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

TL;DR

This paper introduces a lightweight AI agent for edge systems that jointly optimizes PQC and NOMA resource allocation, significantly reducing computational complexity for real-time decision-making.

Contribution

It proposes a novel multi-stage stochastic MINLP model and a linear complexity algorithm for joint PQC and NOMA resource optimization in edge devices.

Findings

Simulation shows improved throughput and energy efficiency.

Complexity reduced to O(N), 46x faster than traditional methods.

Ensures system stability under dynamic wireless conditions.

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.