Name
Abstract | ||
|---|---|---|
Designing channel codes under low latency constraints is one of the most demanding requirements in 5G standards. However, sharp characterizations of the performances of traditional codes are only available in the large block lengths limit. Code designs are guided by those asymptotic analyses and require large block lengths and long latency to achieve the desired error rate. Furthermore, when the codes designed for one channel (e.g. Additive White Gaussian Noise (AWGN) channel) are used for another (e.g. non-AWGN channels), heuristics are necessary to achieve any non trivial performance - thereby severely lacking in robustness as well as adaptivity. Obtained by jointly designing recurrent neural network (RNN) based encoder and decoder, we propose an end-to-end learned neural code which outperforms canonical convolutional code under block settings. With this gained experience of designing a novel neural block code, we propose a new class of codes under low latency constraint - Low-latency Efficient Adaptive Robust Neural (LEARN) codes, which outperform the state-of-the-art low latency codes as well as exhibit robustness and adaptivity properties. LEARN codes show the potential of designing new versatile and universal codes for future communications via tools of modern deep learning coupled with communication engineering insights. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICC.2019.8761286 | ICC 2019 - 2019 IEEE International Conference on Communications (ICC) |
Keywords | Field | DocType |
LEARN codes,recurrent Neural networks,Additive White Gaussian Noise,nonAWGN channels,end-to-end learned neural code,canonical convolutional code,channel codes,deep learning,neural block code,5G standards,low-latency efficient adaptive robust neural codes | Convolutional code,Computer science,Recurrent neural network,Communication channel,Robustness (computer science),Encoder,Artificial intelligence,Deep learning,Latency (engineering),Additive white Gaussian noise,Computer engineering | Journal |
Volume | ISSN | ISBN |
abs/1811.12707 | 1550-3607 | 978-1-5386-8089-6 |
Citations | PageRank | References |
1 | 0.36 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihan Jiang | 1 | 12 | 3.00 |
| Kim, Hyeji | 2 | 23 | 6.94 |
| Himanshu Asnani | 3 | 117 | 15.39 |
| Sreeram Kannan | 4 | 120 | 21.76 |
| Sewoong Oh | 5 | 843 | 60.50 |
| Viswanath, P. | 6 | 1330 | 179.87 |