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20 May 2013 Blind signal processing algorithms under DC biased Gaussian noise
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Proceedings Volume 8879, Nano-Bio Sensing, Imaging, and Spectroscopy; 88790G (2013)
Event: Nano-Bio Sensing, Imaging and Spectroscopy, 2013, Jeju, Korea, Republic of
Distortions caused by the DC-biased laser input can be modeled as DC biased Gaussian noise and removing DC bias is important in the demodulation process of the electrical signal in most optical communications. In this paper, a new performance criterion and a related algorithm for unsupervised equalization are proposed for communication systems in the environment of channel distortions and DC biased Gaussian noise. The proposed criterion utilizes the Euclidean distance between the Dirac-delta function located at zero on the error axis and a probability density function of biased constant modulus errors, where constant modulus error is defined by the difference between the system out and a constant modulus calculated from the transmitted symbol points. From the results obtained from the simulation under channel models with fading and DC bias noise abruptly added to background Gaussian noise, the proposed algorithm converges rapidly even after the interruption of DC bias proving that the proposed criterion can be effectively applied to optical communication systems corrupted by channel distortions and DC bias noise.
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Namyong Kim, Hyung-Gi Byun, and Jeong-Ok Lim "Blind signal processing algorithms under DC biased Gaussian noise", Proc. SPIE 8879, Nano-Bio Sensing, Imaging, and Spectroscopy, 88790G (20 May 2013);

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