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The ever-increasing demand for broadband Internet access has motivated the further development of the digital subscriber line to the G.fast standard in order to expand its operational band from 106 MHz to 212 MHz. Conventional far-end crosstalk (FEXT) based cancellers falter in the upstream transmission of this emerging G.fast system. In this paper, we propose a novel differential evolution algorithm (DEA) aided turbo channel estimation (CE) and multi-user detection (MUD) scheme for the G.fast upstream including the frequency band up to 212 MHz, which is capable of approaching the optimal Cramer-Rao lower bound of the channel estimate, whilst approaching the optimal maximum likelihood (ML) MUD's performance associated with perfect channel state information, and yet only imposing about 5% of its computational complexity. Explicitly, the turbo concept is exploited by iteratively exchanging information between the continuous value-based DEA assisted channel estimator and the discrete valuebased DEA MUD. Our extensive simulations show that 18 dB normalized mean square error gain is attained by the channel estimator and 10 dB signal-to-noise ratio gain can be achieved by the MUD upon exploiting this iteration gain. We also quantify the influence of the CE error, of the copper length and of the impulse noise. Our study demonstrates that the proposed DEA aided turbo CE and MUD

Digital subscriber lines (DSL) relying on twisted pairs and on home-passed fiber network, are still widely deployed in a large part of the world. However, its performance is severely restricted by the occurrence of impulsive noise. In this article, we present both the state-of-the-art and open research opportunities for impulsive noise mitigation in DSL. Its necessity is firstly discussed by briefly characterizing the impulsive noise and outlining the deleterious effects imposed on the system. Then, the state-ofthe-art is discussed by categorizing of the mitigation techniques into those at the transmitter as well as into parametric and nonparametric mitigation solutions at the receiver, followed by a detailed comparison in terms of mitigation efficiency, spectral efficiency, computational complexity and processing delay. Open research opportunities are discussed from the perspective of noise modeling for parametric mitigation, advanced mitigation design and machine-learning aided mitigation.

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