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The concept of evaluating the filter impulse response of the digital equalizer to estimate all deterministic linear channel distortions such as residual chromatic dispersion (CD), polarization mode dispersion (PMD), and PDL has been proved in theory and by extensive simulation. The practicality of robust and precise estimation has been demonstrated over a wide range of combined distortions. Preceding investigations based on measured and simulated data are in agreement. Although the theory of the linear optical channel model provides comprehensive access to all desired parameters, many details of the practical implementation of estimation algorithms must be considered to obtain a precise and robust estimation. Estimates of statistical noise distortions have not been demonstrated. Evaluating the mean value and variance of the signal z(t) to estimate SNR is simple and valuable for monitoring signal quality prior to the decision. However, as the MMSE solution jointly mitigates noise enhancement and inter-symbol interference, the variance of z(t) is also influenced by deterministic signal components. This is particularly true for deterministic nonlinear effects such as SPM that are interpreted as noise by the linear equalizer. In contrast to ZF solutions applicable in OFDM systems with training, blind estimation based on MMSE does not allow reliable identification of noise sources.
This paper deals with a simulation platform of optical transmission systems in MATLAB Simulink. A detailed mathematical methodology to accurately model parts of the optical transmission system is presented. The simulation platform is suitable for single-channel optical transmission systems. The selected approach takes into account noises that occur in parts of the optical transmission system.
This paper presents a theoretical and numerical analysis of a transmission chain based on an Orbital Angular Momentum (OAM) technique for an ideal channel. To generate an OAM beam we define a theoretical encoder (vortex generator) similar to the Spiral Phase Pattern (SPP) which converts a flat Gaussian phase front into a helix. Several OAM channels are multiplexed, modulated with QPSK streams, decoded, and the overall performance is measured in terms of SNR and BER.
The gain of advanced optical communication systems is reduced by the noise of the optical source. Coherent optical communication systems are particularly sensitive to noise from transmitters and local lasers. Optical space communications are emerging; this paper introduces laser satellite communication systems and covers analysis, optimization, design and system-level development of signal transfer between satellites.
Different frequencies of light travel at different speeds in fiber, and the concept of group velocity leads to pulse spreading due to dispersion. This paper describes how computer simulation and animation can provide a visual means to simplify these concepts and make them easier to understand.
High-speed, ultra-high-capacity optical communications are essential for backbone networks. As bit rates increase (e.g., 40 Gb/s to 100 Gb/s), accurate modeling of proposed modulation techniques is crucial to avoid costly experiments. MATLAB Simulink is a universal modeling tool for such systems. This paper describes modeling techniques for advanced photonic transmission systems and provides Simulink models and simulated transmission performance examples.
Contrary to people with normal vision who rely more on internal features of face images for recognizing familiar faces, individuals with central vision loss show a higher dependence on using external features of face images.
In today\\\'s rapidly advancing era of automation, robotics control systems are
Learn MoreThe financial sector is witnessing a technological revolution with the rise of Large Lang
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In today\\\'s rapidly advancing era of automation, robotics control systems are evolving to meet the demand for smarter, faster, and more reliable performance. Among the many innovations driving this transformation is the use of MCP (Model-based Control Paradigms)
The financial sector is witnessing a technological revolution with the rise of Large Language Models (LLMs). Traditionally used for text analysis, LLMs are now being integrated with powerful platforms like MATLAB to develop financial forecasting models