Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this work paves the way to design a fast-learning scheme with parsimony as a future intelligent OPM enabler. 

Chromatic dispersion (CD) compensation in coherent fiber-optic systems represents a very significant DSP block in terms of power dissipation. Since spectrally efficient coherent systems are expected to find a wider deployment in systems shorter than long haul, it becomes relevant to investigate filter implementation aspects of CD compensation in the context of systems with low-to-moderate amounts of accumulated dispersion. The investigation we perform in this paper has an emphasis on implementation aspects such as power dissipation and area usage, it deals with both time-domain and frequency-domain CD compensations, and it considers both A/D-conversion quantization and fixed-point filter design aspects. To enable an accurate analysis on power dissipation and chip area, the evaluated filters are implemented in a 28-nm fully depleted silicon-on-insulator (FD-SOI) process technology. We show that an optimization of the filter response that takes pulse shaping into account can significantly reduce power dissipation and area usage of time-domain implementations, making them a viable alternative to frequency-domain implementations.

Monolithic integration of a metallic photonic crystal (mPhC) structure onto semiconductor infrared (IR) photodetectors can enhance the detector performances. In order to experimentally investigate the parameters involved in optimizing the transmission spectra of the mPhC structures matching the detector operating wavelength in mid- and long-wave IR (MWIR and LWIR) regimes, square thin gold (Au) hole arrays having periodicities of 4.0, 3.6, 2.4, and 1.8 \mu\hbox{m} with various fill factors were fabricated on Si or GaAs substrates in a wafer scale. The thicknesses of the Au films are 50, 100, and 200 nm, respectively. Through this systemic study, suitable mPhC structures were revealed that can be readily integrated onto our type-II InGaSb-based quantum dot MWIR and LWIR photodetectors.