Blind Transmitter Localization Using Deep Learning: A Scalability Study-Reference-Cited by-同舟云学术

Blind Transmitter Localization Using Deep Learning: A Scalability Study

Published:2023-03 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Wireless Communications and Networking Conference (WCNC)
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Author:

Bizon Ivo¹,Nimr Ahmad¹,Schulz Philipp¹,Chafii Marwa²,Fettweis Gerhard P.¹

Affiliation:

1. Technische Universität Dresden (TUD),Vodafone Chair Mobile Communications Systems,Germany

2. New York University (NYU),Engineering Division,Abu Dhabi,UAE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10118584/10118587/10119115.pdf?arnumber=10119115

Reference21 articles.

1. RSS-based Localization of Multiple Unknown Transmitters through Particle Simulation

2. Real-time cooperative spectrum sensing: Reliable localization of unknown interferers in shared spectrum;kulkarni;SCC 2019 12th International ITG Conference on Systems Communications and Coding,2019

3. Exploiting transmitter I/Q imbalance for estimating the number of active users

4. An Additive Model as a Physical Basis for Shadow Fading

5. Constructing accurate Radio Environment Maps with Kriging Interpolation in Cognitive Radio Networks

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1. LSTM-DNN-Based Joint Wireless Signal Waveform Classification and Blind Transmitter Localization;2024 19th International Symposium on Wireless Communication Systems (ISWCS);2024-07-14

2. Localization Through Deep Learning in New and Low Sampling Rate Environments;Lecture Notes in Computer Science;2024

3. On Integrated Cooperative Radio Sensing for Spatial Electromagnetic Analysis in 6G;2023 IEEE Future Networks World Forum (FNWF);2023-11-13