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 …

This letter proposes a novel approach for designing non-surjective (NS) finite alphabet
iterative decoders (FAIDs) for quasi-cyclic low-density parity-check (LDPC) codes, especially …

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 …

With the application of graph neural network (GNN) in the communication physical layer,
GNN-based channel decoding algorithms have become a research hotspot. Compared with …

Channel neural decoding is very promising as it outperforms the traditional channel
decoding algorithms. Unfortunately, it still faces the disadvantage of high computational …

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 …

The performance of model-driven channel neural decoding has surpassed that of traditional
channel decoding algorithms, but at a higher complexity, making it difficult to implement on …

To further reduce the complexity of the channel state information (CSI) based on deep
learning for massive multiple inputs and multiple outputs (MIMO) system, we propose a …

We proposed a low complexity OFDM-Guided deep joint source-channel coding (DJSCC)
model for wireless multipath fading channels using fine-tuned tensor train (TT) …