In the context of quantum secure scenarios, existing research on mobile edge devices and intelligent computing and edge (ICE) systems based on the Non-Orthogonal Multiple Access (NOMA) communication model have overlooked the energy consumption overhead of Post-Quantum Cryptography (PQC) modules, and the high complexity of traditional resource allocation algorithms fails to meet the demands of real-time decision-making. To address these challenges, this paper proposes a lightweight agentic AI framework designed for online joint optimization within ICE-enabled mobile devices. The scheme constructs a multi-stage stochastic Mixed Integer Nonlinear Programming (MINLP) model that incorporates static power-consumption constraints for PQC modules. Based on Lyapunov optimization theory, the long-term optimization problem is decoupled, and a linear complexity algorithm is proposed to solve the nonconvex challenges of NOMA power allocation . Simulation results verify that the proposed scheme significantly improves computational throughput while ensuring system queue stability and energy consumption constraints. Compared with traditional Successive Convex Approximation (SCA) algorithms, the complexity is reduced to $\mathcal{O}(N)$, achieving a speedup of approximately 46 times when the number of devices $N=35$, thereby meeting the real-time decision-making requirements in dynamic wireless environments.

翻译：在量子安全场景下，现有基于非正交多址接入通信模型的移动边缘设备与智能计算边缘系统研究忽视了后量子密码模块的能耗开销，且传统资源分配算法的高复杂度难以满足实时决策需求。针对上述挑战，本文提出一种面向ICE移动设备内在线联合优化的轻量级智能体AI框架。该方案构建了包含PQC模块静态功耗约束的多阶段随机混合整数非线性规划模型，基于李雅普诺夫优化理论解耦长期优化问题，并针对NOMA功率分配的非凸挑战提出线性复杂度算法。仿真结果表明，所提方案在保证系统队列稳定性与能耗约束的同时，显著提升计算吞吐量。与传统逐次凸逼近算法相比，复杂度降至$\mathcal{O}(N)$，当设备数$N=35$时实现约46倍加速比，满足动态无线环境中的实时决策需求。