Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching-Reference-Cited by-同舟云学术

Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching

Published:2022-11-09 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)
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Author:

Rene Jativa E.¹,Salazar Anthony¹,Beltran Katty¹,Caisaluisa Oliver¹

Affiliation:

1. Colegio de Ciencias e Ingeniería, Universidad San Francisco de Quito,Quito,Ecuador

Funder

USFQ

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10018605/10018571/10018626.pdf?arnumber=10018626

Reference13 articles.

1. Propagation, blockage and coverage evaluation in 5G urban wireless networks;arevalo;2021 Global Congress on Electrical Engineering (GC-ElecEng),2021

2. A Comparative Performance Evaluation of Machine Learning Algorithms for Fingerprinting Based Localization in DM-MIMO Wireless Systems Relying on Big Data Techniques

3. Exposing the Fingerprint: Dissecting the Impact of the Wireless Channel on Radio Fingerprinting

4. Real time evaluation of RF fingerprints in wireless LAN localization systems

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1. Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental Principles, Challenges, and Opportunities;MIMO Communications - Fundamental Theory, Propagation Channels, and Antenna Systems;2023-12-20

2. Radio Frequency Pattern Matching - Subscriber Location in 5G Networks;Applications of Computational Intelligence;2023-11-18

3. Radio Frequency Pattern Matching - Smart Subscriber Location in 5G mmWave Networks;2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI);2023-07-26