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RNN-Bi-LSTM spectrum sensing algorithm for NOMA waveform with diverse channel conditions.

Arun Kumar1, Aziz Nanthaamornphong2, Mehedi Masud3

  • 1Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Rangpo, India.

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|August 23, 2025
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Summary
This summary is machine-generated.

A novel Recurrent Neural Network-based Bidirectional Long Short-Term Memory (RNN-Bi-LSTM) model significantly improves spectrum sensing for Non-Orthogonal Multiple Access (NOMA) systems. This advanced model enhances detection accuracy and reduces spectral leakage across diverse Rician and Rayleigh fading channels.

Keywords:
BERNOMAPDPFARNN-Bi-LSTMSpectrum sensing

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Non-Orthogonal Multiple Access (NOMA) is crucial for spectral efficiency in 5G/6G systems.
  • NOMA performance is sensitive to channel conditions, requiring robust spectrum sensing.
  • Existing spectrum sensing methods struggle with varying channel impairments.

Purpose of the Study:

  • To propose and evaluate a novel Recurrent Neural Network-based Bidirectional Long Short-Term Memory (RNN-Bi-LSTM) model.
  • To enhance spectral performance of NOMA systems under Rician and Rayleigh fading channels.
  • To improve spectrum sensing accuracy and efficiency in advanced wireless networks.

Main Methods:

  • Development of an RNN-Bi-LSTM model for spectrum sensing.
  • Evaluation using key performance metrics: Probability of Detection (PD), Probability of False Alarm (PFA), Bit Error Rate (BER), and Power Spectral Density (PSD).
  • Comparative analysis against conventional methods like RNN, LSTM, CSD, Matched Filter (MF), and Energy Detection (ED).

Main Results:

  • RNN-Bi-LSTM achieved 100% PD at -5 dB and -2.5 dB SNR in Rician and Rayleigh channels, respectively, outperforming other methods.
  • Demonstrated a 23.36% improvement in PSD suppression under Rician conditions compared to Rayleigh, indicating reduced spectral leakage.
  • Achieved superior BER performance (10⁻⁵ at 8.8 dB and 5.8 dB SNR) and more accurate PSD estimation.

Conclusions:

  • The RNN-Bi-LSTM model offers superior adaptability and robustness for NOMA spectrum sensing across varying channel conditions.
  • The proposed model significantly enhances spectral efficiency and utilization in next-generation wireless systems.
  • RNN-Bi-LSTM presents an efficient and reliable solution for advanced wireless communication spectrum management.