  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.
