Adaptive Back-Off and Retransmission Strategies for Efficient and Scalable Preamble Allocation in 5G Nr Critical Mmtc

Critical massive machine-type communications (cmMTC) in 5G networks face significant challenges due to congestion and resource contention. This paper proposes a random back-off mechanism within an adaptive back-off window and an adaptive retransmission strategy designed to enhance resource allocation in cmMTC scenarios through a groupingbased approach. Unlike traditional random-access mechanisms, our method mitigates access failures by incorporating a random back-off within an adaptive back-off window and an adaptive retransmission mechanism for devices that initially fail to access the network. By optimizing the back-off and retransmission process, the strategy significantly reduces access latency, enhances reliability, and minimizes collisions. Through analytical modeling and extensive simulations, we demonstrate that our approach outperforms conventional random-access methods in terms of collision probability, access success rate, and average transmission delay. The results confirm that our load adaptive selection strategy effectively balances network load while maintaining high connection rates, making it a robust and scalable solution for future 5G-enabled IoT applications.