Artificial intelligence (AI) has become a promising solution for meeting the stringent
performance requirements on wireless physical layer in sixth-generation (6G) …

This paper provides a comprehensive survey on recent advances in deep learning (DL)
techniques for the channel coding problems. Inspired by the recent successes of DL in a …

With the rapid development of smart grids, the deployment number of transmission lines has
significantly increased, posing significant challenges to the detection and maintenance of …

In this paper, we propose low complexity joint-way compression algorithms with Tensor-
Ring (TR) decomposition and weight sharing to further lower the storage and computational …

Orthogonal frequency-division multiplexing (OFDM) is regarded as one of the key
technologies in wireless communications, particularly in the integration of space and ground …

Channel decoding algorithms based on model-driven deep learning, also known as channel
neural decoding algorithms, have received a lot of attention in recent years. However, the …

Ultra‐reliable low‐latency communications, URLLC, are designed for applications such as
self‐driving cars and telesurgery requiring a response in milliseconds and are very sensitive …

The residual belief propagation (RBP) algorithm, which is the most classic informed dynamic
scheduling strategy, achieves outstanding performance in error correction and can …

The trade-off between decoding performance and hardware costs has been a long-standing
challenge in Low-Density Parity Check (LDPC) decoding. Based on model-driven …

For some linear codes, neural networks have been found to be a way to enhance traditional
belief propagation (BP) decoder. When deploying such methods in practice, quantization …