Name
Abstract | ||
|---|---|---|
In recent years, many works have been focusing on applying machine learning techniques to assist with communication system design. Instead of replacing the functional blocks of communication systems with neural networks, a hybrid manner of ViterbiNet symbol detection was proposed to combine the advantages of Viterbi algorithm and neural networks, which achieves guaranteed performance with reasonable complexity. However, this block-based design not only degrades the system performance but also increases hardware complexity. In this work, we propose a ViterbiNet receiver for joint equalization and channel decoding, which simultaneously considers both the code structure and channel effects, thus achieving global optimum with 3 dB gain. Furthermore, a dedicated neural network model is proposed to avoid the need for perfect channel state information (CSI). It is shown to be more robust under CSI uncertainty with 1.7 dB gain. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MLSP49062.2020.9231847 | 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | ISSN |
Viterbi Algorithm,Convolutional Code,Symbol Detection,Channel Decoding,Neural Network | Conference | 1551-2541 |
ISBN | Citations | PageRank |
978-1-7281-6663-6 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han-Mo Ou | 1 | 0 | 0.34 |
| Chieh-Fang Teng | 2 | 6 | 4.58 |
| Wen-Chiao Tsai | 3 | 0 | 0.34 |
| anyeu andy wu | 4 | 76 | 25.73 |