This work exploits the advantages of two prominent techniques in future communication networks, namely caching and non-orthogonal multiple access (NOMA). Particularly, a system with Rayleigh fading channels and cache-enabled users is analyzed. It is shown that the caching-NOMA combination provides a new opportunity of cache hit which enhances the cache utility as well as the effectiveness of NOMA. Importantly, this comes without requiring users’ collaboration, and thus, avoids many complicated issues such as users’ privacy and security, selfishness, etc. In order to optimize users’ quality of service and, concurrently, ensure the fairness among users, the probability that all users can decode the desired signals is maximized. In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. To address the power allocation problem, two novel methods are proposed. The first one is a divide-and-conquer-based method for which closed-form expressions for the optimal resource allocation policy are derived making this method simple and flexible to the system context. The second one is based on deep reinforcement learning method that allows all users to share the full bandwidth. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods and to compare their performance.

中文翻译：

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缓存辅助 NOMA 系统中的功率分配：优化和深度强化学习方法

这项工作利用了未来通信网络中两种突出技术的优势，即缓存和非正交多址 (NOMA)。特别地，分析了具有瑞利衰落信道和启用缓存的用户的系统。结果表明，缓存-NOMA 组合提供了一个新的缓存命中机会，从而增强了缓存效用以及 NOMA 的有效性。重要的是，这不需要用户的协作，从而避免了许多复杂的问题，如用户的隐私和安全、自私等。 为了优化用户的服务质量，同时保证用户之间的公平，概率最大化所有用户都可以解码所需信号。在 NOMA 中，向用户发送多条消息的组合，并且通过为消息信号找到合适的功率分配来接近定义的目标。为了解决功率分配问题，提出了两种新方法。第一个是基于分而治之的方法，它推导出最优资源分配策略的封闭式表达式，使该方法简单且适用于系统上下文。第二种是基于深度强化学习的方法，允许所有用户共享全部带宽。最后，提供了仿真结果来证明所提出方法的有效性并比较它们的性能。第一个是基于分而治之的方法，它推导出最优资源分配策略的封闭式表达式，使该方法简单且适用于系统上下文。第二种是基于深度强化学习的方法，允许所有用户共享全部带宽。最后，提供了仿真结果来证明所提出方法的有效性并比较它们的性能。第一个是基于分而治之的方法，它推导出最优资源分配策略的封闭式表达式，使该方法简单且适用于系统上下文。第二种是基于深度强化学习的方法，允许所有用户共享全部带宽。最后，提供了仿真结果来证明所提出方法的有效性并比较它们的性能。