  In this work, we introduce EQ-Net: the first holistic framework that solves
both the tasks of log-likelihood ratio (LLR) estimation and quantization using
a data-driven method. We motivate our approach with theoretical insights on two
practical estimation algorithms at the ends of the complexity spectrum and
reveal a connection between the complexity of an algorithm and the information
bottleneck method: simpler algorithms admit smaller bottlenecks when
representing their solution. This motivates us to propose a two-stage algorithm
that uses LLR compression as a pretext task for estimation and is focused on
low-latency, high-performance implementations via deep neural networks. We
carry out extensive experimental evaluation and demonstrate that our single
architecture achieves state-of-the-art results on both tasks when compared to
previous methods, with gains in quantization efficiency as high as $20\%$ and
reduced estimation latency by up to $60\%$ when measured on general purpose and
graphical processing units (GPU). In particular, our approach reduces the GPU
inference latency by more than two times in several multiple-input
multiple-output (MIMO) configurations. Finally, we demonstrate that our scheme
is robust to distributional shifts and retains a significant part of its
performance when evaluated on 5G channel models, as well as channel estimation
errors.
