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In this paper, we study how to solve resource allocation problems inultra-reliable and low-latency communications by unsupervised deep learning,which often yield functional optimization problems with quality-of-service(QoS) constraints. We take a joint power and bandwidth allocation problem as anexample, which minimizes the total bandwidth required to guarantee the QoS ofeach user in terms of the delay bound and overall packet loss probability. Theglobal optimal solution is found in a symmetric scenario. A neural network wasintroduced to find an approximated optimal solution in general scenarios, wherethe QoS is ensured by using the property that the optimal solution shouldsatisfy as the "supervision signal". Simulation results show that thelearning-based solution performs the same as the optimal solution in thesymmetric scenario, and can save around 40% bandwidth with respect to thestate-of-the-art policy.

Unsupervised Deep Learning for Ultra-reliable and Low-latency Communications