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Ultra reliable low latency communications (URLLC) is a new service classintroduced in 5G which is characterized by strict reliability $(1-10^{-5})$ andlow latency requirements (1 ms). To meet these requisites, several strategieslike overprovisioning of resources and channel-predictive algorithms have beendeveloped. This paper describes the application of a Nonlinear AutoregressiveNeural Network (NARNN) as a novel approach to forecast interference levels in awireless system for the purpose of efficient resource allocation. Accurateinterference forecasts also grant the possibility of meeting specific outageprobability requirements in URLLC scenarios. Performance of this proposal isevaluated upon the basis of NARNN predictions accuracy and system resourceusage. Our proposed approach achieved a promising mean absolute percentageerror of 7.8 % on interference predictions and also reduced the resource usagein up to 15 % when compared to a recently proposed interference predictionalgorithm.

A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarios