-to-human (H2H) users coexist. In this study, the authors propose a cognitive-based access strategy with a priority-based queuing model that is designed for LTE with M2M/H2H coexistence, where the M2M communications have real-time (M2M-RT) and non-real-time (M2M-NRT) traffic. Radio access gives the highest priority to H2H, while M2M-RT has higher priority than M2M-NRT. A continuous-time Markov chain model is developed to evaluate the system performance in terms of service completion rate, blocking and forced termination probabilities, and mean queuing delay of the M2M traffic. In addition, resource utilisation by the M2M traffic is also evaluated. Analytical results reveal that, while protecting the H2H services, the proposed queuing model could increase the capacity of the M2M traffic network while decreasing the blocking probability. Additionally, allowing an interrupted M2M-NRT to be inserted back into its queue can further decrease the forced termination rate. For these reasons, it can be concluded that the proposed model can be used to improve the system performance of multi-channel M2M communication networks.