Marcel Nassar

Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning

Asynchronous impulsive noise and periodic impulsive noises limit communication performance in OFDM powerline communication systems. Conventional OFDM receivers that assume additive white Gaussian noise experience degradation in communication performance in impulsive noise. Alternate designs assume a statistical noise model and use the model parameters in mitigating impulsive noise. These receivers require training overhead for parameter estimation, and degrade due to model and parameter mismatch. To mitigate asynchronous impulsive noise, we exploit its sparsity in the time domain, and apply sparse Bayesian learning methods to estimate and subtract the noise impulses. We propose three iterative algorithms with different complexity vs. performance trade-offs: (1) we utilize the noise projection onto null and pilot tones; (2) we add the information in the date tones to perform joint noise estimation and symbol detection; (3) we use decision feedback from the decoder to further enhance the accuracy of noise estimation. These algorithms are also embedded in a time-domain block interleaving OFDM system to mitigate periodic impulsive noise. Compared to conventional OFDM receivers, the proposed methods achieve SNR gains of up to 9 dB in coded and 10 dB in uncoded systems in asynchronous impulsive noise, and up to 6 dB in coded systems in periodic impulsive noise.

Local Utility Power Line Communications in the 3–500 kHz Band: Channel Impairments, Noise, and Standards

Future smart grid systems will intelligently monitor and control energy flows to improve the efficiency and reliability of power delivery. This monitoring and control requires low-delay, highly reliable communication between customers, local utilities, and regional utilities. A vital part of future smart grids is the two way communication links between smart meters at the customer sites and a (decentralized) command and control center operated by the local utility. To enable these two-way communication links, narrowband power line communication (PLC) systems operating in the 3-500 kHz band are attractive because they can be deployed over existing outdoor power lines. Power lines, however, have traditionally been designed for one-directional power delivery and remain hostile environments for communication signal propagation. In this article, we review signal processing approaches to model channel impairments and impulsive noise and mitigate their effects in narrowband PLC systems. We examine ways to improve the communication performance based on current and emerging standards.

Cyclostationary noise modeling in narrowband powerline communication for Smart Grid applications

A Smart Grid intelligently monitors and controls energy flows in an electric grid. Having up-to-date distributed readings of grid conditions helps utilities efficiently scale generation up or down to meet demand. Narrowband powerline communication (PLC) systems can provide these up-to-date readings from subscribers to the local utility over existing power lines. A key challenge in PLC systems is overcoming additive non-Gaussian noise. In this paper, we propose to use a cyclostationary model for the dominant component of additive non-Gaussian noise. The key contributions are (1) fitting measured data from outdoor narrowband PLC system field trials to a cyclostationary model, and (2) developing a cyclostationary noise generation model that fits measured data. We found that the period in the cyclostationary model matched half of the period of the main powerline frequency, which is consistent with previous work in indoor PLC additive noise modeling.

Mitigating Near-field Interference in Laptop Embedded Wireless Transceivers

In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is well modeled using non-Gaussian impulsive statistics. Data communication transceivers, however, are typically designed under the assumption of additive Gaussian noise and exhibit degradation in communication performance in the presence of RFI. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10 − 5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model RFI using Middleton Class A and Symmetric Alpha Stable (SαS) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of RFI models to the measured interference data from a computer platform, (3) the communication performance vs. computational complexity tradeoffs in receivers designed to mitigate RFI modeled as Class A interference, (4) the communication performance vs. computational complexity tradeoffs in filtering and detections methods to combat RFI modeled as SαS interference, and (5) the approximations to filtering and detection methods developed to mitigate RFI for a computationally efficient implementation.

Statistical Modeling of Asynchronous Impulsive Noise in Powerline Communication Networks

Powerline distribution networks are increasingly being employed to support smart grid communication infrastructure and in-home LAN connectivity. However, their primary function of power distribution results in a hostile environment for communication systems. In particular, asynchronous impulsive noise, with levels as high as 50dB above thermal noise, causes significant degradation in communication performance. Much of the prior work uses limited empirical measurements to propose a statistical model for instantaneous statistics of asynchronous noise. In this paper, we (i) derive a canonical statistical-physical model of the instantaneous statistics of asynchronous noise based on the physical properties of the PLC network, and (ii) validate the distribution using simulated and measured PLC noise data. The results of this paper can be used to analyze, simulate, and mitigate the effect of the asynchronous noise on PLC systems.

