标题： 精心策划的协作人工智能边缘与网络数据分析用于B5G/6G无线接入网络 显示英文标题

标题： Curated Collaborative AI Edge with Network Data Analytics for B5G/6G Radio Access Networks

摘要： 尽管有所进步，无线接入网络（RAN）仍然占5G网络总功耗的50%以上。现有的RAN拆分选项并未充分挖掘数据潜力，这为降低运营支出提供了机会。本文通过双重方法解决这一机会。首先，使用提出的精心协作学习（CCL）框架实现了高度准确的网络流量和用户预测，该框架选择性地与相关数据进行协作以进行流量预测。CCL最优地确定了与谁、何时以及如何进行协作，分别比最先进的方法（包括全局、联邦、个性化联邦和循环机构增量学习）显著提高43.9%、39.1%、40.8%和31.35%。其次，提出了分布式单元池化方案（DUPS），利用CCL的深度强化学习和预测推理来高效减少活动DU服务器的数量。DUPS动态地将未充分利用的DU服务器的流量重新定向，以优化资源使用，相比传统策略，能将能源效率提高多达89%，为运营商带来显著的经济效益。通过将CCL驱动的预测与DUPS相结合，本文展示了在5G RAN中最小化能耗和运营成本的变革性方法，显著提高了效率和成本效益。 显示英文摘要

摘要： Despite advancements, Radio Access Networks (RAN) still account for over 50\% of the total power consumption in 5G networks. Existing RAN split options do not fully harness data potential, presenting an opportunity to reduce operational expenditures. This paper addresses this opportunity through a twofold approach. First, highly accurate network traffic and user predictions are achieved using the proposed Curated Collaborative Learning (CCL) framework, which selectively collaborates with relevant correlated data for traffic forecasting. CCL optimally determines whom, when, and what to collaborate with, significantly outperforming state-of-the-art approaches, including global, federated, personalized federated, and cyclic institutional incremental learnings by 43.9%, 39.1%, 40.8%, and 31.35%, respectively. Second, the Distributed Unit Pooling Scheme (DUPS) is proposed, leveraging deep reinforcement learning and prediction inferences from CCL to reduce the number of active DU servers efficiently. DUPS dynamically redirects traffic from underutilized DU servers to optimize resource use, improving energy efficiency by up to 89% over conventional strategies, translating into substantial monetary benefits for operators. By integrating CCL-driven predictions with DUPS, this paper demonstrates a transformative approach for minimizing energy consumption and operational costs in 5G RANs, significantly enhancing efficiency and cost-effectiveness.