Simultaneous Optical Performance Monitoring and Modulation Format/Bit-Rate Identification Using Principal Component Analysis

Chromatic Dispersion Estimation for Optical PDM-QPSK Communication System without Symbol Rate Limitation

“…The differential operation enables the peak point of ACSPW distinctive especially for NRZ pulse shape [4]. The location of ACSPW peak value can be computed as [3]:…”

“…These methods not only need extra operation in the optical transmitter and receiver, but also enhance hardware complexity and DSP logical resources. In [3,4] CD estimation is proposed based on searching the pulse location in the autocorrelation of signal power waveform (ACSPW) without the known dataaid. But its estimation accuracy is limited by the transmission symbol rate [5].…”

Improved symbol rate identification method for on–off keying and advanced modulation format signals based on asynchronous delayed sampling

“…In such networks optical performance monitoring (OPM) is an important part of the management system with which functions such as channel identification, bandwidth resources allocation and signal health assessment can be realized. These functions often require OPM to identify the signal symbol rate in a MF transparent manner [6][7][8][9][10][11]. Thus it is essential to explore a universal and robust SRI method.…”

“…Thus it is essential to explore a universal and robust SRI method. By now several direct detection based cost-effective SRI methods have been proposed [7][8][9][10][11]. In [7] the first lobe width of ACF 1 is used to determine the bit rates of OOK signals, which is simple but very sensitive to chromatic dispersion (CD) and amplified spontaneous emission (ASE) noise impairments.…”

“…In [8] the periodicity of ACF 2 at large delays is used, which is more robust to the impairments, but may produce wrong results when applied to advanced MF signals with AAM, as will be shown later in this paper. Recently SRI methods based on asynchronous amplitude histograms (AAHs) and delaytap plots (DTPs) are proposed [9][10][11]. These methods use advanced pattern recognition algorithms, such as artificial neural networks, for SRI and MF recognition, and can be applied to both OOK and advanced MF signals with AAM.…”