Optimal Detection for Diffusion-Based Molecular Timing Channels

This work studies optimal detection for communication over diffusion-based molecular timing (DBMT) channels. The transmitter simultaneously releases multiple information particles, where the information is encoded in the time of release. The receiver decodes the transmitted information based on the random time of arrival of the information particles, which is modeled as an additive noise channel. For a DBMT channel without flow, this noise follows the L\'evy distribution. Under this channel model, the maximum-likelihood (ML) detector is derived and shown to have high computational complexity. It is also shown that under ML detection, releasing multiple particles improves performance, while for any additive channel with $\alpha$-stable noise where $\alpha<1$ (such as the DBMT channel), under linear processing at the receiver, releasing multiple particles degrades performance relative to releasing a single particle. Hence, a new low-complexity detector, which is based on the first arrival (FA) among all the transmitted particles, is proposed. It is shown that for a small number of released particles, the performance of the FA detector is very close to that of the ML detector. On the other hand, error exponent analysis shows that the performance of the two detectors differ when the number of released particles is large.Comment: 16 pages, 9 figures. Submitted for publicatio

Iterative receiver design for the estimation of Gaussian samples in impulsive noise

Impulsive noise is the main limiting factor for transmission over channels affected by elec-tromagnetic interference. We study the estimation of (correlated) Gaussian signals in an impulsive noise scenario. In this work, we analyze some of the existing, as well as some novel estimation algorithms. Their performance is compared, for the first time, for different channel conditions, including the Markov–Middleton scenario, where the impulsive noise switches between different noise states. Following a modern approach in digital communications, the receiver design is based on a factor graph model and implements a message passing algorithm. The correlation among signal samples, as well as among noise states brings about a loopy factor graph, where an iterative message passing scheme should be employed. As is well known, approximate variational inference techniques are necessary in these cases. We propose and analyze different algorithms and provide a complete performance comparison among them, showing that the expectation propagation, transparent propa-gation, and parallel iterative schedule approaches reach a performance close to optimal, at different channel conditions

Maximum likelihood detection for OFDM signals with strong nonlinear distortion effects

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadore

A Comparison of ICF and Companding for Impulsive Noise Mitigation in Powerline Communication Systems

In future smart cities, smart grid technologies which are usually enabled by Powerline Communication (PLC) techniques are required. However, data transmission over powerline channel traverses a non-Gaussian media due to the presence of Impulsive Noise (IN) operating at the frequencies of PLC system which can be deployed using the IEEE 1901, that uses Orthogonal Frequency Division Multiplexing (OFDM). These OFDM signals have asymmetric amplitude distribution, which makes it difficult to identify and mitigate the IN presence. Converting the amplitude distribution to a uniform distribution can enhance the ability to mitigate IN when nonlinear IN mitigation techniques such as blanking is applied. In this study, we apply Iterative Clipping and Filtering (ICF) and companding schemes which are Peak-to-Average Power Ratio (PAPR) reduction techniques to enable symmetric amplitude distribution of the OFDM signals. With an optimization search for the optimal blanking amplitude for the two PAPR reduction schemes. Results show that companding scheme achieves 4dB gain in terms of received signal-to-noise ratio better than ICF after the blanking was used to remove the IN

Power Line Communication (PLC) Impulsive Noise Mitigation: A Review

Power Line Communication (PLC) is a technology which transforms the power line into pathways for the conveyance of broadband data. It has the advantage for it can avoid new installation since the current installation used for electrical power can also be used for data transmission. However, this power line channel presents a harsh environment for data transmission owing to the challenges of impulsive noise, high attenuation, selective fading and etc. Impulsive noise poses a severe challenge as its Power Spectral Density (PSD) is between 10–15dB above background noise. For good performance of the PLC system, this noise must be mitigated.  This paper presents a review of the techniques for the mitigation of impulsive noise in PLC which is classified into four categories, namely time domain, time/frequency domain, error correction code and other techniques. Time domain technique is a memoryless nonlinear technique where the signal's amplitude only changes according to a specified threshold without changing the phase.  Mitigation of impulsive noise is carried out on the received time domain signal before the demodulation FFT operation of the OFDM. Time/Frequency technique is a method of mitigating impulsive noise on the received signal at both before FFT demodulation and after FFT demodulation of the OFDM system. Error correction code technique is the application of forward error correction code by adding redundancy bits to the useful data bits for detection and possibly correction of error occurring during transmission.  Identifying the best performing technique will enhance the deployment of the technique while exploring the PLC channel capacity enhancement in the future. The best performing scheme in each of the category were selected and their BER vs SNR curves were compared with respect to the impulsive noise + awgn curve. Amongst all of these techniques, the error correction code technique had a performance that presents almost an outright elimination of impulsive noise in power line channel. Keywords: Impulsive noise, time domain, time/frequency domain, error correction code, sparse Bayesian learning, recursive detection and modified PLC-DMT

Robust wireless sensor network for smart grid communication : modeling and performance evaluation

