Enabling ultra-reliable low-latency communication in wireless networks via interference prediction

New radio technologies are expected to support time critical use cases such as
closed loop control in factory automation, targeting sub-ms latencies and high
reliability. Industrial wireless networks supporting such ultra-reliable low-latency communication may consist of small cells with limited coverage operating over a spectrum shared with other radio systems. However, interference represents a major obstacle when operating over such shared spectrum, and may jeopardize the possibility of achieving ultra-reliable low latency communication. In this project, we aim at designing solutions for mitigating the impact of the interference in dense small cells, translating to the wireless support of time critical use cases for a large number of devices. In particular, we aim at investigating machine learning techniques for interference prediction. Efficient response mechanisms are then to be designed in order to maximize the number of successful ultra-reliable low latency transmissions.