Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation

2026-04-28

AI Summary

This work addresses the high energy consumption of post-quantum cryptography (PQC) modules on mobile edge devices in quantum-safe scenarios and the inability of conventional resource allocation algorithms to meet real-time requirements due to excessive computational complexity. To tackle these challenges, the paper proposes a multi-stage stochastic mixed-integer nonlinear programming model that incorporates static PQC power constraints. Leveraging Lyapunov optimization theory, the long-term problem is decoupled, and—for the first time—a lightweight agent framework is introduced to jointly optimize PQC execution and non-orthogonal multiple access (NOMA) resource allocation. This approach reduces algorithmic complexity to linear order O(N), significantly enhancing system throughput while ensuring queue stability and adhering to energy constraints. Compared to traditional successive convex approximation (SCA) algorithms, the proposed method achieves approximately 46× speedup at N=35, enabling real-time decision-making.

Abstract

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.

Problem

• Post-Quantum Cryptography
• Non-Orthogonal Multiple Access
• Resource Allocation
• Edge Computing
• Energy Consumption

Innovation

• Lightweight Quantum Agent
• Post-Quantum Cryptography (PQC)
• Non-Orthogonal Multiple Access (NOMA)
• Lyapunov Optimization
• Edge Intelligence