Multichannel intelligent access to unmanned aerial vehicle assisted power information collection network based on power allocation optimization algorithm

The rapid development of the power Internet of Things has exposed obvious shortcomings in traditional information collection networks in terms of high concurrent access, link stability, and energy consumption control. To improve the access efficiency and reliability of large-scale power consumption information collection scenarios, this study constructs an unmanned aerial vehicle (UAV)-assisted multichannel intelligent access method based on a power allocation optimization algorithm. It integrates the collaborative architecture of nonorthogonal multiple access-mobile edge computing (NOMA-MEC) through dual-layer UAV to achieve integrated optimization of terminal access, task offloading, and power control. Experimental results revealed that under high load when the number of terminals increased to 600, the access success rate of the research method still remained 94.25%, which was better than 76.80% of greedy NOMA and 55.40% of random NOMA. In the task offloading scenario, when the task size reached 3.0 Mbits, the local calculation exceeded 500 ms. The research method could stabilize the delay within 100 ms and maintain an offloading success rate of 93.6%. In the comparison of joint optimization algorithms, the research solution had the lowest system cost (142.50), optimal energy consumption (12.45 J), and average delay (24.60 ms), and quickly converges within 38.50 iterations. In summary, this research method can significantly improve the concurrent access capability, energy efficiency performance and link robustness of the power consumption information collection network, and provides an effective technical path for building an intelligent and highly reliable next-generation power consumption collection system.