6G O-RAN中使用基于DQN的xApp实现节能
Energy Saving in 6G O-RAN Using DQN-based xApp
AI总结：
本文提出基于深度Q网络（DQN）的强化学习方法，在6G O-RAN架构中开发两种xApp实现节能，显著优于启发式和基线方法，在50个UE时能关闭50%的射频卡。
摘要：
开放式无线接入网（RAN）是一种支持开放性、互操作性和智能化的变革性范式，其中O-RAN架构是学术界和工业界最认可的框架。在开放式RAN的背景下，节能（ES）的重要性日益凸显，特别是在第六代移动网络（6G）网络密集化的当前趋势下。RAN中的传统节能方法难以应对网络日益增加的动态性。本文提出使用强化学习（RL）这一机器学习（ML）的子集来改善ES。我们提出了一种用于6G O-RAN中ES的新型深度RL方法，并以xApp（ES-xApp）的形式实现。我们开发了两个基于深度Q网络（DQN）的ES-xApp。ES-xApp-1使用RSS和用户设备（UE）地理位置，而ES-xApp-2仅使用RSS。所提出的模型显著优于启发式和基线xApp，尤其是在UE数量超过20时。当有50个UE时，50%的射频卡（RC）被关闭，而启发式算法仅为17%。我们观察到，信息量更大的输入可能会带来更稳定的训练和结果。本文强调了无线网络中节能的必要性，并为未来的研究和工业实践提供了实用的策略和证据。
英文摘要：
Open Radio Access Network (RAN) is a transformative paradigm that supports openness, interoperability, and intelligence, with the O-RAN architecture being the most recognized framework in academia and industry. In the context of Open RAN, the importance of Energy Saving (ES) is heightened, especially with the current direction of network densification in sixth generation of mobile networks (6G). Traditional energy-saving methods in RAN struggle with the increasing dynamics of the network. This paper proposes using Reinforcement Learning (RL), a subset of Machine Learning (ML), to improve ES. We present a novel deep RL method for ES in 6G O-RAN, implemented as xApp (ES-xApp). We developed two Deep Q-Network (DQN)-based ES-xApps. ES-xApp-1 uses RSS and User Equipment (UE) geolocations, while ES-xApp-2 uses only RSS. The proposed models significantly outperformed heuristic and baseline xApps, especially with over 20 UEs. With 50 UEs, 50% of Radio Cards (RCs) were switched off, compared to 17% with the heuristic algorithm. We have observed that more informative inputs may lead to more stable training and results. This paper highlights the necessity of energy conservation in wireless networks and offers practical strategies and evidence for future research and industry practices.
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