Optical Wireless Performance Monitoring Using Asynchronous Amplitude Histograms

Abstract

Optical performance monitoring (OPM) aims to estimate the amount of distortion in optical networks. Its importance relies on building robust and efficient networks with self and dynamic diagnosis. In this work, channel impairment monitoring is investigated for optical wireless communication. The monitoring is achieved using a support vector machine (SVM) regressor. A cost-effective and straightforward acquisition system is used to build the training features, which are asynchronous amplitude histograms. Three different parameters related to three common channel impairments are monitored using these features: optical signal-to-noise ratio (OSNR), visibility range, and ξ parameter related to pointing error. First, each parameter is monitored when there is only one isolated channel impairment. Then, each parameter is monitored when two and three jointly channel impairments occur. The results showed that using this low complex machine learning (ML) technique, the achieved prediction accuracy was very high (>0.98) for most channel conditions except for the monitoring of the OSNR parameter, where the prediction accuracy dropped to 0.86 under harsh channel conditions. Moreover, the superiority of ML-based techniques is compared with non-ML-based techniques for the OSNR parameter monitoring. The results indicated that the ML-based technique achieved high prediction accuracy than the non-ML-based technique, especially for harsh channel conditions.

Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems

“…The amount of turbulence impairment is defined by the index of refraction structure parameter, C 2 2 , with the following values: 10 −17 m −2/3 and 10 −16 m −2/3 for weak turbulence, 10 −15 m −2/3 for moderate turbulence, and 10 −14 m −2/3 , and 10 −13 m −2/3 for strong turbulence. To emulate the turbulence in the software, the C 2 2 model, which is defined in [22], is used.…”

“…In [21], the OAM modes are exploited for monitoring the visibility range under dusty weather conditions. The work in [22] used the support vector machine (SVM) technique to monitor the turbulence and OSNR parameters in addition to the visibility range under fog weather conditions. Table 1 summarizes the literature work on monitoring the performance of IM/DD optical fiber and FSO systems.…”

Survey on Applications of Machine Learning in Low-Cost Non-Coherent Optical Systems: Potentials, Challenges, and Perspective

“…The above papers propose solutions for DD-OPM in optical-fiber networks. This can be extended to free-space optics (FSO) as [31,73] do to monitor channel impairments. Both papers propose SVM-based solutions to monitor impairments such as pointing error, OSNR, visibility range, amplified spontaneous emission (ASE) noise, and turbulence.…”

An OPM scheme based on ADTP and a combination of CNN and SVM

“…Synchronous sampling technique, commonly used in the formation of eye maps and constellation maps, has been studied in the literature [9][10][11][12], but requires clock recovery and is more complex. Asynchronous sampling techniques are often used to form asynchronous amplitude histograms (AAH) [13], asynchronous delay tapped histograms (ADTP) [14,15], etc. They do not require clock synchronization and have simple mechanisms.…”