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Predicting the current backlog, or traffic load, in framed-ALOHA networksenables the optimization of resource allocation, e.g., of the frame size.However, this prediction is made difficult by the lack of information about thecardinality of collisions and by possibly complex packet generation statistics.Assuming no prior information about the traffic model, apart from a bound onits temporal memory, this paper develops an online learning-based adaptivetraffic load prediction method that is based on Recurrent Neural Networks (RNN)and specifically on the Long Short-Term Memory (LSTM) architecture. In order toenable online training in the absence of feedback on the exact cardinality ofcollisions, the proposed strategy leverages a novel approximate labelingtechnique that is inspired by Method of Moments (MOM) estimators. Numericalresults show that the proposed online predictor considerably outperformsconventional methods and is able to adapt to changing traffic statistics.

Online Supervised Learning for Traffic Load Prediction in Framed-ALOHA Networks