The cloud-native computing is a promising solution for optimizing mobile networks, as it can improve the networks’ flexibility and scalability. However, it also brings new challenges to resource allocation, such as real-time performance and a smaller portion of the allocation. In this article, we develop a cloud-native network architecture which enables dynamic and flexible resource allocations. To address the challenges of real-time and fragmented allocation, we propose a resource allocation algorithm based on deep reinforcement learning towards a 6G wireless network. The proposed algorithm monitors the state of the whole network and trains the allocation policy, which can optimize the utility while meeting service level agreements of the network slices. We further validate the algorithm’s performance in the simulation environment and the experimental cloud-native network testbed. Furthermore, we highlight a suite of open research challenges in resource allocation in the cloud-native network towards 6G.

中文翻译：

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无线通信网络的用户偏好感知资源管理


云原生计算是优化移动网络的一种有前途的解决方案，因为它可以提高网络的灵活性和可扩展性。然而，这也给资源分配带来了新的挑战，例如实时性和分配比例变小等。在本文中，我们开发了一种云原生网络架构，可实现动态灵活的资源分配。为了解决实时和碎片分配的挑战，我们提出了一种基于深度强化学习的6G无线网络资源分配算法。该算法监控整个网络的状态并训练分配策略，可以在满足网络切片的服务水平协议的同时优化效用。我们进一步在模拟环境和实验性云原生网络测试台上验证了算法的性能。此外，我们重点介绍了面向 6G 的云原生网络资源分配方面的一系列开放研究挑战。