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
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O-RAN RIC 的连接管理 xAPP：图神经网络和强化学习方法
DOI：
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发表时间：
2021
期刊：
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通讯作者：
S. Talwar
中科院分区：
文献类型：
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作者：
Oner Orhan;Vasuki Narasimha Swamy;T. Tetzlaff;M. Nassar;Hosein Nikopour;S. Talwar
连接管理对于任何无线网络来说都是一个重要的问题，以确保整个网络的平稳和平衡运行。传统的连接管理方法(特别是用户-小区关联)考虑次优和贪婪的解决方案，例如每个用户连接到具有最大接收功率的小区。然而，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网络促进了针对各种网络问题的基于ML/AI的解决方案的纳入。本文考虑基于O-RAN网络体系结构的智能连接管理，以优化网络中的用户关联和负载均衡。我们将连接管理问题描述为一个组合图优化问题。我们提出了一种深度强化学习(DRL)解决方案，它使用底层图来学习图神经网络(GNN)的权重，以实现最优的用户-小区关联。我们考虑三个候选目标函数：总和用户吞吐量、小区覆盖和负载均衡。我们的结果显示，与基准贪婪技术相比，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%。
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.