Table 6 Spectral cloning in optics

Instilling cyclicity: The utility of the concept of cyclostationarity in signal processing is further broadened by the fact that some data processing systems can benefit from artificially introducing cyclostationarity into data that exhibits no cyclicity whatsoever. An example of this is systems for amplifying and detecting fast transients that occur, for example, in optical data processing, as briefly discussed below.

Minimum-noise optical amplification is “the ability to amplify optical signals with minimum excess noise [and] is of critical interest in a wide variety of photonic-systems applications that span optical communications, imaging, sensing, metrology, and quantum state processing, to name a few” [Stojan Radic, Advances in Optics and Photonics, Vol. 5, Issue 3, pp. 318–384 (2013) https://doi.org/10.1364/AOP.5.000318]. Spectral cloning is a relatively new approach to phase-sensitive “subnoise” or “noiseless” optical amplification, which—to quote Radic—is “opening diverse applications that rely on low-noise optical operation: wavelength conversion, phase regeneration, and signal spectral replication”. Spectral cloning is achieved with nonlinear optical parametric amplification, but the underlying principle is that of replicating the spectrum of a signal in multiple distinct bands and then frequency-shifting all such bands to a common band where the replicas are added. Because the spectral replication is able to be performed prior to the processing stage that is the dominant source of receiver noise, the creation and addition of the replicas results in coherent processing gain against the low noise (idealistically called “noiseless” or “subnoise” amplification), coming out of the cloning stage, and produces a pre-amplified signal that can then be further amplified or processed by subsequent noisier stages. This instillation and exploitation of spectral redundancy is identical to instillation and exploitation of cyclostationarity. This approach to performing “subnoise” detection of fast random events is described in [V. Ataie, D. Esman, B. P.-P. Kuo, N. Alic, and S. Radic, “Subnoise detection of a fast random event”, Science, sciencemag.org, 11 Dec 2015, Vol. 350 Issue 6266].