Optimal Cross-Layer Resource Allocation for Critical MTC Traffic in Mixed LTE Networks

Machine-type communications (MTC) play an important role in implementing and enabling the Internet of Things. Long -term evolution (LTE) is a strong candidate technology for the interconnection of the MTC devices. However, to optimize LTE for MTC purposes, several issues need to be addressed. This is due to the different and diverse quality of service (QoS) requirements of MTC compared to those of human-to-human (H2H) communications. In particular, critical-MTC pose many challenges to radio resource management in LTE. They have stringent QoS requirements that need to be considered without sacrificing the QoS of the H2H traffic. In this paper, we formulate the resource allocation optimization problem from a cross-layer design perspective to consider both the QoS requirements of critical-MTC and those of H2H communications. We propose methods to handle the optimization problem to reduce the computational complexity of the optimal solution. Additionally, the performance of the proposed resource allocation algorithms is evaluated analytically. Moreover, more computationally efficient algorithms are proposed to practically implement the resource allocation process in several operational cases. Finally, the computational complexity of the proposed algorithms is analyzed. The simulations results show the superiority of the proposed algorithms and methods compared to other techniques from the literature.