4 accepted papers
Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance. However, the state-of-the-art neural channel decoders cannot achieve high decoding performance and low complexity simultaneously. To overcome this challenge, in this paper we propose d…
Tensors, as the multidimensional generalization of matrices, are naturally suited for representing and processing high-dimensional data. To date, tensors have been widely adopted in various data-intensive applications, such as machine learning and big data analysis. However, due to the inherent larg…
Deep neural networks (DNNs) have emerged as an important artificial intelligence technique. However, the computation-intensive and storage-intensive DNNs pose severe challenges on efficient execution over the underlying hardware platform. In this paper we propose to impose Toeplitz structure on DNN…
Error-correcting codes are very important in modern communication systems. In this paper, we investigate efficient reduced-complexity deep neural network (DNN)-aided channel decoders. Specifically, we leverage DNN training to obtain individual scaling parameters for normalized min-sum algorithms, th…