Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance. However, due to the increasing number of users and the constraint of caching storage capacity, there exists a tradeoff between NOMA transmission performance and fronthaul saving. In this paper, a hierarchical game framework is presented to solve the joint optimization problem of user access mode selection and content popularity prediction in NOMA based F-RANs. More specifically, the access mode selection problem is formulated as an evolutionary game. The proposals’ evolutionary payoff expressions are derived by stochastic geometry tool, and the cost functions are related to the fog access point (F-AP) content placement profile as well as the fronthaul constraint. Moreover, the problem of what contents the F-AP should cache is modeled as a content popularity prediction problem, and based on both local and global user request states, a machine learning algorithm is presented to solve it. Simulation results validate the accuracy of analytical results and demonstrate our proposed algorithms can further improve the performance of NOMA based F-RANs.

中文翻译：

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基于非正交多址接入的 F-RAN 中的联合用户接入模式选择和内容流行度预测

非正交多址（NOMA）被认为是下一代无线通信系统的一项有前途的技术。将 NOMA 引入雾无线接入网络 (F-RAN) 能够为多个用户提供同时传输并显着提高 F-RAN 性能。然而，由于用户数量的增加和缓存存储容量的限制，NOMA传输性能和前传节省之间存在权衡。在本文中，提出了一种分层博弈框架来解决基于 NOMA 的 F-RAN 中用户访问模式选择和内容流行度预测的联合优化问题。更具体地说，访问模式选择问题被表述为一个进化博弈。提案的进化收益表达式是通过随机几何工具导出的，成本函数与雾接入点 (F-AP) 内容放置配置文件以及前传约束相关。此外，F-AP 应该缓存什么内容的问题被建模为内容流行度预测问题，并基于本地和全局用户请求状态，提出了一种机器学习算法来解决它。仿真结果验证了分析结果的准确性，并证明了我们提出的算法可以进一步提高基于 NOMA 的 F-RAN 的性能。