Adaptive Capacity Optimization Algorithm Leveraging Joint PHY-MAC Layer Modeling for Dual-Mode Communication Systems

The extensive deployment of the Power Internet of Things (PIoT) relies on dual-mode communication (HPLC + HRF) for robust data acquisition. However, under massive bursty traffic, conventional static MAC superframe scheduling struggles to reconcile high throughput with stringent reliability constraints. To mitigate this, we propose a dynamic adaptive scheduling scheme. Initially, a joint PHY-MAC layer dual-mode system architecture is proposed. At the MAC layer, a dual-link parallel multiplexing contention access mechanism is applied; at the physical layer, a capacity bottleneck determination model is established, incorporating log-normal–Bernoulli–Gaussian mixed noise and multipath fading. Subsequently, an extended two-dimensional Markov chain analytically derives key performance indicators, including equivalent collision probability, joint outage probability, access delay, and network throughput. Building upon this, a Q-learning-based algorithm is proposed. By constructing an asymmetric penalty–reward function, the central coordinator (CCO) autonomously optimizes the Contention Access Period (CAP) to Contention-Free Period (CFP) ratio under dynamic node scales. Simulations demonstrate this methodology effectively averts channel congestion during extreme concurrent traffic surges. Ultimately, it strictly preserves service reliability while substantially augmenting the concurrent carrying capacity and resource utilization of the dual-mode network.