https://puma.ub.uni-stuttgart.de/user/sdnr/decodingPUMA RSS feed for /user/sdnr/decoding2026-04-23T09:11:37+02:00https://puma.ub.uni-stuttgart.de/bibtex/257d4852a03cf87cde7d4ab9b446fb787/sdnrsdnr2023-02-02T15:12:32+01:00autoencoder decoding detection joint ml myown <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sebastian Dörner" itemprop="url" href="/person/1ea02e0e94da595145c8078efd88bba5f/author/0"><span itemprop="name">S. Dörner</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jannis Clausius" itemprop="url" href="/person/1ea02e0e94da595145c8078efd88bba5f/author/1"><span itemprop="name">J. Clausius</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sebastian Cammerer" itemprop="url" href="/person/1ea02e0e94da595145c8078efd88bba5f/author/2"><span itemprop="name">S. Cammerer</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan ten Brink" itemprop="url" href="/person/1ea02e0e94da595145c8078efd88bba5f/author/3"><span itemprop="name">S. ten Brink</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">IEEE Transactions on Communications</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">71 </span></span>(<span itemprop="issueNumber">2</span>): <span itemprop="pagination">837-850</span></em> </span>(<em><span>2023<meta content="2023" itemprop="datePublished"/></span></em>)</span>Thu Feb 02 15:12:32 CET 2023IEEE Transactions on Communications2837-850Learning Joint Detection, Equalization and Decoding for Short-Packet Communications712023autoencoder decoding detection joint ml myown We propose and practically demonstrate a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a message before actually decoding it. For this, we extend the recently proposed serial Turbo-autoencoder neural network (NN) architecture and train it to find short messages that can be, all “at once”, detected, synchronized, equalized and decoded when sent over an unsynchronized channel with memory. The conceptional advantage of the proposed system stems from a holistic message structure with superimposed pilots for joint detection and decoding without the need of relying on a dedicated preamble. This results not only in a higher spectral efficiency, but also translates into the possibility of shorter messages compared to using a dedicated preamble. We compare the detection error rate (DER), bit error rate (BER) and block error rate (BLER) performance of the proposed system with a hand-crafted state-of-the-art conventional baseline and our simulations show a significant advantage of the proposed autoencoder-based system over the conventional baseline in every scenario up to messages conveying k =96 information bits. Finally, we practically evaluate and confirm the improved performance of the proposed system over-the-air (OTA) using a software-defined radio (SDR)-based measurement testbed.