Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Adaptation for Autoencoder-Based End-to-End Communication Over
  Wireless Channels   The problem of domain adaptation conventionally considers the setting where a
source domain has plenty of labeled data, and a target domain (with a different
data distribution) has plenty of unlabeled data but none or very limited
labeled data. In this paper, we address the setting where the target domain has
only limited labeled data from a distribution that is expected to change
frequently. We first propose a fast and light-weight method for adapting a
Gaussian mixture density network (MDN) using only a small set of target domain
samples. This method is well-suited for the setting where the distribution of
target data changes rapidly (e.g., a wireless channel), making it challenging
to collect a large number of samples and retrain. We then apply the proposed
MDN adaptation method to the problem of end-of-end learning of a wireless
communication autoencoder. A communication autoencoder models the encoder,
decoder, and the channel using neural networks, and learns them jointly to
minimize the overall decoding error rate. However, the error rate of an
autoencoder trained on a particular (source) channel distribution can degrade
as the channel distribution changes frequently, not allowing enough time for
data collection and retraining of the autoencoder to the target channel
distribution. We propose a method for adapting the autoencoder without
modifying the encoder and decoder neural networks, and adapting only the MDN
model of the channel. The method utilizes feature transformations at the
decoder to compensate for changes in the channel distribution, and effectively
present to the decoder samples close to the source distribution. Experimental
evaluation on simulated datasets and real mmWave wireless channels demonstrate
that the proposed methods can quickly adapt the MDN model, and improve or
maintain the error rate of the autoencoder under changing channel conditions.

30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex