Mobile Sensor Location Optimization U sing Support Vector Machines with Error-Correcting Output Codes
| Autor: | Nader M. Namazi, Feng Ouyang, Sharif H. R. Khalil |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning 05 social sciences 050801 communication & media studies Free-space path loss Azimuth Support vector machine 0508 media and communications Side lobe 0502 economics and business Telecommunications link 050211 marketing Radio frequency Artificial intelligence business Algorithm |
| Zdroj: | 2019 2nd World Symposium on Communication Engineering (WSCE). |
| DOI: | 10.1109/wsce49000.2019.9040991 |
| Popis: | This work is concerned with the introduction and development of a technique to optimally position a Mobile Sensor (MS) in a location with adequate side lobe Radio Frequency (RF) signal power. The proposed method involves the generation of a database (DB) of side lobe power distribution for different azimuth angles of the downlink transmitted signal. The generated DB is subsequently used to train and test a Machine Learning (ML) multiclass classifier, as well as two distinct Convolution Neural Networks (CNN), to identify the desired MS location. Simulation experiments are performed which indicate a maximum accuracy of 99.25%, 96.56% and 96.10% for 8 different receiver locations. |
| Databáze: | OpenAIRE |
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