Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This problem is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of interference links rapidly increase due to the line-of-sight channels in UAV communications. Existing interference management solutions need each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming. In particular, we show that interference can still be effectively mitigated even without knowing its channel information. We then discuss novel approaches to scale the algorithms with linear/sublinear complexity and decentralize them using multi-agent reinforcement learning. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.

翻译：现代蜂窝网络采用多小区结构并运用通用频率复用技术以最大化频谱效率，这导致了严重的跨小区干扰。随着蜂窝网络因无人飞行器（UAV）的采用而向三维化演进，该问题日益加剧——由于无人机通信中视距信道的特性，干扰链路的强度与数量急剧增加。现有干扰管理方案需要每个发射机获知干扰信号的信道信息，因信号开销过大而缺乏实用性。本文提出利用深度强化学习进行干扰管理以解决这一缺陷。具体而言，我们论证了即使在不掌握信道信息的情况下，干扰仍可被有效抑制。随后，我们探讨了通过线性/次线性复杂度实现算法规模化、以及利用多智能体强化学习实现算法去中心化的创新方法。通过驾驭干扰，所提方案为民用无人机的持续发展提供了支撑。