用可微编程让无线网络实时自适应干扰，提升资源管理效率。
DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming
- 将干扰管理算法转为可微神经网络，实现端到端学习。
- 在真实场景下显著提升系统效用，支持动态环境实时响应。
- 适合研究智能无线资源调度的工程师与学者。
下一代无线网络（如卫星至开放式无线电接入网系统）需要具备敏捷性和智能化的资源管理能力，以应对动态多用户干扰及随机服务质量约束。本文提出DIFFRACT，一种基于可微编程的神经化效用最大化框架，将深度学习与优化融合于无线网络中。核心在于利用标准干扰函数的数学结构——这是无线功率控制的基础。通过建立该类函数的对偶理论，我们借助算法展开技术将迭代干扰管理算法映射为可微神经网络架构，从而在网路边缘实现分布式、端到端的梯度学习，支持陆地与非地面环境下对干扰的实时自适应。DIFFRACT通过建模复杂信道动态并利用可微模型的表达能力，实现了可扩展且鲁棒的效用最大化。实验结果验证了该框架的理论严谨性与实际有效性，适用于下一代无线系统。
原文摘要 · Abstract (English)
Next-generation wireless networks, including satellite-to-Open RAN systems, demand agile and intelligent resource management capable of handling dynamic multi-user interference under stochastic quality of service constraints. This paper introduces DIFFRACT, a neuralized utility maximization framework that leverages differentiable programming to integrate deep learning with optimization in wireless networks. Central to our approach is the exploitation of the mathematical structure of standard interference functions, which are foundational in wireless power control. By developing a duality theory for these functions, we map iterative interference management algorithms into differentiable neural network architectures via algorithm unrolling. This enables distributed, end-to-end gradient-based learning at the network edge, supporting real-time adaptation to interference in both terrestrial and non-terrestrial environments. DIFFRACT allows for scalable and robust utility maximization by modeling complex channel dynamics and leveraging the expressiveness of differentiable models. Experimental results confirm the framework's theoretical soundness and practical effectiveness for next-generation wireless systems.
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