论文标题
开放无线访问网络（O-RAN）的基于团队学习的资源分配
Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)
论文作者
论文摘要
最近，已经提出了开放无线电访问网络（O-RAN）的概念，该概念旨在在下一代无线电访问网络（RAN）中采用智能和开放性。它提供了标准化的接口以及通过X申请（XAPP）从第三方供应商托管网络应用程序的能力，从而为网络管理提供了更高的灵活性。但是，这可能会导致网络功能实现的冲突，尤其是当这些功能由不同的供应商实施时。在本文中，我们旨在减轻O-Ran的近实时（近RT）无线电智能控制器（RIC）之间的XAPP之间的冲突。特别是，我们提出了一个团队学习算法，以通过增加XAPP之间的合作来提高网络的性能。我们将团队学习方法与独立的深Q学习进行比较，其中网络功能分别优化了资源。我们的模拟表明，在各种用户移动性和流量负载下，团队学习具有更好的网络性能。团队学习具有6 Mbps的交通负荷和20 m/s的用户移动速度，可实现8％的吞吐量，而PDR降低了64.8％。
Recently, the concept of open radio access network (O-RAN) has been proposed, which aims to adopt intelligence and openness in the next generation radio access networks (RAN). It provides standardized interfaces and the ability to host network applications from third-party vendors by x-applications (xAPPs), which enables higher flexibility for network management. However, this may lead to conflicts in network function implementations, especially when these functions are implemented by different vendors. In this paper, we aim to mitigate the conflicts between xAPPs for near-real-time (near-RT) radio intelligent controller (RIC) of O-RAN. In particular, we propose a team learning algorithm to enhance the performance of the network by increasing cooperation between xAPPs. We compare the team learning approach with independent deep Q-learning where network functions individually optimize resources. Our simulations show that team learning has better network performance under various user mobility and traffic loads. With 6 Mbps traffic load and 20 m/s user movement speed, team learning achieves 8% higher throughput and 64.8% lower PDR.
