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Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This challenge 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 require each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this article, 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. 

In recent years, aiming to enhance and extend user experiences beyond the real world, Extended Reality (XR) has emerged to become a new paradigm that enables a plethora of applications [1], e.g., online gaming, online conferencing, social media, etc. XR refers to the human-machine interactions that combine real and virtual environments with the support of computing/communications technologies and wearable devices. The XR content is generated by providers or other users, including audio, video and other metadata. In general, the generated XR content is transmitted to XR devices and rendered into XR scenes (i.e., to generate an image from a 2D or 3D model by means of a computer program), where users can experience a hybrid experience of the real and virtual worlds.