Machine learning methods for optical communications
<p>Radio over Fiber (RoF) technology has been realized in different forms ranging from analog to more complex forms [1-6]. The enhancement of capacity and wireless coverage has posed significant challenges to the existing optical and wireless access networks. Machine Learning (ML) methods have...
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Trends in Computer Science and Information Technology - Peertechz Publications,
2020-09-17.
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| 001 | peertech__10_17352_tcsit_000023 | ||
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| 100 | 1 | 0 | |a Machine learning methods for optical communications |e author |
| 245 | 0 | 0 | |a Machine learning methods for optical communications |
| 260 | |b Trends in Computer Science and Information Technology - Peertechz Publications, |c 2020-09-17. | ||
| 520 | |a <p>Radio over Fiber (RoF) technology has been realized in different forms ranging from analog to more complex forms [1-6]. The enhancement of capacity and wireless coverage has posed significant challenges to the existing optical and wireless access networks. Machine Learning (ML) methods have given a new direction to meet the ever-increasing challenges in fiber-optic communications. Since, ML-based methods are well known to perform exceptionally well in scenarios where it is too difficult to explicitly describe the underlying physics and mathematics of the problem and the numerical procedures available require significant computational resources/time. </p><p><br></p> | ||
| 540 | |a Copyright © Machine learning methods for optical communications et al. | ||
| 546 | |a en | ||
| 655 | 7 | |a Mini Review |2 local | |
| 856 | 4 | 1 | |u https://doi.org/10.17352/tcsit.000023 |z Connect to this object online. |