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 wir
The increasing complexity of next-generation wireless networks, like satellite-to-Open RAN, is driving a critical need for more intelligent and agile resource management solutions.
Integrating deep learning with network optimization through differentiable programming enables more efficient and resilient wireless communication, crucial for future digital infrastructure.
Traditional heuristic-based network management will be increasingly augmented or replaced by AI-driven, real-time optimization approaches that can adapt to dynamic conditions.
- · Telecommunications companies
- · AI/ML infrastructure providers
- · Network equipment manufacturers
- · Legacy network optimization software vendors
- · Networks reliant on static resource allocation
Improved spectral efficiency and quality of service in complex wireless environments.
Accelerated deployment of advanced network technologies, including 6G and ubiquitous IoT, due to enhanced management capabilities.
New competitive landscapes in telecommunications driven by providers with superior AI-optimized network performance.
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Read at arXiv cs.AI