Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models

• Published in: IEEE communications letters Vol. 22; no. 11; pp. 2278 - 2281
• Main Authors: Raj, Vishnu, Kalyani, Sheetal
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Channel models; Communication systems; Communications systems; Computer simulation; Deep learning; Machine learning; Neural networks; optimization; Perturbation methods; Receivers; Training; Transmitters
• DOI: 10.1109/LCOMM.2018.2868103
• ISSN: 1089-7798

Recent developments in applying deep learning techniques to train end-to-end communication systems have shown great promise in improving the overall performance of the system. However, most of the current methods for applying deep learning to train physical-layer characteristics assume the availability of the explicit channel model. Training a neural network requires the availability of the functional form all the layers in the network to calculate gradients for optimization. The unavailability of gradients in a physical channel forced previous works to adopt simulation-based strategies to train the network and then fine tune only the receiver part with the actual channel. In this letter, we present a practical method to train an end-to-end communication system without relying on explicit channel models. By utilizing stochastic perturbation techniques, we show that the proposed method can train a deep learning-based communication system in real channel without any assumption on channel models.