PERFORMANCE EVALUATION OF SPECTRUM SENSING TECHNIQUES USING DEEP LEARNING FOR COGNITIVE RADIO NETWORKS

Abstract

In recent years, the spectrum demand for wireless communication services and applications has been increasing drastically besides spectrum resource management and allocation have become a hot issue. Cognitive Radio (CR) is designed and implemented to overcome this existing problem by allocating a spectrum band to Primary and Secondary users dynamically. One of the key features to decide over spectrum utilization for a CR is the spectrum sensing (SS) unit which detects and identifies spectral data from the environment. Conventional SS schemes such as Energy detection (ED), Cyclo-stationary and matched filters were first developed and employed on CRs. Their drawbacks such as the inability to exploit both spatial and temporal features of data, high false alarms and less detection probability over noisy data lead to further studies to develop AI, particularly Machine learning (ML) and Deep learning (DL) integrated models. This thesis work is mainly focused on the performance of DL-based models to sense, predict and classify a spectral dataset. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) models are adapted and their performance in spectrum classification has been evaluated. One of the major contributions of this thesis work was to adapt a hybrid Convolutional Recurrent neural network (CRNN) and to compare its performance with the above existing Neural Network models. CNN is a good performing model in extracting spatial features whereas RNN performs well in extracting temporal features of spectral data. The performance of these DL models has been evaluated using metrics such as classification accuracy, probability of detection (Pd), probability of false alarm (Pfa), Sensing error (SE) and confusion matrix metric formulations. The signal samples were generated with GNU for SNRs from -20 dB to 18 dB with step size of 2 dB over flat fading channel and AWGN. This reliable synthetic dataset consists of 11 modulations with varying SNR levels to train, validate and test our DL models. The simulation experiment was carried out in Python Notebook and virtual Google- Colab environment. The results show our proposed hybrid model outperforms the other DL models in terms of high classification accuracy, high probability of detection and less SE. The LSTM model also performed better than CNN and RNN models with its less probability of false alarm in identifying a signal feature. Although all the DL models proved their better performances, CNN was less accurate in identifying the signal feature particularly in low SNR ranges.