{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/29a6985c1d9aa4f77b2cd33b1b6615277/sdnr", "tags" : [ "channel","charting","mimo","myown","triplet" ], "intraHash" : "9a6985c1d9aa4f77b2cd33b1b6615277", "interHash" : "6dfc7e52bf293f05fbd79233b7f048bd", "label" : "Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements", "user" : "sdnr", "description" : "", "date" : "2022-08-15 13:51:58", "changeDate" : "2022-08-16 12:00:08", "count" : 7, "pub-type": "inproceedings", "booktitle": "2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)", "year": "2022", "url": "https://ieeexplore.ieee.org/document/9833925", "author": [ "Florian Euchner","Phillip Stephan","Marc Gauger","Sebastian Dörner","Stephan ten Brink" ], "authors": [ {"first" : "Florian", "last" : "Euchner"}, {"first" : "Phillip", "last" : "Stephan"}, {"first" : "Marc", "last" : "Gauger"}, {"first" : "Sebastian", "last" : "Dörner"}, {"first" : "Stephan", "last" : "ten Brink"} ], "abstract": "The objective of channel charting is to learn a virtual map of the radio environment from high-dimensional channel state information (CSI) that is acquired by a multi-antenna wireless system. Since, in static environments, CSI is a function of the transmitter location, a mapping from CSI to channel chart coordinates can be learned in a self-supervised manner using dimensionality reduction techniques. The state-of-the-art triplet-based approach is evaluated on multiple datasets measured by a distributed massive multiple-input multiple-output (MIMO) channel sounder, with both co-located and distributed antenna setups. The importance of suitable triplet selection is investigated by comparing results to channel charts learned from a genie-aided triplet generator and learned from triplets on simulated trajectories through measured data. Finally, the transferability of learned forward charting functions to similar, but different radio environments is explored.", "doi" : "10.1109/SPAWC51304.2022.9833925", "bibtexKey": "EuchnerCC2022" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2fd8ae4ab782e3b470967adc34723afc0/sdnr", "tags" : [ "channel","csi","distributed","mimo","ml","myown","sounder" ], "intraHash" : "fd8ae4ab782e3b470967adc34723afc0", "interHash" : "ad997828ebffcf60d901bce85a6286d2", "label" : "A Distributed Massive MIMO Channel Sounder for \"Big CSI Data\"-driven Machine Learning", "user" : "sdnr", "description" : "", "date" : "2022-04-26 16:12:58", "changeDate" : "2022-04-27 10:55:08", "count" : 3, "pub-type": "inproceedings", "booktitle": "25th International ITG Workshop on Smart Antennas (WSA 2021)", "year": "2021", "url": "https://ieeexplore.ieee.org/document/9739175", "author": [ "Florian Euchner","Marc Gauger","Sebastian Dörner","Stephan ten Brink" ], "authors": [ {"first" : "Florian", "last" : "Euchner"}, {"first" : "Marc", "last" : "Gauger"}, {"first" : "Sebastian", "last" : "Dörner"}, {"first" : "Stephan", "last" : "ten Brink"} ], "abstract": "A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of different machine learning algorithms for user localization, JCAS, channel charting, enabling massive MIMO in FDD operation, and many others. The proposed channel sounder architecture is distinct from similar previous designs in that each individual single-antenna receiver is completely autonomous, enabling arbitrary grouping into spatially distributed antenna deployments, and offering virtually unlimited scalability in the number of antennas. Optionally, extracted channel coefficient vectors can be tagged with ground truth position data, obtained either through a GNSS receiver (for outdoor operation) or through various indoor positioning techniques.", "venue" : "Sophia Antipolis, France", "isbn" : "978-3-8007-5686-5", "bibtexKey": "euchner2021distributed" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2fd8ae4ab782e3b470967adc34723afc0/inue", "tags" : [ "myown","sounder","from:sdnr","distributed","channel","csi","mimo","ml" ], "intraHash" : "fd8ae4ab782e3b470967adc34723afc0", "interHash" : "ad997828ebffcf60d901bce85a6286d2", "label" : "A Distributed Massive MIMO Channel Sounder for \"Big CSI Data\"-driven Machine Learning", "user" : "inue", "description" : "", "date" : "2022-04-26 