Publications

S. Schibisch, S. Cammerer, S. Dörner, J. Hoydis, und S. ten Brink. Online Label Recovery for Deep Learning-based Communication through Error Correcting Codes. 2018 15th International Symposium on Wireless Communication Systems (ISWCS), 1-5, August 2018. [PUMA: components;Artificial impairments;corrupted myown from:sdnr codes;Communication (artificial systems;channel network;online conditions;varying label data;correct intelligence);neural networks;Receivers;Training;OFDM;Error recovery;deep codes;learning computing;trainable labels;end-to-end codes;labeled pre-equalizer correction systems;Neurons modulation;telecommunication neural set;trainable systems;error trained data learning-based training hardware adaptive systems;adaptive correcting nets;OFDM systems;trainable communication;error communication]

S. Dörner, S. Cammerer, J. Hoydis, und S. ten Brink. Deep Learning Based Communication Over the Air. IEEE Journal of Selected Topics in Signal Processing, (12)1:132-143, Februar 2018. [PUMA: myown network;over-the-air;software-defined software-defined from:sdnr transmissions;open-source (artificial systems;block-based module;transmitter transmission;receiver implementations;deep computing;two-step nets;radio intelligence);neural libraries;continuous learning;communications learning;end-to-end rate;over-the-air synchronization;frame error transmissions;receiver deep neural software synchronization data learning;modulation;neural learning libraries;software radio implementations;off-the-shelf networks;NNs;block procedure;end-to-end radio;synchronisation;telecommunication radios;Training;Receivers;Communication networks;Hardware;Transmitters;Synchronization;Autoencoder;communication;deep receivers;software systems;Artificial]

M. Arnold, S. Dörner, S. Cammerer, und S. ten Brink. On Deep Learning-Based Massive MIMO Indoor User Localization. 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 1-5, Juni 2018. [PUMA: multiple-output myown from:sdnr (artificial multiplex;complex communication;neural networks;Training;Antenna estimation;gradient system;Artificial descent modulation;deep intelligence);MIMO massive indoor localization;deep arrays;Antenna networks;multiple-input methods;learning user measurements;OFDM;Machine neural frequency systems;gradient channel learning learning-based training coefficients;indoor division nets;OFDM optimization;two-step positioning MIMO procedure;OFDM user;orthogonal]

M. Widmaier, M. Arnold, S. Dörner, S. Cammerer, und S. ten Brink. Towards Practical Indoor Positioning Based on Massive MIMO Systems. 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 1-6, September 2019. [PUMA: extractor;Artificial multiple-output myown from:sdnr (artificial communication;neural nets;telecommunication system;neural extraction arrays;Feature structure;phase measurements;Training;Antenna radio;learning system;user intelligence);MIMO acquisition;feature NN localization communication;indoor neural data networks;channel branch;indoor extraction;indoor systems;indoor scenarios;recall positioning MIMO accuracy;training networks;Antenna information;multiple-input state system;IPS computing;massive task;tailored communication]

A. Felix, S. Cammerer, S. Dörner, J. Hoydis, und S. ten Brink. OFDM-Autoencoder for End-to-End Learning of Communications Systems. 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 1-5, Juni 2018. [PUMA: amplifier;frequency-selective myown from:sdnr network-based autoencoders;single-tap computing;end-to-end (artificial networks;Synchronization;Training;Signal processing;Robustness libraries;synchronisation;telecommunication channels;cyclic fading modulation;software training;deep oscillators;commodity multiplexing;OFDM;Receivers;Artificial errors;single-carrier prefix;orthogonal implementations;reliable libraries;imprecise equalisers;fading hardware;multipath intelligence);multipath neural software synchronization frequency communication;OFDM-autoencoder;nonlinear learning equalization;sampling channels;gradient-based channels;learning division learning;deep channels;neural nets;OFDM]