| Paper ID | ASPS-2.4 |
| Paper Title |
CONVOLUTIONAL NEURAL NETWORK-AIDED BIT-FLIPPING FOR BELIEF PROPAGATION DECODING OF POLAR CODES |
| Authors |
Chieh-Fang Teng, Andrew Kuan-Shiuan Ho, Chen-Hsi Wu, Sin-Sheng Wong, An-Yeu (Andy) Wu, National Taiwan University, Taiwan |
| Session | ASPS-2: Algorithm/Architecture Co-design |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Applied Signal Processing Systems: Design & Synthesis [DIS-ARCH, DIS-LPWR] |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Known for their capacity-achieving abilities, polar codes have been selected as the control channel coding scheme for 5G communications. To satisfy high throughput and low latency, belief propagation (BP) is ideal as the decoding algorithm due to its nature of parallel processing. However, the error performance of BP is in general worse than that of enhanced successive cancellation (SC). Recently, bit-flipping (BF) mechanism is applied to BP decoding to lower the error rate. However, its trial-and-error process results in longer latency. In this work, we propose a convolutional neural network-aided bit-flipping (CNN-BF) mechanism to further enhance BP decoding. With carefully designed input data and model architecture, our proposed CNN-BF can achieve better error correction capability with less flipping attempts than prior works. It also achieves a lower block error rate (BLER) than SC list (SCL). |
