MK-SPMA: A Markov-Chain-Based Priority Escalation Protocol for Multi-class UAV Communications

Unmanned aerial vehicle (UAV) clusters in post-disaster emergency communications require reliable and low-latency multiple access mechanisms to ensure real-time data delivery under dynamic network conditions. This paper introduces a Markov-based Statistical Priority Multiple Access (MK-SPMA) scheme, which incorporates a priority-aware Markov chain into the conventional SPMA framework. By dynamically upgrading data packets to higher-priority classes once the retransmission count reaches a predefined threshold, MK-SPMA enables adaptive priority escalation beyond the initial assignment, ensuring that persistently delayed packets gain increased transmission opportunities. By adaptively adjusting transmission probabilities based on both priority levels and historical transmission outcomes, MK-SPMA achieves more efficient channel access control and resource utilization. Analytical results provide closed-form expressions for state transition probabilities, steady-state distributions, throughput, delay, success probability, and slot utilization. Extensive MATLAB simulations demonstrate that, under identical configurations, MK-SPMA consistently outperforms traditional SPMA in terms of throughput, latency, and slot efficiency, particularly in dense multi-priority scenarios. These results highlight the potential of Markovian adaptation with priority-upgrade mechanisms to enhance mission-critical UAV communications, offering a promising solution for reliable and efficient post-disaster network access.