aiqbench analysis
DIFFRACT demonstrates a practical convergence of deep learning and optimization by applying algorithm unrolling to solve dynamic interference management in next-generation wireless networks. By mapping iterative interference functions into differentiable architectures, the framework enables distributed, real-time adaptation at the network edge without sacrificing theoretical soundness. This approach offers a scalable solution for resource allocation in complex environments ranging from terrestrial systems to satellite-to-Open RAN infrastructures.
DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming
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 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.
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 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.
Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
arXiv:2606.07114 [cs.NI]
(or arXiv:2606.07114v1 [cs.NI] for this version)
https://doi.org/10.48550/arXiv.2606.07114 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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