Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems

New normalized constant modulus algorithms with relaxation

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Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

Cdma blind channel equalization: a weighted subsface a proach

This paper considers the problem of blind demodulation of multiuser information symbols in a direct-sequence code-division multiple access (DS-CDMA) environment. Channel estimation and symbol detection in the presence of both multiple access interference (MAI) and intersymbol interference (ISI) is carried out with second order statistics methods from the received data. This problem is similar to direction of arrival (DOA) estimation, where many solutions like the MUSIC algorithm orPeer ReviewedPostprint (published version