Strategic Demand-Planning in Wireless Networks: Can Generative-AI Save Spectrum and Energy?
Berk \c{C}ilo\u{g}lu, G\"orkem Berkay Ko\c{c}, Afsoon Alidadi, Shamsabadi, Metin Ozturk, Halim Yanikomeroglu

TL;DR
This paper explores how Generative-AI can optimize demand management in wireless networks, potentially saving spectrum and energy by transforming content and improving network efficiency in various scenarios.
Contribution
It introduces the concept of strategic demand-planning using GenAI for demand-shaping, demand-labeling, and demand-rescheduling in wireless networks, highlighting its potential benefits.
Findings
GenAI can compress and convert content to optimize network performance.
GenAI enhances demand-shaping for cell-switching, user association, and interference management.
Potential for significant spectrum and energy savings in wireless 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…
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Taxonomy
TopicsOpportunistic and Delay-Tolerant Networks
