DeepNP: Deep Learning-Based Noise Prediction for Ultra-Reliable Low-Latency Communications

Closing the gap between high data rates and low delay in real-time streaming applications is a major challenge in advanced communication systems. While adaptive network coding schemes have the potential of balancing the rate and the delay in real-time, they often rely on a prediction of the channel behavior. In practice, such a prediction is based on delayed feedbacks, making it difficult to acquire causality, particularly when the channel model is unknown. In this work, we propose a deep learning-based noise prediction (DeepNP) algorithm, which augments the recently proposed adaptive and causal random linear network coding scheme with a neural network that learns to carry out noise prediction from data. This neural augmentation is utilized to maximize the throughput while minimizing in-order delivery delay of the coding scheme, and operate in a channel-model-agnostic manner. We numerically show that performance can dramatically increase by the learned prediction of the channel noise rate, demonstrating that DeepNP gains up to a factor of four in mean and maximum delay and a factor of two in throughput compared with statistic-based network coding approaches.