Sensing Mechanism of Cognitive Radio Interference Reduction using Convolution Neural Network (CNN) Based Internet of Things (IOT)
Keywords:
Mechanism, Cognitive, Radio, Interference, Convolution Neural NetworkAbstract
The persistent failure in communication network failure were anchored by Received signal to noise ratio, Interference to noise ratio, Interference to signal ratio, Receiver noise figure, and Quantized effect bits that could not attain their respective thresholds. This failure in communication network was overcome by introducing sensing mechanism of cognitive radio interference reduction using convolution neural network. To achieve this, it was done in this manner, the present metric causes of poor sensing mechanism of cognitive radio as a result of interference was characterized and established, conventional SIMULINK model for sensing mechanism of cognitive radio was designed, CNN was trained in the present metric causes of poor sensing mechanism of cognitive radio as a result of interference for quick minimization, SIMULINK model for designed and an algorithm that would implement the process was developed. Then, the SIMULINK model for sensing mechanism of cognitive radio interference reduction using convolution neural network (CNN) based intern ate of things (IOT) was designed and the results obtained were justified and validated. the results obtained were the conventional Received signal to noise ratio that causes poor sensing mechanism of cognitive radio was 0.8dB. Om the other hand, when CNN was integrated into it , it instantly increased to 1.04dB. and the conventional Interference to noise ratio that causes poor sensing mechanism of cognitive radio was 1.4dB. Meanwhile, when CNN was integrated into the system, It automatically reduced to 1db thereby meeting the threshold of 1 dB. Finally, with these results obtained, the percentage improvement in sensing mechanism of cognitive radio interference reduction when convolution neural network (CNN) based intern ate of things (IOT) was28.6%, the CNN-based interference reduction mechanism provides a more intelligent, efficient, and adaptive solution for spectrum sensing in IoT-enabled cognitive radio networks. Future work can focus on hybrid deep learning models and edge computing integration to further enhance real-time performance and system scalability.
References
Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. *Computer Networks, 50*(13), 2127–2159. [https://doi.org/10.1016/j.comnet.2006.05.001](https://doi.org/10.1016/j.comnet.2006.05.001)
Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. *IEEE Journal on Selected Areas in Communications, 23*(2), 201–220. [https://doi.org/10.1109/JSAC.2004.839380](https://doi.org/10.1109/JSAC.2004.839380)
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. *Nature, 521*(7553), 436–444. [https://doi.org/10.1038/nature14539](https://doi.org/10.1038/nature14539)
Liu, Y., Ding, Z., Elkashlan, M., & Poor, H. V. (2020). Cooperative non-orthogonal multiple access with simultaneous wireless information and power transfer. *IEEE Journal on Selected Areas in Communications, 34*(4), 938–953. [https://doi.org/10.1109/JSAC.2016.2549360](https://doi.org/10.1109/JSAC.2016.2549360)
