2022
DOI: 10.3390/photonics9010030
|View full text |Cite
|
Sign up to set email alerts
|

Machine Learning Applications for Short Reach Optical Communication

Abstract: With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
8
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 28 publications
(8 citation statements)
references
References 151 publications
0
8
0
Order By: Relevance
“…At the same time, with the continuous improvement of the technical level, the all-optical network is becoming an important information transmission carrier of the global Internet backbone network by virtue of its own transmission characteristics. It is of broad interest [26,27], and the International Internet Engineering Task Force (IETF) also defines the structure accordingly. e structure of the all-optical network based on SDN is shown in.…”
Section: Proposed Frameworkmentioning
confidence: 99%
“…At the same time, with the continuous improvement of the technical level, the all-optical network is becoming an important information transmission carrier of the global Internet backbone network by virtue of its own transmission characteristics. It is of broad interest [26,27], and the International Internet Engineering Task Force (IETF) also defines the structure accordingly. e structure of the all-optical network based on SDN is shown in.…”
Section: Proposed Frameworkmentioning
confidence: 99%
“…Machine learning (ML) approaches have gained increased attention due to the demand for enhanced linearization to achieve superior outcomes [33,35,38,40,[42][43][44]. This growing interest is underscored by recent research conducted by Pereira et al, who explored ML algorithms for linearizing electrically amplified Radio over Fiber (RoF) systems [45].…”
Section: Introductionmentioning
confidence: 99%
“…LULC classification using optical satellite imagery has recently been successfully performed using a machine learning approach, which improves accuracy compared to more conventional classification techniques (Talukdar et al, 2020). Generally, there are four types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforcement learning (Xie et al, 2022), nonetheless, supervised and unsupervised learning are the most popular. Supervised learning techniques include support vector machines (Cortes et al, 1995), random forests (Breiman, 2001), classification and regression trees (Breiman et al, 1984), artificial neural networks (Tim Hill et al, 1994), while other techniques unsupervised learning includes fuzzy c-means algorithms (Saman et al, 2014), K-means algorithms (Abbas et al, 2016).…”
Section: Introductionmentioning
confidence: 99%