Transfer Learning for Noise Reduction in Wireless Communication Channels

In the presence of dynamic and non stationary environments which the existing techniques of traditional noise reduction are not so well adapted. In this work, this work studies transfer learning as a way to improve noise reduction in other wireless communication channels by using pretrained multichannel models or using domain adaptation strategies. The answer to this reason is why we utilize big data sets trained on both state of the art models with deep learning architectures; Convolutional Neural Networks (CNN) and Transformer based models. We then base ourselves on these representations robust to noisy signals in the frame of self supervised learning framworks and learn to find them tuned for a given channel conditions. To this end, we also simulate noise profiles using GANs for resolution to learned models. We demonstrate this with experimental results of the given wireless signal benchmarks in terms of improved SNR and Bit Error Rate. We demonstrate that actual continuous suppression of noise on an evolving wireless occasions is possible with present day AI frameworks and move learning at scale and adaptability.