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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Spectrum occupancy prediction using LSTM models for cognitive radio applications.

Tamizhelakkiya Kolangiyappan1, Sabitha Gauni1,2, Prabhu Chandhar3

  • 1Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.

Network (Bristol, England)
|September 30, 2024
PubMed
Summary

This study introduces a Long Short-Term Memory (LSTM) model for spectrum occupancy prediction in cognitive radio networks. The LSTM approach enhances mobile traffic prediction accuracy for efficient spectrum management.

Keywords:
ARIMACNNCognitive RadioLSTMTraffic prediction

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Area of Science:

  • Telecommunications Engineering
  • Machine Learning for Wireless Networks

Background:

  • Mobile traffic prediction is crucial for spectrum management in next-generation cellular networks, particularly in Cognitive Radio (CR) applications.
  • Accurate prediction enables efficient dynamic spectrum access and resource allocation.

Purpose of the Study:

  • To propose and evaluate a Long Short-Term Memory (LSTM) based Spectrum Occupancy Prediction (SOP) approach for infrastructure-based cellular traffic systems.
  • To compare the performance of various LSTM models against traditional statistical methods.

Main Methods:

  • A binary dataset was created by monitoring spectrum activities across nine Long Term Evolution (LTE) frequency channels.
  • Multiple LSTM models (Convolutional, CNN, Stacked, Bidirectional) were trained offline and tested on the dataset.
  • Prediction performance was evaluated using Mean Absolute Error (MAE).

Main Results:

  • The proposed LSTM-based SOP model demonstrated superior prediction accuracy compared to the Auto-Regressive Integrated Moving Average (ARIMA) model.
  • The LSTM model achieved 2.5% higher prediction accuracy, effectively aligning traffic trends with actual samples.

Conclusions:

  • LSTM-based models offer a significant improvement for spectrum occupancy prediction in cellular networks.
  • This approach facilitates more effective spectrum management and resource utilization in CR applications.