Optimized resource allocation techniques for critical machine-type communications in mixed LTE networks

To implement the revolutionary Internet of Things (IoT) paradigm, the evolution of the communication networks to incorporate machine-type communications (MTC), in addition to conventional human-type communications (HTC) has become inevitable. Critical MTC, in contrast to massive MTC, represents that type of communications that requires high network availability, ultra-high reliability, very low latency, and high security, to enable what is known as mission-critical IoT. Due to the fact that cellular networks are considered one of the most promising wireless technologies to serve critical MTC, the International Telecommunication Union (ITU) targets critical MTC as a major use case, along with the enhanced mobile broadband (eMBB) and massive MTC, in the design of the upcoming generation of cellular networks. Therefore, the Third Generation Partnership Project (3GPP) is evolving the current Long-Term Evolution (LTE) standard to efficiently serve critical MTC to fulfill the fifth-generation (5G) requirements using the evolved LTE (eLTE) in addition to the new radio (NR). In this regard, 3GPP has introduced several enhancements in the latest releases to support critical MTC in LTE, which is designed mainly for HTC. However, guaranteeing stringent quality-of-service (QoS) for critical MTC while not sacrificing that of conventional HTC is a challenging task from the radio resource management perspective. In this dissertation, we optimize the resource allocation and scheduling process for critical MTC in mixed LTE networks in different operational and implementation cases. We target maximizing the overall system utility while providing accurate guarantees for the QoS requirements of critical MTC, through a cross-layer design, and that of HTC as well. For this purpose, we utilize advanced techniques from the queueing theory and mathematical optimization. In addition, we adopt heuristic approaches and matching-based techniques to design computationally-efficient resource allocation schemes to be used in practice. In this regard, we analyze the proposed methods from a practical perspective. Furthermore, we run extensive simulations to evaluate the performance of the proposed techniques, validate the theoretical analysis, and compare the performance with other schemes. The simulation results reveal a close-to-optimal performance for the proposed algorithms while outperforming other techniques from the literature.