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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","misc":{"issn":"1090-3038","doi":"10.1109/VTCFall.2019.8891273"},"note":"","number":"","number-of-pages":"5","page":"1-6","page-first":"1","publisher":"","publisher-place":"","status":"","title":"Towards Practical Indoor Positioning Based on Massive MIMO Systems","type":"paper-conference","username":"inue","version":"","volume":""},"37043d0c795fb6415f42c3cbc7821139sdnr":{"DOI":"10.1109/SPAWC.2018.8446013","ISBN":"","ISSN":"1948-3252","URL":"https://ieeexplore.ieee.org/document/8446013","abstract":"We examine the usability of deep neural networks for multiple-input multiple-output (MIMO) user positioning solely based on the orthogonal frequency division multiplex (OFDM) complex channel coefficients. In contrast to other indoor positioning systems (IPSs), the proposed method does not require any additional piloting overhead or any other changes in the communications system itself as it is deployed on top of an existing OFDM MIMO system. Supported by actual measurements, we are mainly interested in the more challenging non-line of sight (NLoS) scenario. However, gradient descent optimization is known to require a large amount of data-points for training, i.e., the required database would be too large when compared to conventional methods. Thus, we propose a two-step training procedure, with training on simulated line of sight (LoS) data in the first step, and finetuning on measured NLoS positions in the second step. This turns out to reduce the required measured training positions and thus, reduces the effort for data acquisition.","annote":"","author":[{"family":"Arnold","given":"Maximilian"},{"family":"Dörner","given":"Sebastian"},{"family":"Cammerer","given":"Sebastian"},{"family":"ten Brink","given":"Stephan"}],"citation-label":"positioning_spawc","collection-editor":[],"collection-title":"","container-author":[],"container-title":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2018","June"]],"literal":"2018"},"event-place":"","id":"37043d0c795fb6415f42c3cbc7821139sdnr","interhash":"9e12fc3098e3ba0e029e11d959b7c620","intrahash":"37043d0c795fb6415f42c3cbc7821139","issue":"","issued":{"date-parts":[["2018","June"]],"literal":"2018"},"keyword":"localization ml myown","misc":{"issn":"1948-3252","doi":"10.1109/SPAWC.2018.8446013"},"note":"","number":"","number-of-pages":"4","page":"1-5","page-first":"1","publisher":"","publisher-place":"","status":"","title":"On Deep Learning-Based Massive MIMO Indoor User Localization","type":"paper-conference","username":"sdnr","version":"","volume":""},"610dffb2ee56e926e84dc38d72403650sdnr":{"DOI":"10.1109/VTCFall.2019.8891273","ISBN":"","ISSN":"1090-3038","URL":"https://ieeexplore.ieee.org/document/8891273","abstract":"We showcase the practicability of an indoor positioning system (IPS) solely based on neural networks (NNs) and the channel state information (CSI) of a (Massive) multiple-input multiple-output (MIMO) communication system, i.e., only build on the basis of data that is already existent in today's systems. As such our IPS system promises both, a good accuracy without the need of any additional protocol/signaling overhead for the user localization task. In particular, we propose a tailored NN structure with an additional phase branch as feature extractor and (compared to previous results) a significantly reduced amount of trainable parameters, leading to a minimization of the amount of required training data. We provide actual measurements for indoor scenarios with up to 64 antennas covering a large area of 80m 2 . In the second part, several robustness investigations for real-measurements are conducted, i.e., once trained, we analyze the recall accuracy over a period of several days. Further, we analyze the impact of pedestrians walking in-between the measurements and show that finetuning and pre-training of the NN helps to mitigate effects of hardware drifts and alterations in the propagation environment over time. This reduces the amount of required training samples at equal precision and, thereby, decreases the effort of the costly training data acquisition.","annote":"","author":[{"family":"Widmaier","given":"Mark"},{"family":"Arnold","given":"Maximilian"},{"family":"Dörner","given":"Sebastian"},{"family":"Cammerer","given":"Sebastian"},{"family":"ten Brink","given":"Stephan"}],"citation-label":"positioning_2","collection-editor":[],"collection-title":"","container-author":[],"container-title":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2019","Sep."]],"literal":"2019"},"event-place":"","id":"610dffb2ee56e926e84dc38d72403650sdnr","interhash":"335beb417e4f5878b61eae57e246d5f5","intrahash":"610dffb2ee56e926e84dc38d72403650","issue":"","issued":{"date-parts":[["2019","Sep."]],"literal":"2019"},"keyword":"localization ml myown","misc":{"issn":"1090-3038","doi":"10.1109/VTCFall.2019.8891273"},"note":"","number":"","number-of-pages":"5","page":"1-6","page-first":"1","publisher":"","publisher-place":"","status":"","title":"Towards Practical Indoor Positioning Based on Massive MIMO Systems","type":"paper-conference","username":"sdnr","version":"","volume":""}}