Cognitive digital receiver for burst mode phase modulated radio over fiber links

Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram

“…Nonetheless, MFI for fiber optic communications still remains underdeveloped. By now several different methods have been proposed for optical MFI, these methods can be classified into two main categories: (1) MFI methods based on digital coherent receivers [4][5][6]; (2) MFI methods based on direct detection (DD) [7][8][9]. The first kind of methods can take advantage of full field information of the optical signal, thus theoretically they can tolerate large link distortions, like the chromatic dispersion (CD) and polarization mode dispersion (PMD) by employing advanced digital signal processing techniques, and thus they can be applied in different kinds of transmission system, like dispersion compensated or non-compensated, short or long haul transmission systems.…”

“…In such networks modulation format identification (MFI) are essential techniques for optical performance monitoring, signal demodulation and network resource management [3][4][5][6][7][8]. The topic of MFI has been well explored for radio communications.…”

Cognitive Dynamic Optical Networks

“…Thus, in [10] we have proposed a cognitive digital receiver which, by means of clustering algorithms, is able to identify the incoming signal format (QPSK/8PSK/16QAM) without the need of receiving a prior control message, thus opening the door to the autonomous modification of the modulation format, as shown in Fig. 2(b).…”

Clustering algorithms for Stokes space modulation format recognition

“…Various approaches to optical MFR can be found in the literature, each of which has its limitations: (a) method based on k-means clustering of the received constellation diagram [3], which introduces a circular dependence between demodulation algorithms and the MFR subsystem; (b) direct-detection-based technique employing artificial neural networks trained with features extracted from optical eye histograms [4], which is not resistant to fiber impairments, such as chromatic dispersion (CD); (c) procedure based on the computation of signal cumulants [5], requiring prior polarization demultiplexing; (d) method based on the distribution of the amplitude histogram [6], which requires prior knowledge of optical signalto-noise ratio (OSNR); (e) classification using the received signal intensity histogram [7], not compatible with QAM modulation formats; (f) counting the number of clusters (groups of neighboring points) formed in Stokes space by using statistical signal processing methods [8], requiring high OSNR; and (g) a hybrid approach, combining (f) and (c) [9], which alleviates OSNR limitation. Out of these techniques, the Stokes-space-based MFR (Stokes MFR) subsystem is the most versatile, as it allows for modulation classification at an early stage in the DSP chain, before digital polarization demultiplexing [8], thus enabling subsequent DSP algorithms to be optimized for the detected modulation format.…”