DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming
This work addresses the need for agile, intelligent resource management in next-generation wireless networks (e.g., satellite-to-Open RAN) by providing a scalable, distributed framework that adapts to dynamic interference.
DIFFRACT integrates deep learning with optimization for wireless resource management by unrolling iterative interference algorithms into differentiable neural networks, enabling real-time utility maximization under dynamic interference. Experiments confirm its theoretical soundness and practical effectiveness for next-generation wireless systems.
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.