Wireless communications advance hand-in-hand with artificial intelligence (AI), indicating an interconnected advancement where each facilitates and benefits from the other. This synergy is particularly evident in the development of the sixth-generation technology standard for mobile networks (6G), envisioned to be AI-native. Generative-AI (GenAI), a novel technology capable of producing various types of outputs, including text, images, and videos, offers significant potential for wireless communications, with its distinctive features. Traditionally, conventional AI techniques have been employed for predictions, classifications, and optimization, while GenAI has more to offer. 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 kind (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, and disaster scenarios management. 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.

翻译：无线通信与人工智能（AI）协同发展，呈现出相互促进、共同演进的紧密联系。这一协同效应在第六代移动通信技术（6G）的发展中尤为显著，6G被构想为原生AI驱动的系统。生成式人工智能（GenAI）作为一种能够生成文本、图像、视频等多种输出形式的新兴技术，凭借其独特特性，为无线通信领域带来了巨大潜力。传统上，常规AI技术已被广泛应用于预测、分类和优化任务，而GenAI则能提供更多可能。本文通过需求标注、需求塑形与需求重调度，引入了战略性需求规划的概念。据此，我们提出将GenAI作为促进无线网络中需求塑形的有力工具。具体而言，GenAI可用于压缩与转换各类内容（例如，从高带宽模式转向低带宽模式，如将视频转换为文本），从而在基站开关、用户关联与负载均衡、干扰管理以及灾难场景管理等多样化应用场景中提升无线网络性能。因此，GenAI能够在无线网络中发挥节省能源与频谱的作用。随着AI领域的最新进展，包括大型语言模型等复杂算法的出现，以及专为AI任务设计的更强大硬件（如AI加速器）的发展，需求规划——尤其是通过GenAI实现的需求塑形——概念正变得日益重要。此外，近期在用户终端等设备上部署GenAI的努力，使得这一概念的实现更加直接可行。