Deep Learning for Future: Advancements and Prospects in Signal Denoising for Cognitive Radio
Noureddine El-Haryqy, Abdelmouttalib Bousrout, Zhour Madini, Younes Zouine · 2024
Within communication systems, the challenge of noise and interference presents a formidable barrier to reliable data transmission. While classical denoising methods have provided valuable insights, their limitations impede their effectiveness. However, the emergence of deep learning offers a promising solution, especially within the domain of cognitive radio. Deep learning techniques exhibit remarkable efficacy in addressing noise and interference across various signal types. Recent research emphasizes the transformative potential of deep learning in advancing signal denoising methodologies, highlighting its pivotal role in bolstering the performance of cognitive radio systems.