Shared Spectrum Monitoring Using Deep Learning
| Autor: | Farrukh A. Bhatti, Ahmed Selim, M. Jaleed Khan, Francisco Paisana |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science Universal Software Radio Peripheral Deep learning ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS 010401 analytical chemistry Real-time computing Bandwidth (signal processing) 02 engineering and technology Filter bank 01 natural sciences Convolutional neural network 0104 chemical sciences law.invention Artificial Intelligence Hardware and Architecture law 0202 electrical engineering electronic engineering information engineering Wireless Spectrogram 020201 artificial intelligence & image processing Artificial intelligence Radar business |
| Zdroj: | IEEE Transactions on Cognitive Communications and Networking. 7:1171-1185 |
| ISSN: | 2372-2045 |
| Popis: | Shared spectrum usage is inevitable due to the ongoing increase in wireless services and bandwidth requirements. Spectrum monitoring is a key enabler for efficient spectrum sharing by multiple radio access technologies (RATs). In this paper, we present signal classification using deep neural networks to identify various radio technologies and their associated interferences. We use Convolutional Neural Networks (CNN) to perform signal classification and employ six well-known CNN models to train for ten signal classes. These classes include LTE, Radar, WiFi and FBMC (Filter Bank Multicarrier) and their interference combinations, which include, LTE+Radar, LTE+WiFi, FBMC+Radar, FBMC+WiFi, WiFi+Radar and Noise. The CNN models include, AlexNet, VGG16, ResNet18, SqueezeNet, InceptionV3 and ResNet50. The radio signal data sets for training and testing of CNN-based classifiers are acquired using a USRP-based experimental setup. Extensive measurements of these radio technologies (LTE, WiFi, Radar and FBMC) are done over different locations and times to generate a robust dataset. We propose a novel (spectrogram) representation called the Quarter-spectrogram (Q-spectrogram) that squeezes temporal and frequency information for input to CNN models. While considering classification accuracy, model complexity and prediction time for a single input Q-spectrogram (image), ResNet18 (CNN model) gives the best overall performance with 98% classification accuracy. While SqueezeNet (CNN model) offers the lowest model complexity which makes it very suitable for resource-constrained radio monitoring devices and also offers the least prediction time of 110 msec. Moreover, we also propose a simple WiFi classification scheme that buffers several WiFi Q-spectrograms and then makes a decision about WiFi’s presence and also gives a quantified measure of WiFi traffic density. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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