English(EN) DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming

新框架使用可微分编程进行无线网络优化

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-05 10:09

研究人员开发了DIFFRACT，一个使用可微分编程优化无线网络的新框架。该方法将深度学习与优化技术相结合，以管理下一代系统（如卫星和Open RAN）中的动态干扰和服务质量。通过将迭代算法映射到可微分神经网络，DIFFRACT能够在网络边缘实现分布式、基于梯度的学习，以进行实时适应。 AI

影响为未来无线通信系统实现更具适应性和效率的资源管理。

排序理由该集群包含一篇详细介绍新框架和方法的学术论文。

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arXiv cs.AI TIER_1 English(EN) · Chee Wei Tan, Siya Chen · 2026-06-08 04:00

DIFFRACT：通过可微分编程实现无线网络的神经化效用最大化

arXiv:2606.07114v1 Announce Type: cross Abstract: 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…
arXiv cs.AI TIER_1 English(EN) · Siya Chen · 2026-06-05 10:09

DIFFRACT：通过可微分编程实现无线网络的神经化效用最大化

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 m…