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In the context of wireless communications, we propose a deep learningapproach to learn the mapping from the instantaneous state of a frequencyselective fading channel to the corresponding frame error probability (FEP) foran arbitrary set of transmission parameters. We propose an abstract model of abit interleaved coded modulation (BICM) orthogonal frequency divisionmultiplexing (OFDM) link chain and show that the maximum likelihood (ML)estimator of the model parameters estimates the true FEP distribution. Further,we exploit deep neural networks as a general purpose tool to implement ourmodel and propose a training scheme for which, even while training with thebinary frame error events (i.e., ACKs / NACKs), the network outputs converge tothe FEP conditioned on the input channel state. We provide simulation resultsthat demonstrate gains in the FEP prediction accuracy with our approach ascompared to the traditional effective exponential SIR metric (EESM) approachfor a range of channel code rates, and show that these gains can be exploitedto increase the link throughput.

Deep Learning for Frame Error Probability Prediction in BICM-OFDM Systems