Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning (1303.1217v1)

Abstract: Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noise and hence experience degradation in communication performance in impulsive noise. Alternate designs assume a parametric statistical model of impulsive noise and use the model parameters in mitigating impulsive noise. These receivers require overhead in training and parameter estimation, and degrade due to model and parameter mismatch, especially in highly dynamic environments. In this paper, we model impulsive noise as a sparse vector in the time domain without any other assumptions, and apply sparse Bayesian learning methods for estimation and mitigation without training. We propose three iterative algorithms with different complexity vs. performance trade-offs: (1) we utilize the noise projection onto null and pilot tones to estimate and subtract the noise impulses; (2) we add the information in the data tones to perform joint noise estimation and OFDM detection; (3) we embed our algorithm into a decision feedback structure to further enhance the performance of coded systems. When compared to conventional OFDM PLC receivers, the proposed receivers achieve SNR gains of up to 9 dB in coded and 10 dB in uncoded systems in the presence of impulsive noise.

• The paper demonstrates the use of sparse Bayesian learning to estimate and mitigate impulsive noise in PLC systems without relying on predefined noise models.
• It introduces three novel iterative algorithms that optimize noise estimation and OFDM detection, significantly boosting SNR by up to 10 dB.
• The proposed framework outperforms conventional and CS-based methods, offering a robust solution for dynamic and unpredictable noise environments in smart grid communications.

Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning

The paper entitled "Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning" explores the complexities of mitigating impulsive noise in powerline communication (PLC) systems, employing sparse Bayesian learning (SBL) techniques. The authors investigate the challenges posed by asynchronous and cyclostationary impulsive noise on Orthogonal Frequency Division Multiplexing (OFDM) based PLC systems and propose solutions grounded in the sparse representation of noise.

Noise Characterization and Challenges

In PLC systems, impulsive noise is a significant impediment, leading to performance degradation. This noise, often non-Gaussian, is categorized into asynchronous impulsive noise, typically encountered in higher frequency scenarios due to random emissions, and cyclostationary noise, induced by periodic interference synchronous with the AC mains cycle. The paper reviews the complexities associated with these noise types, citing that cyclostationary noise often dominates narrowband PLC systems while asynchronous noise is prevalent in broadband communications contexts.

Sparse Bayesian Learning for Noise Mitigation

The paper introduces a robust framework leveraging SBL to estimate and mitigate impulsive noise without reliance on predefined statistical noise models. This non-parametric approach offers flexibility over traditional parametric methods, which are often constrained by their need for model-specific parameters and assumptions about the noise environment.

1. Sparse Representation in the Time Domain: The research models impulsive noise as a sparse vector in the time domain. SBL techniques are applied to provide a probabilistic framework that allows for the efficient estimation of sparse signals. This approach does not require training sequences, thereby reducing overhead and ensuring adaptability in dynamic environments.
2. Iterative Algorithms: Three novel iterative algorithms are proposed, each catering to different complexity and performance trade-offs. The initial algorithm utilizes noise projection onto null and pilot tones, the second integrates data tones for joint noise estimation and OFDM detection, and the third embeds the method into a decision feedback structure to optimize performance further.

Performance Evaluation and Results

The paper presents a comprehensive evaluation of the proposed algorithms under various impulsive noise scenarios modeled on Gaussian mixture and Middleton Class A distributions, as well as the cyclostationary noise. The findings show significant SNR gains compared to conventional OFDM PLC systems—up to 10 dB in uncoded systems and 9 dB in coded systems. These improvements highlight the effectiveness of the SBL framework in adapting to noise and enhancing communication reliability in PLC systems.

A notable aspect is the comparison against the CS-based algorithm and parametric techniques that assume knowledge of noise models. The SBL-based approach consistently outperforms these methods, particularly in scenarios where model assumptions and parameter estimates are not aligned with the actual noise characteristics.

Implications and Future Directions

The implications of this research are profound for practical PLC systems, especially in smart grid communications. By alleviating the constraints of parametric noise models, these methods promise more resilient communication frameworks adaptable to evolving and unpredictable noise environments. The room for future exploration is vast; potential advances include optimizing the algorithms for real-time applications and extending the framework to other forms of interference beyond impulsive noise.

In summary, the paper provides a compelling case for the adoption of sparse Bayesian learning in PLC systems, with empirical evidence of its superior performance in mitigating impulsive noise without prior training or model parameters. This research lays a robust foundation for future developments in adaptive noise mitigation strategies, with the potential for broad application across diverse communication frameworks.