Reservoir Computing Based Equalization for Radio over Fiber System

Li, Jingwei; Lyu, Yibo; Li, Xu; Wang, Tianxiang; Dong, Xiaowen

doi:10.23919/icact51234.2021.9370401

Cited by 6 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the aforementioned advantages, RC has attracted more and more attentions in research community. It has be utilized in signal equalization [67,[75][76][77][78][79][80][81], speech recognition [82,83], time-series prediction or classification [82,[84][85][86][87][88][89][90][91], and de-noising in temporal sequence [92,93].…”

Section: Optical Reservoir Computingmentioning

confidence: 99%

The challenges of modern computing and new opportunities for optics

Zhang

et al. 2021

PhotoniX

Self Cite

View full text Add to dashboard Cite

In recent years, the explosive development of artificial intelligence implementing by artificial neural networks (ANNs) creates inconceivable demands for computing hardware. However, conventional computing hardware based on electronic transistor and von Neumann architecture cannot satisfy such an inconceivable demand due to the unsustainability of Moore’s Law and the failure of Dennard’s scaling rules. Fortunately, analog optical computing offers an alternative way to release unprecedented computational capability to accelerate varies computing drained tasks. In this article, the challenges of the modern computing technologies and potential solutions are briefly explained in Chapter 1. In Chapter 2, the latest research progresses of analog optical computing are separated into three directions: vector/matrix manipulation, reservoir computing and photonic Ising machine. Each direction has been explicitly summarized and discussed. The last chapter explains the prospects and the new challenges of analog optical computing.

show abstract

Section: Optical Reservoir Computingmentioning

confidence: 99%

The challenges of modern computing and new opportunities for optics

Zhang

et al. 2021

PhotoniX

Self Cite

View full text Add to dashboard Cite

show abstract

“…In these studies, the neural network has shown better performance than traditional equalizers, and hence, it becomes a very useful and popular technology for short-reach applications [113]. Among various proposed neural network architectures, the feedforward neural network (FFNN) [103,104,114], reservoir computing (RC) [82,106,111,[115][116][117][118], and RNN [54,105] allow time-dependent processes (e.g., ISI) to be captured and learned effectively. Therefore, they are good candidates for the linear and nonlinear equalization of memory communication systems such as IM/DD systems [82].…”

Section: Machine-learning-based Equalizer For Short-reach Optical Com...mentioning

confidence: 99%

“…In [117], RC has also been used to achieve channel equalization in a WDM-RoF system. In WDM-RoF systems, due to the linear fiber distortion caused by dispersion, the fiber nonlinearity due to the Kerr effect, and the inelastic scattering of signals, the adjacent channel power ratio (ACPR) performance typically becomes worse after transmission.…”

Section: Reservoir Computing Based Equalizermentioning

confidence: 99%

Machine Learning Applications for Short Reach Optical Communication

et al. 2022

View full text Add to dashboard Cite

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 is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

show abstract

“…These studies highlight that neural networks outperform traditional equalizers, positioning them as a sought-after technology with significant value in short-reach applications [8]. Various architectural designs have been proposed for this purpose, including feedforward neural networks (FFNNs) [9][10][11], reservoir computing (RC) [12][13][14][15][16][17][18], and recurrent neural networks (RNNs) [19,20]. These architectures can be effectively utilized for both linear and nonlinear equalization tasks.…”

Section: Introductionmentioning

confidence: 99%

Symbol error rate minimization using deep learning approaches for short-reach optical communication networks

Iqbal,

Ghafoor,

Ahmad

et al. 2024

Front. Phys.

View full text Add to dashboard Cite

Short-reach optical communication networks have various applications in areas where high-speed connectivity is needed, for example, inter- and intra-data center links, optical access networks, and indoor and in-building communication systems. Machine learning (ML) approaches provide a key solution for numerous challenging situations due to their robust decision-making, problem-solving, and pattern-recognition abilities. In this work, our focus is on utilizing deep learning models to minimize symbol error rates in short-reach optical communication setups. Various channel impairments, such as nonlinearity, chromatic dispersion (CD), and attenuation, are accurately modeled. Initially, we address the challenge of modeling a nonlinear channel. Consequently, we harness a deep learning model called autoencoders (AEs) to facilitate communication over nonlinear channels. Furthermore, we investigate how the inclusion of a nonlinear channel within an autoencoder influences the received constellation as the optical fiber length increases. Another facet of our work involves the deployment of a deep neural network-based receiver utilizing a channel influenced by chromatic dispersion. By gradually extending the optical length, we explore its impact on the received constellation and, consequently, the symbol error rate. Finally, we incorporate the split-step Fourier method (SSFM) to emulate the combined effects of nonlinearities, chromatic dispersion, and attenuation in the optical channel. This is accomplished through a neural network-based receiver. The outcome of this work is an evaluation and reduction of the symbol error rate as the length of the optical fiber is varied.

show abstract

Reservoir Computing Based Equalization for Radio over Fiber System

Cited by 6 publications

References 18 publications

The challenges of modern computing and new opportunities for optics

The challenges of modern computing and new opportunities for optics

Machine Learning Applications for Short Reach Optical Communication

Symbol error rate minimization using deep learning approaches for short-reach optical communication networks

Contact Info

Product

Resources

About