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arXiv · 2611.04474

Hierarchical Intelligent Evolutionary Optimization for UAV-Assisted NOMA-MEC Systems

Yaxuan Jiao·Jiaxuan Lv·Jyoti Sahni·Wuyungerile Li·Winston K. G. Seah
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Abstract

The integration of unmanned aerial vehicles (UAVs), mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) provides an effective solution for computation-intensive and latency-sensitive services in infrastructure-deficient environments. However, the joint optimization of UAV trajectories, task offloading, communication and computing resources under heterogeneous task priorities results in a highly coupled mixed-integer non-convex problem. To address these challenges, this paper proposes a Hierarchical Intelligent Evolutionary Algorithm (HIEA) for priority-aware multi-UAV cooperative NOMA-MEC networks, which integrates multi-head deep Q-network (MH-DQN)-based intelligent decision generation, communication-aware initialization, adaptive evolutionary exploration and successive convex approximation (SCA)-based precision refinement. Specifically, a novel communication-aware initialization mechanism, termed Channel Quality Indicator (CQI)-guided Load-Aware Trajectory Initialization (CQI-LATI), is designed to incorporate CQI and task workload characteristics into evolutionary population construction, generating high-quality and diversity-preserving initial populations. Theoretical analysis characterizes HIEA's search-space reduction, the quality-diversity trade-off of CQI-guided initialization, and the accuracy-efficiency trade-off of SCA refinement, and extensive simulations demonstrate that HIEA outperforms benchmark schemes, with gains of up to 6.5% in throughput, 13.6% in task completion rate and 15.3% in energy efficiency.

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Yaxuan Jiao, Jiaxuan Lv, Jyoti Sahni, Wuyungerile Li, Winston K. G. Seah. 2026-11-03. Hierarchical Intelligent Evolutionary Optimization for UAV-Assisted NOMA-MEC Systems. https://arxiv.org/abs/2611.04474

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