Mission-critical communication is one of the central design aspects of 5G communications. But there are numerous challenges and explicit requirements for development of a successful mission-critical communication system. Reliability and delay optimization are the two most crucial among them. Achieving reliability is influenced by several difficulties, including but not limited to fading, mobility, interference, and inefficient resource utilization. Achieving reliability may cost one of the most critical features of mission critical communication, which is delay. This thesis discusses possible strategies to achieve reliability in a mission-critical network. Based on the strategies, a framework for a reliable mission-critical system has also been proposed. A simulation study of the effects of different pivotal factors that affect communication channel is described. This study provides a better understanding of the requirements for improving the reliability of a practical communication Supervisory Committee

In this paper, we propose a supervised‐learning‐based spatial performance prediction (SLPP) framework for next‐generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine‐learning ubiquitous for accurate data‐based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis‐based prediction approach has been proposed in this paper. Comparison results with different machine‐learning techniques on real‐world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

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