Abstract: A method of assessing a signal to identify particular signal characteristics comprises application of machine learning to multi-dimensional histograms derived from multi-tap sampling of the signal. The signal is sampled from at least two tap points to retrieve a sample set, and the at least two tap points are adapted to retrieve distinct samples from the signal, such as time spaced samples or spectrally distinct samples. Multiple sample sets are retrieved from the signal over time. The at least two dimensional histogram is built from the joint probability distribution of the plurality of sample sets. A machine learning algorithm then processes the multi-dimensional histogram, and is trained to predict a value of at least one characteristic of the signal.

Abstract: Monitoring an optical signal comprises sampling the optical signal from two or more distinct tap points to retrieve a sample set. Multiple such sample sets are obtained over time. A joint probability distribution or phase portrait of the sample sets is assessed for indications of optical signal quality. The tap distinction can be polarization, for example to determine OSNR, or frequency. The tap distinction can be a time delay, which can enable diagnostic differentiation between multiple impairments, such as OSNR, dispersion, PMD, jitter, Q, and the like. Machine learning algorithms are particularly suitable for such diagnosis, particularly when provided a two dimensional histogram of sample density in the phase portrait.

Abstract: A method of assessing a signal to identify particular signal characteristics comprises application of machine learning to multi-dimensional histograms derived from multi-tap sampling of the signal. The signal is sampled from at least two tap points to retrieve a sample set, and the at least two tap points are adapted to retrieve distinct samples from the signal, such as time spaced samples or spectrally distinct samples. Multiple sample sets are retrieved from the signal over time. The at least two dimensional histogram is built from the joint probability distribution of the plurality of sample sets. A machine learning algorithm then processes the multi-dimensional histogram, and is trained to predict a value of at least one characteristic of the signal.

Abstract: Monitoring an optical signal comprises sampling the optical signal from two or more distinct tap points to retrieve a sample set. Multiple such sample sets are obtained over time. A joint probability distribution or phase portrait of the sample sets is assessed for indications of optical signal quality. The tap distinction can be polarisation, for example to determine OSNR, or frequency. The tap distinction can be a time delay, which can enable diagnostic differentiation between multiple impairments, such as OSNR, dispersion, PMD, jitter, Q, and the like. Machine learning algorithms are particularly suitable for such diagnosis, particularly when provided a two dimensional histogram of sample density in the phase portrait.

Abstract: A method and apparatus for inducing a localised optical transmission loss in an optical fiber cable is described, which is particularly well suited for use with optical time-domain reflectometry (OTDR) equipment to aid in locating faults in an optical fiber cable. A small portion of the optical fiber cable is cooled, such as by immersion in a cryogenic liquid like liquid nitrogen, and fibres in the cooled cable portion exhibit localised optical transmission loss which can be detected as a point loss (26) with the OTDR trace (24). The technique of inducing the point loss is non-invasive, and the localised transmission loss disappears when the cable portion returns to the ambient temperature, without damage to the cable.