DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming

DIFFRACT is a new neuralized framework that combines deep learning with wireless network optimization through differentiable programming, enabling distributed resource management across satellite and terrestrial networks. The approach maps interference management algorithms into neural network architectures, allowing real-time adaptation to dynamic network conditions with scalable utility maximization.

DIFFRACT represents a significant advancement in applying machine learning techniques to solve classical wireless network optimization problems. The framework's innovation lies in its theoretical foundation—leveraging the mathematical structure of interference functions and developing a duality theory that enables the translation of iterative algorithms into differentiable neural architectures. This bridge between classical optimization and deep learning addresses a critical need in next-generation wireless systems, particularly for non-terrestrial networks like satellite-to-Open RAN systems that must operate under stochastic quality-of-service constraints.

The research builds on growing momentum in using neural networks for network resource allocation, where traditional optimization methods struggle with real-time adaptation to changing interference conditions. By implementing algorithm unrolling—a technique that unfolds iterative algorithms into neural network layers—DIFFRACT enables end-to-end gradient-based learning at the network edge. This distributed approach is particularly valuable for systems requiring rapid decision-making without centralized computation.

For the telecommunications industry and network operators, this framework offers tangible benefits. Current wireless systems often rely on heuristic or reactive power control mechanisms that cannot fully exploit network capacity under complex interference scenarios. DIFFRACT's ability to model dynamic channel conditions while maintaining scalability addresses deployment challenges in dense urban environments and emerging satellite networks.

The practical implications extend beyond academia. Telecommunications equipment manufacturers and network operators developing 5G/6G infrastructure may incorporate differentiable programming techniques to improve spectral efficiency. Future research should focus on real-world deployment validation, particularly testing robustness under extreme interference conditions and compatibility with existing network standards.

• →DIFFRACT combines interference function theory with neural networks via algorithm unrolling to optimize wireless resource allocation
• →The framework enables distributed, real-time learning at network edges for both terrestrial and satellite systems
• →Differentiable programming bridges classical optimization and deep learning for next-generation wireless networks
• →The approach addresses dynamic multi-user interference under stochastic constraints more effectively than traditional methods
• →Practical applications include improved spectral efficiency for 5G/6G and satellite-to-ground communication systems