轻量级量子代理优化边缘设备资源分配，兼顾安全与实时性。
Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation
- 构建多阶段随机优化模型，融合后量子加密能耗约束。
- 提出线性复杂度算法，实现46倍提速，支持动态环境实时决策。
- 适合边缘计算中需高安全、低延迟的物联网应用。
在量子安全场景下，现有基于非正交多址（NOMA）通信模型的移动边缘设备与智能计算边缘（ICE）系统研究忽略了后量子密码（PQC）模块的能耗开销，且传统资源分配算法复杂度过高，难以满足实时决策需求。本文提出一种面向在线联合优化的轻量级智能体框架。该方案构建包含静态功耗约束的多阶段随机混合整数非线性规划（MINLP）模型，基于李雅普诺夫优化理论将长期优化问题解耦，并提出线性复杂度算法以应对NOMA功率分配的非凸挑战。仿真结果表明，所提方案显著提升计算吞吐量，同时保障系统队列稳定性和能耗约束。相较于传统逐次凸逼近（SCA）算法，复杂度降至$/mathcal{O}(N)$，当设备数$N=35$时，加速比达约46倍，满足动态无线环境下实时决策要求。
原文摘要 · Abstract (English)
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.
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