An End-to-End Scheme for Learning Over Compressed Data Transmitted Through a Noisy Channel

Abstract

Within the emerging area of goal-oriented communications, this paper introduces a novel end-to-end transmission scheme dedicated to learning over a noisy channel, under the constraint that no prior training dataset is available. In this scheme, the transmitter makes use of powerful Spherical Harmonic Transform and Irregular Hexagonal Quadratic Amplitude Modulation techniques, while the receiver relies on a Complex-Valued Neural Network (CVNN) so as to realize the learning task onto the received noisy data. As a main feature of the proposed scheme, the transmitter is fixed and does not depend on the source statistics, while the receiver is trained from a first data transmission phase, thus providing an efficient transmission-versus-learning approach under the considered constraint. The proposed transmission scheme may be adapted to a variety of learning problems, and the paper specifically investigates clustering and classification, two very common learning tasks. In the last part of the paper, the source/channel coding rate of the proposed transmission scheme is evaluated theoretically and from numerical simulations. This analysis shows a clear advantage in terms of coding rate of our scheme compared to conventional coding approaches, when targeting the same learning performance level.