With the advent of new wireless technologies, it is expected that the use of Machine-Type Communication (MTC) will significantly increase in next generation wireless networks. Wireless communication networks are considered to support MTC due to their availability and existing infrastructures. As these networks are designed and optimized in a way that they fit best for Human Type Communication (HTC), there is a need of an efficient radio re- source management (RRM) to accommodate MTC traffic without affecting the regular HTC traffic in the network. In this thesis, a continuous-time Markov chain (CTMC) model-based RRM scheme is proposed to analyze the impact of MTC traffic on HTC traffic in wireless communication networks, in terms of blocking probability and channel utilization. Numerical results are provided, demonstrating the effectiveness of the proposed RRM scheme in providing the quality of service (QoS) isolation between HTC and MTC traffic.
Aggregation and Resource Scheduling in Machine-Type Communication Networks: A Stochastic Geometry Approach
