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Power Line Communication (PLC) has received much attention due to the wide connectivity and availability of power lines. Effective PLC must overcome the harsh and noisy environments inherent in PLC channels. Noise in power lines is modeled as a cyclostationary Gaussian process. In order to achieve reliable communication using power lines, effective measures including error control techniques need to be taken against this particular noise. Low-Density Parity-Check (LDPC) codes have excellent performance in power lines. This paper presents two new iterative algorithms for noise estimation on power lines based on Higher-order statistics and the Maximum-Likelihood (ML) estimation principle, respectively.
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