WIP：无小区大规模MIMO中基于LLM的节能服务集群构建
WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO
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中文总结 AI 辅助
本文提出利用基于大语言模型的AI智能体解决无小区大规模MIMO中的服务集群构建问题，以提升6G网络能量效率，实验显示相比基线最高可提升32%。
中文摘要 AI 辅助
提高6G无线网络能量效率（EE）的一种方式是更高效地利用现有网络基础设施。这可以通过引入以用户为中心的无小区大规模多输入多输出（UCCF MMMIMO）来实现，该技术允许多个基站（BS）同时服务单个用户。从这个角度来看，关键挑战在于决定哪些基站应服务给定用户，即所谓的服务集群构建（SCF）。在本文中，我们提出使用基于大语言模型（LLM）的人工智能（AI）智能体来解决这一问题，旨在提升能量效率。我们使用一个基于3D射线追踪的复杂蜂窝网络模拟器对所提出的AI智能体进行了评估，比较了几种GPT模型和现有最先进的算法。结果表明，与基线相比，所提出的AI智能体在能量效率上最高可提升32%。
英文摘要
One way to increase the Energy Efficiency (EE) of 6G wireless networks is to utilize existing network infrastructure more efficiently. This can be achieved by introducing User-Centric Cell-Free Massive Multiple-Input-Multiple-Output (UCCF MMMIMO), which allows for simultaneously serving a single user by multiple Base Stations (BSs). From this perspective, the key challenge is to decide which BSs should serve a given user, known as the Serving Cluster Formulation (SCF). In this paper, we propose to deal with this problem by using an Artificial Intelligence (AI) agent based on a Large Language Model (LLM), targeting improvement of EE. We evaluated the proposed AI agent using a complex, 3D Ray Tracer-based, cellular network simulator, comparing a few GPT models and state-of-the-art algorithms. The results show up to 32% gain in EE of the proposed AI agent compared to the baseline.
发表机构
- Poznan University of Technology（波兹南理工大学）
机构由 AI 辅助整理，请以论文原文为准。
