Name
Abstract | ||
|---|---|---|
Industrial IoT (IIoT) in conjunction with Ultra-Reliable Low-Latency Communications (URLLC) often struggles with data-rich, information-poor contexts. Blind Source Separation (BSS) is one of the key technologies which can obtain the desired high-value information from all of the observed raw sensory data. As shown by recent studies, BSS can be both fast enough for low-latency requirements and sufficiently accurate to be a reliable method in large IoT deployments. Nonetheless, the trade-off between signal context usage and data recovery accuracy often affects the separation quality of BSS. In this paper, we propose for the first time a novel dual path convolutional neural network model, called Y-Net, for high accuracy BSS. Specifically, the separation quality is improved by the parallel perception and joint combination of both high-and low-level features of input signals, which we demonstrated through extensive numerical evaluations. In particular, Y-Net improves the Source-to-Distortion Ratio by 2.70% to 35.32% for different target signals, while the model size is only slightly increased, compared to other current solutions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/GCWkshps50303.2020.9367428 | 2020 IEEE Globecom Workshops (GC Wkshps |
Keywords | DocType | ISSN |
blind source separation,data analysis,algorithm optimization,neural network,industry 4.0,URLLC | Conference | 2166-0069 |
ISBN | Citations | PageRank |
978-1-7281-7308-5 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huanzhuo Wu | 1 | 1 | 1.42 |
| Jia He | 2 | 0 | 0.34 |
| Máté Tömösközi | 3 | 0 | 0.34 |
| Frank H. P. Fitzek | 4 | 706 | 123.89 |