Differential Evolution Algorithm Aided Turbo Channel Estimation and Multi-User Detection for G.Fast Systems in the Presence of FEXT

摘要

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 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 a 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 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 value-based 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, the copper length, and the impulse noise. This paper demonstrates that the proposed DEA-aided turbo CE and MUD scheme is capable of offering near-capacity performance at an affordable complexity for the emerging G.fast systems.