FEW-SHOT DOMAIN ADAPTATION FOR END-TO-END COMMUNICATION

Abstract

The problem of end-to-end learning of a communication system using an autoencoder -consisting of an encoder, channel, and decoder modeled using neural networks -has recently been shown to be an effective approach. A challenge faced in the practical adoption of this learning approach is that under changing channel conditions (e.g. a wireless link), it requires frequent retraining of the autoencoder in order to maintain a low decoding error rate. Since retraining is both time consuming and requires a large number of samples, it becomes impractical when the channel distribution is changing quickly. We propose to address this problem using a fast and sample-efficient (few-shot) domain adaptation method that does not change the encoder and decoder networks. Different from conventional training-time unsupervised or semi-supervised domain adaptation, here we have a trained autoencoder from a source distribution that we want to adapt (at test time) to a target distribution using only a small labeled dataset, and no unlabeled data. We focus on a generative channel model based on the Gaussian mixture density network (MDN), and propose a regularized, parameter-efficient adaptation of the MDN using a set of affine transformations. The learned affine transformations are then used to design an optimal transformation at the decoder input to compensate for the distribution shift, and effectively present to the decoder inputs close to the source distribution. Experiments on many simulated distribution changes common to the wireless setting, and a real mmWave FPGA testbed demonstrate the effectiveness of our method at adaptation using very few target domain samples 1 .

1. INTRODUCTION

End-to-end (e2e) learning of a communication system using an autoencoder has been recently shown to be a promising approach for designing the next generation of wireless networks (O'Shea & Hoydis, 2017; Dörner et al., 2018; Aoudia & Hoydis, 2019; O'Shea et al., 2019; Ye et al., 2018; Wang et al., 2017) . This new paradigm is a viable alternative for optimizing communication in diverse applications, hardware, and environments (Hoydis et al., 2021) . It is particularly promising for dense deployments of low-cost transceivers, where there is interference between the devices and hardware imperfections that are difficult to model analytically. The key idea of e2e learning for a communication system is to use an autoencoder architecture to model and learn the transmitter and receiver jointly using neural networks in order to minimize the e2e symbol error rate (SER). The channel (i.e., propagation medium and transceiver imperfections) can be represented as a stochastic transfer function that transforms its input z ∈ R d to an output x ∈ R d . It can be regarded as a black-box that is typically non-linear and non-differentiable due to hardware imperfections (e.g., quantization and amplifiers). Since autoencoders are trained using stochastic gradient descent (SGD)-based optimization (O'Shea & Hoydis, 2017) , it is challenging to work with a black-box channel that is not differentiable. One approach to address this problem is to use a known mathemat-



Code for our work: https://github.com/jayaram-r/domain-adaptation-autoencoder 1

