Asynchronous impulsive noise mitigation based on subspace support estimation for PLC systems

Abstract

An asynchronous impulsive noise mitigation algorithm is proposed for power line communication (PLC) systems. Multiple techniques have been proposed in the literature in order to mitigate asynchronous impulsive noise to enable PLC, e.g., in smart grid applications. Most of the schemes proposed so far are based on non-linear methods, such as clipping and nulling. Even though these non-linear schemes reduce bit error rate (BER) by a significant amount, it will be shown in this paper that they can be easily outperformed by the proposed linear method.The performance improvement of the proposed algorithm comes from the fact that the identification of true locations of impulsive noise samples is very accurate, so that the reduction of the impulsive noise is only carried out at the truly contaminated samples of the received signal. This approach avoids typical problems encountered in clipping and nulling algorithms, where samples besides the ones containing the impulsive noise are also affected, thus creating additional distortion in the received signal. The superior performance of the proposed scheme is demonstrated via numerical simulations.

Simultaneous cancellation of narrow band interference and impulsive noise in PLC systems

“…To get rid of the IN component, we adopt an iterative implementation of the true support estimation (TSE) algorithm proposed in [4]. As the first iteration we identify the IN contaminated samples in, r p , using the TSE scheme, and null them.…”

“…The resulting observation vector, r p , in (5) is then used to estimate the IN occurring during the preamble symbol transmission. We again take advantage of the TSE algorithm to perform the IN estimation at this stage [4]. The estimated IN noise,î p , is then subtracted from the observation vector r p , resulting in…”

“…The resulting vectorr s is the observation vector for the TSE algorithm to estimate the IN samples occurring during the transmission of the OFDM symbol in the frame [4]. The resulting estimate of the IN,î s is subtracted from the received samples r s .…”

“…On one hand, the IN, which is sparse in nature in the time domain, presents a very high amplitude as compared to the background noise. If not mitigated, these IN samples damage symbols in all the sub-carriers [4], [5]. On the other, the NBI is sparse in the frequency domain and therefore its presence typically affects only the sub-carrier that is hit by it [6]- [8].…”

Maximum-likelihood channel estimation in presence of impulsive noise for PLC systems

“…To estimate the IN we can use the true support estimation (TSE) algorithm proposed in [12]. This algorithm exploits the spectrum of unused subcarriers, which contains only noise samples, to estimate the support of the IN.…”