Potential Application of Machine Learning in Optical Communication Systems

In this work, we will examine practically encoding information in the state of polarization using M- POLSK. We employ polarization-shift keying (POLSK) to generate an expected pattern of changing states of polarization (SOPs). We generate a random sequence of voltages that represents a random sequence of bits, then we encode this sequence in the state of polarization. we apply the sequence of voltages -using the DAQ assistant- to the polarization modulator to embed this random sequence of bits in the state of polarization. At the far end, we collect the Stokes parameters data of the encoded file using polarization detector. Then, we reduce the dimensions of the collected data to one dimension using a Matlab code. In the final stage of the data recovery, we process the data and discriminate bits using both averaging and machine learning techniques to recover the random sequence that has been sent. Finally, by comparing the sent data set with the recovered data set. We can calculate the efficiency of data recovery process or the bit error rate of the received file. These POLSK symbols will be encoded in a fully polarized light. We will encode binary POLSK, 4-POLSK and 8-POLSK symbols in the SOPs of light in different runs. Also, we will propose the use of averaging to process the Stokes parameters that result from encoding binary POLSK and machine learning techniques to analyze the process of the Stokes parameters that belong to 4-POLSK and 8-POLSK. The state of polarization is presented by five variables. Three of them are the Stokes parameters S1, S2, and S3. The other two are the angles 2γ, 2β. The final dimension is going to be the horizontal angle in the Poincare Sphere representation. Then, we are going to predict the class (symbol) that belongs to each part of processed data. The length of the data points that represent each symbol is dominated by the sampling rate at the receiver. Symbol prediction will be accomplished using the classification learner’s techniques called K-Nearest Neighbor and Support Vector Machine. Basically, these techniques predict the class of the data based on the model built using guided data (row data and its class). Finally, we match the predicted class with the original symbol file to measure the accuracy of the prediction models. In other words, the number of symbols that has been predicted successfully

Stokes Vector Modulation of Optical Signals; Coherence, Noise, and Digital Signal Processing

Stokes vector modulation (SVM) is a method of encoding information onto an optical wave by controlling its amplitude and its state of polarization (SOP). SVM offers the potential to achieve the high spectral efficiency of multi-dimensional symbols using a power-efficient, direct-detection receiver. Combining the two independent degrees of freedom representing polarization with one representing amplitude, SVM symbols are defined in a 3-D space of Stokes vectors, where vector length represents the amplitude and altitude/azimuth angles represent the SOP. The recoverable information content is fundamentally limited by the noise on the received signal, which may include shot noise due to photon-counting, electrical circuit noise, amplified spontaneous emission due to optical amplifiers, and self-interference of low-coherence light sources. Some of these noise terms do not obey the usual approximation of additive white Gaussian noise, and some may not be isotropic in Stokes space. Taking these complexities into account, I will theoretically analyze and compare several recently-proposed SVM receiver designs under different conditions of source coherence and channel impairments. For the most promising options, I will design symbol constellations and receiver decision strategies suitable for maximal data throughput. Construction and operation of apparatus to experimentally verify bit-error performance up to at least 10 Gsym/s with different sources, constellations, fiber spans, and receivers will be an essential component of the work. Possible extensions may include simultaneous modulation of the degree of polarization, to create a 4-D symbol space. Further, I will develop and characterize a system based on a cubic constellation for 8-SVM, using an off-the-shelf integrated modulator driven with simple bias points and data waveforms. Symbol error rates (SER) and bit error rates BER) are measured up to 30 Gb/s, and analysis of the symbol errors reveals a significant effect of inter-symbol interference. Finally, I will theoretically and experimentally demonstrate a novel adaptation of independent component analysis (ICA) for compensation of both cross-polarization and inter-symbol interference in a direct-detection link using Stokes vector modulation (SVM). SVM systems suffer from multiple simultaneous impairments that can be difficult to resolve with conventional optical channel DSP techniques. The proposed method is based on a six-dimensional adaptation of ICA that simultaneously derotates the SVM constellation, corrects distortion of constellation shape, and mitigates inter-symbol interference (ISI) at high symbol rates. Experimental results at 7.5 Gb/s and 15Gb/s show that the newly-developed ICA-based equalizer achieves power penalties below ~1 dB, compared to the ideal theoretical bit-error rate (BER) curves. At 30-Gb/s, where ISI is more severe, ICA still enables polarization de-rotation and BE

Semiconductor Optical Amplifiers and mm-Wave Wireless Links for Converged Access Networks

Future access networks are converged optical-wireless networks, where fixed-line and wireless services share the same infrastructure. In this book, semiconductor optical amplifiers (SOA) and mm-wave wireless links are investigated, and their use in converged access networks is explored: SOAs compensate losses in the network, and thereby extend the network reach. Millimeter-wave wireless links substitute fiber links when cabling is not economical

Symmetry & nonlinear compensation in fiber-optic transmissions

This thesis presents methods and practical implementations for compensating or suppressing signal distortions induced by fiber nonlinearity in long-distance transmissions. Our methods take advantage of the availability and already wide deployment of dispersion-compensating fibers with various choices of dispersions and dispersion slopes. The basic principle behind the methods is to choose suitable fibers and to arrange them properly into transmission lines manifesting scaled symmetries. Based on the nonlinear Schrodinger equation which describes the nonlinear and dispersive signal propagation in optical fibers, we have shown analytically that a scaled symmetry renders the nonlinear signal distortion by the first part of a transmission line to be largely undone by the second part, when an optical phase conjugator is installed in the middle of the line. Without a phase conjugator, the most detrimental nonlinear interactions among pulses within a wavelength channel may be significantly suppressed in a scaled symmetric line. We have identified two types of scaled symmetries: mirror and translation. Although mirror-symmetric systems have been discussed by other authors before, our own proposals and designs using high-dispersion fibers in conjunction with distributive Raman or erbium-doped amplification could make practical transmission systems manifesting nearly perfect mirror symmetries in the scaled sense and hence excellent nonlinear compensations. Firstly noted and investigated thoroughly by us, the concept of scaled translation symmetries in transmission lines may well spur the adoption of nonlinear compensation methods in practical transmission systems, since distributive amplifiers are no longer necessary for translation symmetries. To support our mathematical analyses, extensive computer simulations have been carried out to validate the effectiveness of our proposed systems, most of which assume practical system setups and parameters and could therefore serve as paradigms for real system designs