Joint Impulsive Noise Estimation and Data Detection Conceived for LDPC-Coded DMT-Based DSL Systems

Tong Bai

doi:10.1109/ACCESS.2017.2766603

2017-01-01

DOI：10.1109/ACCESS.2017.2766603

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摘要

En 中文

Impulsive noise is one of the most challenging issues in digital subscriber lines (DSL). In order to mitigate the deleterious effects of impulsive noise, the conventional automatic repeat request invokes cyclic redundancy checking (CRC) in order to estimate the existence of impulsive noise and then triggers retransmission, which degrades the spectral efficiency attained. More straightforward techniques of mitigating impulsive noise, such as blanking and clipping, require specific design, which increases the implementation complexity. Against the background, we propose a novel two-stage joint impulsive noise estimation and data detection scheme conceived for low-density parity-check (LDPC) coded discrete multitone (DMT)-based DSL systems. More explicitly, first of all, we propose a semi-blind estimation method, which is capable of estimating the arrival of impulsive noise without using CRC and additionally evaluating the power of impulsive noise with an adequate accuracy. Second, in order to improve the accuracy of impulsive noise estimation in more advanced LDPC-coded DMT-based DSL systems, we propose a decision-directed method for the second stage of channel decoding and data detection with the aid of extrinsic information transfer (EXIT) charts. Our proposed two-stage scheme is capable of approaching the performance of the idealistic scenario of perfectly knowing both the arrival time and the instantaneous power of impulsive noise. Moreover, we analyze the mean square error of the proposed schemes in order to quantify the estimation accuracy and to reduce the estimation complexity. Our simulation results demonstrate that our proposed scheme is capable of achieving a near-capacity performance to using our LDPC coded DMT-based DSL system in the presence of impulsive noise.

Keyword:

Digital subscriber line

joint impulsive noise estimation and data detection

near-capacity performance