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A novel all-optical stealth and secured transmission is proposed and demonstrated. Spectral replicas of the covert signal are carried by multiple tones of a gain switched optical frequency comb, optically coded with spectral phase mask, and concealed below EDFA’s noise. The secured signal’s spectrum is spread far beyond the bandwidth of a coherent receiver, thus forcing real time all-optical processing. An unauthorized user, who does not possess knowledge on the phase mask, can only obtain a noisy and distorted signal, that cannot be improved by post-processing. On the other hand, the authorized user decodes the signal using an inverse spectral phase mask and achieves a substantial optical processing gain via multi-homodyne coherent detection. A transmission of 20 Gbps under negative −7.5 dB OSNR is demonstrated here, yielding error-free detection by the eligible user.

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Optical communication systems often include nonlinear components, such as Mach-Zehnder modulators (MZMs). The components' nonlinearity may have a deleterious effect on the system performance, especially when the memory effect is included. One of the most common methods to mitigate the resultant distortion effects is digital predistortion (DPD) based on the Volterra polynomial model. Typically, the DPD coefficients are extracted using the least squares approach. However, naive implementation of the Volterra polynomial model usually introduces significant complexity due to the large number of model coefficients. Here, we propose the use of orthogonal polynomial basis functions for efficient DPD implementation. The orthogonal basis enables the estimation of each coefficient separately, which provides a significant computational gain. Furthermore, the amount of dominant terms in the orthogonal basis representation is significantly lower, which leads to dramatic relaxation in the DPD implementation. In addition to the analytical modeling approach, a nonparametric method is developed by applying eigenvalue decomposition on the correlation matrix, which is required for the case of dependent random variables, or for the case of unknown probability distribution functions. MZM-based lab experiments and simulations were performed, which indicated a potential saving of 50%-80% in the amount of useful dimensions.

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