5G wireless networks require highly spectral-efficient multiple access techniques, which play an important role in determining the performance of mobile communication systems. Multiple access techniques can be classified into orthogonal and nonorthogonal based on the way the resources are allocated to the users. This paper investigates the joint user association and resource allocation problem in an uplink multicast NOMA system to maximize the power efficiency with guaranteeing the quality-of-experience of all subscribers. We also introduce an adaptive load balancing approach that aspires to obtain “almost optimal” fairness among servers from the quality of service (QoS) perspective in which learning automata (LA) has been used to find the optimal solution for this dynamic problem. This approach contains a sophisticated learning automata which consists of time-separation and the “artificial” ergodic paradigms. Different from conventional model-based resource allocation methods, this paper suggested a hierarchical reinforcement learning based frameworks to solve this non-convex and dynamic power optimization problem, referred to as hierarchical deep learning-based resource allocation framework. The entire resource allocation policies of this framework are adjusted by updating the weights of their neural networks according to feedback of the system. The presented learning automata find the \(\epsilon\)-optimal solution for the problem by resorting to a two-time scale-based SLA paradigm. Numerical results show that the suggested hierarchical resource allocation framework in combination with the load balancing approach, can significantly improve the energy efficiency of the whole NOMA system compared with other approaches.

5G无线网络需要高效频谱高效的多址技术，这在确定移动通信系统的性能中起着重要作用。基于将资源分配给用户的方式，可以将多路访问技术分为正交和非正交。本文研究了上行组播NOMA系统中的联合用户关联和资源分配问题，以在保证所有订户体验质量的情况下最大化功率效率。我们还引入了一种自适应负载平衡方法，该方法旨在从服务质量（QoS）角度获得服务器之间的“几乎最佳”公平性，其中使用学习自动机（LA）来找到针对此动态问题的最佳解决方案。这种方法包含一个复杂的学习自动机，该自动机由时间分隔和“人工”遍历模式组成。与传统的基于模型的资源分配方法不同，本文提出了一种基于分层强化学习的框架来解决该非凸和动态功率优化问题，称为基于分层深度学习的资源分配框架。通过根据系统的反馈更新其神经网络的权重，可以调整此框架的整个资源分配策略。提出的学习自动机找到 本文提出了一种基于分层强化学习的框架来解决这一非凸和动态功率优化问题，称为基于分层深度学习的资源分配框架。通过根据系统的反馈更新其神经网络的权重，可以调整此框架的整个资源分配策略。提出的学习自动机找到 本文提出了一种基于分层强化学习的框架来解决这一非凸和动态功率优化问题，称为基于分层深度学习的资源分配框架。通过根据系统的反馈更新其神经网络的权重，可以调整此框架的整个资源分配策略。提出的学习自动机找到\（\ epsilon \）-通过采用基于两次缩放的SLA范式来解决问题的最佳方案。数值结果表明，与其他方法相比，该建议的分层资源分配框架与负载平衡方法相结合可以显着提高整个NOMA系统的能源效率。