16:12:58", "changeDate" : "2022-04-26 14:12:58", "count" : 3, "pub-type": "inproceedings", "booktitle": "25th International ITG Workshop on Smart Antennas (WSA 2021)", "year": "2021", "url": "https://ieeexplore.ieee.org/document/9739175", "author": [ "Florian Euchner","Marc Gauger","Sebastian Dörner","Stephan ten Brink" ], "authors": [ {"first" : "Florian", "last" : "Euchner"}, {"first" : "Marc", "last" : "Gauger"}, {"first" : "Sebastian", "last" : "Dörner"}, {"first" : "Stephan", "last" : "ten Brink"} ], "abstract": "A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of different machine learning algorithms for user localization, JCAS, channel charting, enabling massive MIMO in FDD operation, and many others. The proposed channel sounder architecture is distinct from similar previous designs in that each individual single-antenna receiver is completely autonomous, enabling arbitrary grouping into spatially distributed antenna deployments, and offering virtually unlimited scalability in the number of antennas. Optionally, extracted channel coefficient vectors can be tagged with ground truth position data, obtained either through a GNSS receiver (for outdoor operation) or through various indoor positioning techniques.", "venue" : "Sophia Antipolis, France", "bibtexKey": "euchner2021distributed" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2a0432022d14f27c777159d03d194b7a9/inue", "tags" : [ "myown","from:sdnr","mimo","ml" ], "intraHash" : "a0432022d14f27c777159d03d194b7a9", "interHash" : "f67846f6952105b0bf3d6ce119ffc942", "label" : "Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction", "user" : "inue", "description" : "", "date" : "2020-03-20 15:21:22", "changeDate" : "2020-03-20 14:21:22", "count" : 3, "pub-type": "misc", "year": "2019", "url": "https://arxiv.org/abs/1901.03664", "author": [ "Maximilian Arnold","Sebastian Dörner","Sebastian Cammerer","Sarah Yan","Jakob Hoydis","Stephan ten Brink" ], "authors": [ {"first" : "Maximilian", "last" : "Arnold"}, {"first" : "Sebastian", "last" : "Dörner"}, {"first" : "Sebastian", "last" : "Cammerer"}, {"first" : "Sarah", "last" : "Yan"}, {"first" : "Jakob", "last" : "Hoydis"}, {"first" : "Stephan", "last" : "ten Brink"} ], "eprint" : "1901.03664", "archiveprefix" : "arXiv", "primaryclass" : "cs.IT", "bibtexKey": "FDDmMIMO" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2dfa09f10917bf31c26f1584616b1c9e6/inue", "tags" : [ "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" ], "intraHash" : "dfa09f10917bf31c26f1584616b1c9e6", "interHash" : "9e12fc3098e3ba0e029e11d959b7c620", "label" : "On Deep Learning-Based Massive MIMO Indoor User Localization", "user" : "inue", "description" : "", "date" : "2020-03-20 15:07:06", "changeDate" : "2020-03-20 14:07:06", "count" : 3, "pub-type": "inproceedings", "booktitle": "2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)", "year": "2018", "url": "", "author": [ "M. Arnold","S. Dörner","S. Cammerer","S. ten Brink" ], "authors": [ {"first" : "M.", "last" : "Arnold"}, {"first" : "S.", "last" : "Dörner"}, {"first" : "S.", "last" : "Cammerer"}, {"first" : "S.", "last" : "ten Brink"} ], "pages": "1-5", "issn" : "1948-3252", "doi" : "10.1109/SPAWC.2018.8446013", "bibtexKey": "positioning_spawc" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2a76ca2f1d096a8c6b9fec9442f25ad16/inue", "tags" : [ "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" ], "intraHash" : "a76ca2f1d096a8c6b9fec9442f25ad16", "interHash" : "335beb417e4f5878b61eae57e246d5f5", "label" : "Towards Practical Indoor Positioning Based on Massive MIMO Systems", "user" : "inue", "description" : "", "date" : "2020-03-20 15:07:06", "changeDate" : "2020-03-20 14:07:06", "count" : 5, "pub-type": "inproceedings", "booktitle": "2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)", "year": "2019", "url": "", "author": [ "M. Widmaier","M. Arnold","S. Dörner","S. Cammerer","S. ten Brink" ], "authors": [ {"first" : "M.", "last" : "Widmaier"}, {"first" : "M.", "last" : "Arnold"}, {"first" : "S.", "last" : "Dörner"}, {"first" : "S.", "last" : "Cammerer"}, {"first" : "S.", "last" : "ten Brink"} ], "pages": "1-6", "issn" : "1090-3038", "doi" : "10.1109/VTCFall.2019.8891273", "bibtexKey": "positioning_2" } ] }