An Innovative AI Architecture for Detecting the Primary User in the Spectrum

Abstract

The demand for spectrum is rising by the day as the number of consumers using the spectrum increases. However, the spectrum’s coverage is constrained to a certain area dependent on the local population. Then, researchers come up with an idea of allocating secondary users in the spectrum in the absence of primary users. For this process, a new scheme has been raised known as spectrum sensing in which the primary user’s presence using a variety of procedures. The device used for this process is called Cognitive radio. The spectrum sensing process involves gathering the signal features from the spectrum and then a threshold will be set depending on those values. With this threshold, the final block in Cognitive radio will decide whether the primary user is present or not. The techniques that are involved in spectrum sensing are energy detection, matched filtering, correlation, etc. These techniques cause a reduction in the probability of detection and involve a complex process to sense the spectrum. To overcome these drawbacks, the optimal signal is constructed from the original signal, and this, the spectrum is sensed. This process provides better results in terms of the probability of detection. To increase the scope of the research, the entropy features are extracted and trained with an LSTM based deep learning architecture. This trained network is tested with hybrid a feature which is a combination of both power-optimized features and entropy features. This process derives the spectrum status along with the accuracy and loss curves. The proposed method reduces complexity in sensing the spectrum along with that it produces an accuracy of 99.9% and the probability of detection of 1 at low PSNR values, outcomes when compared to cutting-edge techniques.