The development of the next-generation wireless networks are regarded as the essentials to embrace of Internet of Things (IoT) and edge computing in heterogeneous networks (HetNets). Due to the the spectrum scarcity problem and the large number of connectivity demand of IoT users, intelligent interference management for IoT is worthy of thorough investigation and should be well discussed with consideration on edge computing in heterogeneous networks (HetNets). Two crucial challenges in the context are: 1) placing edge cache based on dynamic request of IoT users, and 2) cache-enabled interference management with time-varying wireless channels. In this paper, we proposed smart edge caching-aided partial opportunistic interference alignment(POIA) with deep reinforcement learning for IoT downlink system in HetNets. Towards this end, the proposed scheme can update the base station (BS) cache dynamically, and then select the optimal cache-enabled POIA user group considering the time-varying user’s requests and time-varying wireless channels. To solve this problem efficiently, the reinforcement learning is exploited that can take advantage of a deep Q-learing to replace the system action. Extensive evaluations demonstrate that the proposed method is effectiveness according to sum rate and energy efficiency of IoT downlink transmission for HetNets.

下一代无线网络的发展被视为在异构网络（HetNets）中拥抱物联网（IoT）和边缘计算的基础。由于频谱稀缺问题和IoT用户的大量连接需求，针对IoT的智能干扰管理值得深入研究，应考虑异构网络（HetNets）中的边缘计算，进行深入讨论。上下文中的两个关键挑战是：1）根据IoT用户的动态请求放置边缘缓存，以及2）随时间变化的无线信道启用缓存的干扰管理。本文针对HetNets的IoT下行系统，提出了一种具有深度强化学习功能的智能边缘缓存辅助部分机会干扰对齐（POIA）。为此，所提出的方案可以动态地更新基站（BS）缓存，然后考虑随时间变化的用户请求和随时间变化的无线信道来选择启用了高速缓存的最佳POIA用户组。为了有效地解决此问题，我们采用了强化学习方法，可以利用深层Q学习替代系统动作。广泛的评估表明，该方法根据HetNet的IoT下行链路传输的总速率和能效来确定是否有效。利用了强化学习，可以利用深度Q学习来代替系统动作。广泛的评估表明，该方法根据HetNet的IoT下行链路传输的总速率和能效来确定是否有效。利用了强化学习，可以利用深度Q学习来代替系统动作。广泛的评估表明，该方法根据HetNet的IoT下行链路传输的总速率和能效来确定是否有效。