Strategic Demand-Planning in Wireless Networks: Can Generative-AI Save Spectrum and Energy?
Berk Çiloğlu, Görkem Berkay Koç, Afsoon Alidadi Shamsabadi, Metin Ozturk, Halim Yanikomeroglu
TL;DR
This paper proposes a GenAI-driven demand-planning framework for 6G and beyond wireless networks, introducing demand-labeling, demand-shaping, and demand-rescheduling as core components to compress, convert, or defer user data in order to save energy and spectrum. It surveys GenAI model families (GANs, VAEs, diffusion models, transformers) and discusses how plug-in GenAI modules at user terminals or base stations can enable wiring intelligent demand-planning workflows. The authors illustrate usage scenarios — including sustainability via cell-switching, improved user association/load balancing, interference management, disaster resilience, and business implications — and analyze challenges such as hardware constraints, regulatory carbon considerations, security, information loss, and data privacy. The work highlights the potential for spectrum leasing and lower operational costs, especially in dense VHetNets and NTN deployments, while recognizing the need for robust validation, transparency, and privacy guarantees to realize practical gains in next-generation networks.
Abstract
Generative-AI (GenAI), a novel technology capable of producing various types of outputs, including text, images, and videos, offers significant potential for wireless communications. This article introduces the concept of strategic demand-planning through demand-labeling, demand-shaping, and demand-rescheduling. Accordingly, GenAI is proposed as a powerful tool to facilitate demand-shaping in wireless networks. More specifically, GenAI is used to compress and convert the content of various types (e.g., from a higher bandwidth mode to a lower one, such as from a video to text), which subsequently enhances performance of wireless networks in various usage scenarios, such as cell-switching, user association and load balancing, interference management, as well as disasters and unusual gatherings. Therefore, GenAI can serve a function in saving energy and spectrum in wireless networks. With recent advancements in AI, including sophisticated algorithms like large language models and the development of more powerful hardware built exclusively for AI tasks, such as AI accelerators, the concept of demand-planning, particularly demand-shaping through GenAI, becomes increasingly relevant. Furthermore, recent efforts to make GenAI accessible on devices, such as user terminals, make the implementation of this concept even more straightforward and feasible.