JoWUA

Abstract

This paper proposes a new deep learning estimation algorithm of spectrum sensing and channel estimation in Cognitive Radio Sensor Networks (CRSNs) using Non-Orthogonal Multiple Access (NOMA). The given model will combine Long Short-Term Memory (LSTM) Networks with Bayesian Neural Networks (BNNs) to improve the work of the system in dynamic and unpredictable wireless conditions. The LSTM networks are used to predict with accuracy complex-valued Rayleigh Fading Channels and Bayesian is used to model the uncertainty with which such predictions are made. Also, a parallel Bayesian LSTM spectrum sensing model classifies activity of primary users (PU) to provide intelligent spectrum access and reduce interference. Prediction and spectrum sensing: Prediction and spectrum sensing is possible with the model in real-time and this is important in efficient spectrum management in CRSN where the Mean Absolute Error (MAE) of channel estimation is brought to under 0.02, implying high accuracy of channel condition prediction. Results of simulation demonstrate a significant enhancement when compared with traditional systems. The maximum accuracy of the spectrum sensing in the model is 98% at Signal to Noise Ratio of 10 dB and also a low Bit Error Rate (BER). LSTM combined with Bayesian inference structuring enables a combination of accurate channel estimation and trusted spectrum sensing, which are significant in terms of accuracy and the quantification of the uncertainties. These findings indicate the possibility of the proposed Bayesian-enhanced LSTM model to enhance the CRSN performance, especially in a low SNR and high-interference environment. This method is superior to the traditional models, which is a guarantee of the stable communication and spectrum utilization in the complicated wireless settings.