Connection Management xAPP for O-RAN RIC: A Graph Neural Network and Reinforcement Learning Approach

Connection Management xAPP for O-RAN RIC: A Graph Neural Network and Reinforcement Learning Approach 论文解读

Abstract

Connection management is an important problem for any wireless network to ensure smooth and well-balanced operation throughout. Traditional methods for connection management (specifically user-cell association) consider sub-optimal and greedy solutions such as connection of each user to a cell with maximum receive power. However, network performance can be improved by leveraging machine learning (ML) and artificial intelligence (AI) based solutions. The next generation software defined 5G networks defined by the Open Radio Access Network (O-RAN) alliance facilitates the inclusion of ML/AI based solutions for various network problems. In this paper, we consider intelligent connection management based on the O-RAN network architecture to optimize user association and load balancing in the network. We formulate connection management as a combinatorial graph optimization problem. We propose a deep reinforcement learning (DRL) solution that uses the underlying graph to learn the weights of the graph neural networks (GNN) for optimal user-cell association. We consider three candidate objective functions: sum user throughput, cell coverage, and load balancing. Our results show up to 10% gain in throughput, 45-140% gain cell coverage, 20-45% gain in load balancing depending on network deployment configurations compared to baseline greedy techniques. Index Terms—Open Radio Access Networks, RAN Intelligent Controller, Graph Neural Networks, Deep Reinforcement learning, Connection Management, xAPP.

连接管理对于任何无线网络来说，都是确保始终流畅和均衡运行的一个重要问题。用于连接管理（特别是用户-小区关联）的传统方法考虑次优和贪婪的解决方案，例如将每个用户连接到具有最大接收功率的小区。但是，可以通过利用基于机器学习 (ML) 和人工智能 (AI) 的解决方案来提高网络性能。由开放无线电接入网络 (O-RAN) 联盟定义的下一代软件定义的 5G 网络有助于包含针对各种网络问题的基于 ML/AI 的解决方案。在本文中，我们考虑基于 O-RAN 网络架构的智能连接管理来优化网络中的用户关联和负载平衡。我们将连接管理制定为组合图优化问题。我们提出了一种深度强化学习 (DRL) 解决方案，该解决方案使用底层图来学习图神经网络 (GNN) 的权重，以获得最佳的用户-细胞关联。我们考虑三个候选目标函数：总用户吞吐量、小区覆盖和负载平衡。我们的结果表明，与基线贪婪技术相比，根据网络部署配置，吞吐量提高了 10%，小区覆盖率提高了 45-140%，负载平衡提高了 20-45%。索引词——开放无线电接入网络、RAN 智能控制器、图神经网络、深度强化学习、连接管理、xAPP

REF:

Connection Management xAPP for O-RAN RIC: A Graph Neural Network and Reinforcement Learning Approach  

标签：Management,network,Neural,RAN,user,cell,Connection,xAPP,Network
来源： https://www.cnblogs.com/shaohef/p/15735894.html