Properties of base stations, CUE and D2D pairs.

Large language models have revolutionized the field of natural language processing and are now becoming a one-stop solution to various tasks. In the field of Networking, LLMs can also play a major role when it comes to resource optimization and sharing. While Sumrate maximization has been a crucial factor for resource optimization in the networking domain, the optimal or sub-optimal algorithms it requires can be cumbersome to comprehend and implement. An effective solution is leveraging the generative power of LLMs for such tasks where there is no necessity for prior algorithmic and programming knowledge. A zero-shot analysis of these models is necessary to define the feasibility of using them in such tasks. Using different combinations of total cellular users and total D2D pairs, our empirical results suggest that the maximum average efficiency of these models for sumrate maximization in comparison to state-of-the-art approaches is around 58%, which is obtained using GPT. The experiment also concludes that some variants of the large language models currently in use are not suitable for numerical and structural data without fine-tuning their parameters.

大语言模型（Large Language Model，LLM）已然革新了自然语言处理领域，如今正逐步成为各类任务的一站式解决方案。在计算机网络领域中，大语言模型同样可在资源优化与共享方面发挥关键作用。尽管和速率最大化（Sumrate Maximization）一直是网络领域资源优化的核心考量因素，但其所需的最优或次优算法往往理解与实现难度颇高。针对此类无需具备先验算法与编程知识的任务，借助大语言模型的生成能力是一种高效解决方案。要明确此类任务中使用大语言模型的可行性，需对其开展零样本（Zero-shot）分析。本研究通过调整蜂窝用户总数与设备到设备（Device-to-Device，D2D）对总数的不同组合开展实验，实证结果表明，相较于当前最优方法，大语言模型在和速率最大化任务中的最高平均效率约为58%，该结果通过GPT模型获得。本次实验同时证实，当前使用的部分大语言模型变体，在未对参数进行微调的情况下，并不适用于数值与结构化数据处理。