Our planet is gradually heading towards an energy famine due to growing population and industrialization. Hence, increasing electricity consumption and prices, diminishing fossil fuels and lack significantly in environment-friendliness due to their emission of greenhouse gasses, and inefficient usage of existing energy supplies have caused serious network congestion problems in many countries in recent years. In addition to this overstressed situation, nowadays, the electric power system is facing many challenges, such as high maintenance cost, aging equipment, lack of effective fault diagnostics, power supply reliability, etc., which further increase the possibility of system breakdown. Furthermore, the adaptation of the new renewable energy sources with the existing power plants to provide an alternative way for electricity production transformed it in a very large and complex scale, which increases new issues. To address these challenges, a new concept of next generation electric power system, called the "smart grid", has emerged in which Information and Communication Technologies (ICTs) are playing the key role. For a reliable smart grid, monitoring and control of power system parameters in the transmission and distribution segments are crucial. This necessitates the deployment of a robust communication network within the power grid. Traditionally, power grid communications are realized through wired communications, including power line communication (PLC). However, the cost of its installation might be expensive especially for remote control and monitoring applications. More recently, plenty of research interests have been drawn to the wireless communications for smart grid applications. In this regard, the most promising methods of smart grid monitoring explored in the literature is based on wireless sensor network (WSN). Indeed, the collaborative nature of WSN brings significant advantages over the traditional wireless networks, including low-cost, wider coverage, self-organization, and rapid deployment. Unfortunately, harsh and hostile electric power system environments pose great challenges in the reliability of sensor node communications because of strong RF interference and noise called impulsive noise. On account of the fundamental of WSN-based smart grid communications and the possible impacts of impulsive noise on the reliability of sensor node communications, this dissertation is supposed to further fill the lacking of the existing research outcomes. To be specific, the contributions of this dissertation can be summarized as three fold: (i) investigation and performance analysis of impulsive noise mitigation techniques for point-to-point single-carrier communication systems impaired by bursty impulsive noise; (ii) design and performance analysis of collaborative WSN for smart grid communication by considering the RF noise model in the designing process, a particular intension is given to how the time-correlation among the noise samples can be taken into account; (iii) optimal minimum mean square error (MMSE)estimation of physical phenomenon like temperature, current, voltage, etc., typically modeled by a Gaussian source in the presence of impulsive noise. In the first part, we compare and analyze the widely used non-linear methods such as clipping, blanking, and combined clipping-blanking to mitigate the noxious effects of bursty impulsive noise for point-to-point communication systems with low-density parity-check (LDPC) coded single-carrier transmission. While, the performance of these mitigation techniques are widely investigated for multi-carrier communication systems using orthogonal frequency division multiplexing (OFDM) transmission under the effect of memoryless impulsive noise, we note that OFDM is outperformed by its single-carrier counterpart when the impulses are very strong and/or they occur frequently, which likely exists in contemporary communication systems including smart grid communications. Likewise, the assumption of memoryless noise model is not valid for many communication scenarios. Moreover, we propose log-likelihood ratio (LLR)-based impulsive noise mitigation for the considered scenario. We show that the memory property of the noise can be exploited in the LLR calculation through maximum a posteriori (MAP) detection. In this context, provided simulation results highlight the superiority of the LLR-based mitigation scheme over the simple clipping/blanking schemes. The second contribution can be divided into two aspects: (i) we consider the performance analysis of a single-relay decode-and-forward (DF) cooperative relaying scheme over channels impaired by bursty impulsive noise. For this channel, the bit error rate (BER) performances of direct transmission and a DF relaying scheme using M-PSK modulation in the presence of Rayleigh fading with a MAP receiver are derived; (ii) as a continuation of single-relay collaborative WSN scheme, we propose a novel relay selection protocol for a multi-relay DF collaborative WSN taking into account the bursty impulsive noise. The proposed protocol chooses the N’th best relay considering both the channel gains and the states of the impulsive noise of the source-relay and relay-destination links. To analyze the performance of the proposed protocol, we first derive closed-form expressions for the probability density function (PDF) of the received SNR. Then, these PDFs are used to derive closed-form expressions for the BER and the outage probability. Finally, we also derive the asymptotic BER and outage expressions to quantify the diversity benefits. From the obtained results, it is seen that the proposed receivers based on the MAP detection criterion is the most suitable one for bursty impulsive noise environments as it has been designed according to the statistical behavior of the noise. Different from the aforementioned contributions, talked about the reliable detection of finite alphabets in the presence of bursty impulsive noise, in the thrid part, we investigate the optimal MMSE estimation for a scalar Gaussian source impaired by impulsive noise. In Chapter 5, the MMSE optimal Bayesian estimation for a scalar Gaussian source, in the presence of bursty impulsive noise is considered. On the other hand, in Chapter 6, we investigate the distributed estimation of a scalar Gaussian source in WSNs in the presence of Middleton class-A noise. From the obtained results we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian channel