• Vivek Banerjee
• Rajesh Rai

In the next generation of wireless communication systems, resource allocation is crucial, particularly for cognitive radio networks (CRNs). Several approaches to resource allocation have been put forth in an effort to maximize CRN performance. However, since most wireless systems require precise and timely channel state information and/or other network statistics, putting these strategies into practice and achieving real-time performance in wireless systems is difficult. This paper presents a training method to train the neural networks and suggests a resource allocation strategy based on deep learning (DL). When compared to traditional resource allocation schemes, simulation results demonstrate the computational efficiency of our DL-based approach.

CRN, Wireless Communication, Deep Learning, Spectrum, Energy Efficiency

"Resource Allocation in Cognitive Radio Networks Based on Deep Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.e165-e170, December-2023, Available :http://www.jetir.org/papers/JETIR2312422.pdf

"Resource Allocation in Cognitive Radio Networks Based on Deep Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppe165-e170, December-2023, Available at : http://www.jetir.org/papers/JETIR2312422.pdf