Mitigating near-field interference in laptop embedded wireless transceivers

In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is impulsive in nature. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10-5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model impulsive noise using Middleton class A and symmetric alpha stable (SaS) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of parameter estimation algorithms to measured RFI data from the computer platform, (3) the communication performance vs. computational complexity tradeoffs for the correlation receiver, Wiener filter, and Bayesian detector, and (4) the performance of myriad filtering in combating RFI interference modeled as SaS interference.

Cyclic spectral analysis of power line noise in the 3–200 kHz band

Narrowband OFDM Power Line Communication (NB-OFDM PLC) systems are a key component of current and future smart grids. NB-OFDM PLC systems enable next-generation smart metering, distributed control, and monitoring applications over existing power delivery infrastructure. It has been shown that the performance of these systems is severely limited by impulsive, non-Gaussian additive noise. A substantial component of this noise has time-periodic statistics (i.e. it is cyclostationary) synchronous to the AC mains cycle. In this work, we analyze the cyclic structure of power line noise observed in a G3 PLC system operating in the CENELEC 3-148.5 kHz band. Our contributions include: (i) the characterization of noise measurements in several urban usage environments, (ii) the development of a cyclic bit loading method for G3, and (iii) the quantification of its throughput gains over measured noise. Through this analysis, we confirm strong cyclostationarity in power lines and identify several sources of the cyclic noise.

Non-Parametric Impulsive Noise Mitigation in OFDM Systems Using Sparse Bayesian Learning

Additive asynchronous impulsive noise limits communication performance in certain OFDM systems, such as powerline communications, cellular LTE and 802.11n systems. Under additive impulsive noise, the fast Fourier transform (FFT) in the OFDM receiver introduces time-dependence in the subcarrier noise statistics. As a result, complexity of optimal detection becomes exponential in the number of subcarriers. Many previous approaches assume a statistical model of the impulsive noise and use parametric methods in the receiver to mitigate impulsive noise. Parametric methods degrade with increasing model mismatch, and require training and parameter estimation. In this paper, we apply sparse Bayesian learning techniques to estimate and mitigate impulsive noise in OFDM systems without the need for training. We propose two non-parametric iterative algorithms: (1) estimate impulsive noise by its projection onto null and pilot tones so that the OFDM symbol is recovered by subtracting out the impulsive noise estimate; and (2) jointly estimate the OFDM symbol and impulsive noise utilizing information on all tones. In our simulations, the estimators achieve 5dB and 10dB SNR gains in communication performance respectively, as compared to conventional OFDM receivers.

Low complexity EM-based decoding for OFDM systems with impulsive noise

Modern OFDM systems such as cellular LTE and powerline communications experience additive impulsive noise emitted from their environment. OFDM modulation has been shown to provide resilience to impulsive noise due to its code diversity. However, typical OFDM receivers designed under the Gaussian noise assumption will lead to suboptimal performance due to the dependence in noise statistics across subcarriers resulting from the FFT operation. As a result, optimal detection of OFDM symbols becomes prohibitive due to its exponential complexity. We consider the design of a practical class of OFDM receivers that are constrained to perform independent detection on each subcarrier. In this paper, we propose an EM based low-complexity iterative decoding algorithm for OFDM systems in impulsive noise environments that preserves the independent decoding across subcarriers. We validate its performance under typical impulsive noise conditions based on noise traces collected from wireless and powerline platforms. Our proposed method achieves a gain between 2 – 7dB over the conventional OFDM receiver depending on the SNR range.

Bayesian Active Learning for Drug Combinations

The dynamics of complex diseases are governed by intricate interactions of myriad factors. Drug combinations, formed by mixing several single-drug treatments at various doses, can enhance the effectiveness of the therapy by targeting multiple contributing factors. The main challenge in designing drug combinations is the highly nonlinear interaction of the constituent drugs. Prior work focused on guided space-exploratory heuristics that require discretization of drug doses. While being more efficient than random sampling, these methods are impractical if the drug space is high dimensional or if the drug sensitivity is unknown. Furthermore, the effectiveness of the obtained combinations may decrease if the resolution of the discretization grid is not sufficiently fine. In this paper, we model the biological system response to a continuous combination of drug doses by a Gaussian process (GP). We perform closed-loop experiments that rely on the expected improvement criterion to efficiently guide the exploration process toward drug combinations with the optimal response. When computing the criterion, we marginalize out the GP hyperparameters in a fully Bayesian manner using a particle filter. Finally, we employ a hybrid Monte Carlo algorithm to rapidly explore the high-dimensional continuous search space. We demonstrate the effectiveness of our approach on a fully factorial Drosophila dataset, an antiviral drug dataset for Herpes simplex virus type 1, and simulated human Apoptosis networks. The results show that our approach significantly reduces the number of required trials compared to existing methods.