Deep learning (DL) provides a framework for designing new communication systems that
embrace practical impairments. In this paper, we present an exploration of DL as applied to …

… channels. In this article, we take the advantage of deep learning approaches to handle the
various channel … the advantage of deep learning to handle the various channel distortions, by …

… driven deep learning (DL) equalization scheme is proposed to deal with the severe channel
… module in traditional system for learning the channel nonlinearity and the symbol de-…

… EXPERIMENTS We compare deep learning based solutions with 3 baselines: • Hammerstain
(HPM) and Wiener (WPM) polynomial model (Eq. 1) with M = 2 and P = 3, trained using …

… , we propose a channel estimation and equalization scheme based on deep learning (DL-CE) …
4 shows the channel equalization performance of DLCE scheme and the other schemes …

… The deep learning-based algorithm is trained in three steps including the pretraining step,
training stage, and testing stage. During the first two steps, the model is developed offline …

… -based channel equalization and data detection, which utilize deep learning neural networks
(NNs) to learn … Power of deep learning for channel estimation and signal detection in OFDM …

… , we propose a novel blind equalization-aided deep learning (DL) … two modules: A blind
equalization module and a subsequent … step-sizes for the blind equalization algorithm, which are …

… equalizing the channel and carrier frequency offset (CFO) … a deep learning based channel
and CFO equalization technique for … Now, if we consider the output of the channel equalization …

… In this work, we propose a fully blind Deep Learning(DL)based signal receiver with two
sequential CV-NNs banking on the probability density fitting (PDF) criterion. The first one …