论文标题
NOMA网络中的基于深度学习的无线电资源管理：用户协会，亚渠道和电力分配
Deep Learning based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation
论文作者
论文摘要
随着未来无线通信的快速发展，Noma技术和毫米波（MMWave）技术的结合已成为研究热点。 NOMA在MMWave异质网络中的应用可以满足用户在未来通信中不同应用程序和方案中的各种需求。在本文中，我们提出了一个机器学习框架，以在如此复杂的情况下处理用户协会，亚渠道和电源分配问题。我们专注于在服务质量（QoS），干扰限制和功率限制的限制下最大化系统的能源效率（EE）。具体而言，用户关联是通过Lagrange双重分解方法解决的，而半监督学习和深神经网络（DNN）分别用于子渠道和电源分配。特别是，引入了未标记的样品，以提高亚通道分配的近似和概括能力。模拟表明所提出的方案可以以较低的复杂性获得更高的EE。
With the rapid development of future wireless communication, the combination of NOMA technology and millimeter-wave(mmWave) technology has become a research hotspot. The application of NOMA in mmWave heterogeneous networks can meet the diverse needs of users in different applications and scenarios in future communications. In this paper, we propose a machine learning framework to deal with the user association, subchannel and power allocation problems in such a complex scenario. We focus on maximizing the energy efficiency (EE) of the system under the constraints of quality of service (QoS), interference limitation, and power limitation. Specifically, user association is solved through the Lagrange dual decomposition method, while semi-supervised learning and deep neural network (DNN) are used for the subchannel and power allocation, respectively. In particular, unlabeled samples are introduced to improve approximation and generalization ability for subchannel allocation. The simulation indicates that the proposed scheme can achieve higher EE with lower complexity.
