Although there already exists a rich literature on cooperative diversity, current results are mainly restricted to the conventional assumption of additive white Gaussian noise (AWGN). AWGN model realistically represents the thermal noise at the receiver, but ignores the impulsive nature of atmospheric noise, electromagnetic interference, or man-made noise which might be dominant in many practical applications. In this thesis, we investigate the performance of cooperative communication over Rayleigh fading channels in the presence of impulsive noise modeled by Middleton Class A noise. We consider a multi-relay network with amplify-and-forward relaying and orthogonal cooperation protocol. As for the coding across the relays, we employ either space-time coding or repetition coding. For each scheme, we assume various scenarios based on relays’ location and quantify the diversity advantages through the derivation of the pairwise error probability. Based on the minimization of a union bound on the error rate performance, we further propose optimal power allocation schemes and demonstrate significant performance gains over their counterparts with equal power allocation. We finally present an extensive Monte Carlo simulation to confirm our analytical results and corroborate on our results.

Although there already exists a rich literature on cooperative diversity, current results are mainly restricted to the conventional assumption of additive white Gaussian noise (AWGN). AWGN model realistically represents the thermal noise at the receiver, but ignores the impulsive nature of atmospheric noise, electromagnetic interference, or man-made noise which might be dominant in many practical applications. In this thesis, we investigate the performance of cooperative communication over Rayleigh fading channels in the presence of impulsive noise modeled by Middleton Class A noise. We consider a multi-relay network with amplify-and-forward relaying and orthogonal cooperation protocol. As for the coding across the relays, we employ either space-time coding or repetition coding. For each scheme, we assume various scenarios based on relays’ location and quantify the diversity advantages through the derivation of the pairwise error probability. Based on the minimization of a union bound on the error rate performance, we further propose optimal power allocation schemes and demonstrate significant performance gains over their counterparts with equal power allocation. We finally present an extensive Monte Carlo simulation to confirm our analytical results and corroborate on our results.

In wireless communication systems, receivers have generally been designed under the assumption that the additive noise in system is Gaussian. Wireless receivers, however, are affected by radio frequency interference (RFI) generated from various sources such as other wireless users, switching electronics, and computational platforms. RFI is well modeled using non-Gaussian impulsive statistics and can severely degrade the communication performance of wireless receivers designed under the assumption of additive Gaussian noise. Methods to avoid, cancel, or reduce RFI have been an active area of research over the past three decades. In practice, RFI cannot be completely avoided or canceled at the receiver. This dissertation derives the statistics of the residual RFI and utilizes them to analyze and improve the communication performance of wireless receivers. The primary contributions of this dissertation are to (i) derive instantaneous statistics of co-channel interference in a field of Poisson and Poisson-Poisson clustered interferers, (ii) characterize throughput, delay, and reliability of decentralized wireless networks with temporal correlation, and (iii) design pre-filters to mitigate RFI in wireless receivers. / text

The small target detection in High Frequency Surface Wave Radar is limited by the presence of various clutter and interference. Several novel signal processing techniques are developed to improve the system detection performance. As an external interference due to local lightning, impulsive noise increases the broadband noise level and then precludes the targets from detection. A new excision approach is proposed with modified linear predictions as the reconstruction solution. The system performance is further improved by de-noising the estimated covariance matrix through signal property mapping method. The existence of non-stationary sea clutter and ionospheric clutter can result in excessive false alarm rate through the high sidelobe level in adaptive beamforming. The optimum threshold discrete quadratic inequality constraints method is proposed to guarantee the sidelobe-controlling problem consistently feasible and optimal. This constrained optimization problem can be formulated into a second order cone problem with efficient mathematical solution. Both simulation and experimental results validate the improved performance and feasibility of our method. Based on the special noise characteristics of High Frequency radar, an adaptive switching Constant False Alarm Rate detector is proposed for targets detection in the beamformed range-Doppler map. The switching rule and adaptive footprint are applied to provide the optimum background noise estimation. For this new method about 14% probability of detection improvement has been verified by experimental data, and meanwhile the false alarm rate is reduced significantly compared to the original CFAR. The conventional Doppler processing has difficulty to recognize a target if its frequency is close to a Bragg line. One detector is proposed to solve this co-located co-channel resolvability problem under the assumption that target/clutter have different phase modulation. Moreover with the pre-whitening processing, the Reversible Jump Markov Chain Monte Carlo method can provide target number and Direction-of-Arrival estimation with lower detection threshold compared to beamforming and subspace methods. RJMCMC is able to convergent to the optimal resolution for a data set that is small compared with information theoretic criteria.

This report intends to verify the possibility that the FastICA algorithm could be applied to the GPS system to eliminate the impulsive noise from the receiver end. As the impulsive noise is so unpredictable in its pattern and of great energy level to swallow the signal we need, traditional signal selection methods exhibit no much use in dealing with this problem. Blind Source Separation seems to be a good way to solve this, but most of the other BSS algorithms beside FastICA showed more or less degrees of dependency on the pattern of the noise. In this thesis, the basic mathematic modelling of this advanced algorithm, along with the principles of the commonly used fast independent component analysis (fastICA) based on fixed-point algorithm are discussed. To verify that this method is useful under industrial use environment to remove the impulsive noises from digital BPSK modulated signals, an observation signal mixed with additive impulsive noise is generated and separated by fastICA method. And in the last part of the thesis, the fastICA algorithm is applied to the GPS receiver modeled in the SoftGNSS project and verified to be effective in industrial applications. The results have been analyzed. / 6

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Many different types of promising spectrum sensing algorithms for Cognitive Radio (CR) have already been developed. However, many of these algorithms lack robustness with respect to signal statistical parameters uncertainties, such as the noise variance or the shape of its distribution (often assumed to be simply Gaussian). In conjunction with the low Signal-to-Noise Ratio (SNR) requirements, this lack of robustness can often render interesting sensing algorithms impractical for real-life applications. In this thesis, we primarily focus on the impact of heavy-tail noise distributions on different CR detectors and the use of signal limiters (mostly the spatial sign function) to improve their robustness to such noise distributions. Introducing a non-linear transformation of the received signal prior to its processing by the detector fundamentally changes the signal distribution which in turn modifies the distribution of the detector statistic. In order to parametrize the detector and study its performance, it is then necessary to know the shape of the modified distribution.Three types of detectors are investigated: a generic second-order cyclic-feature detectors, a Scaled-Largest Eigenvalue (SLE) detector studied in the context of stationary time-series and a new Sequential Likelihood Ratio Test (SLRT) detector. The analysis conducted for each detector revolves around the influence of its parameters, the distribution of the detector statistic and several comparisons with similar detectors for various detection scenarios. Our results indicate that at the cost of a moderate performance loss in a Gaussian noise environment, all the detectors fitted with a signal limiter become robust to impulsive noise and noise parameters uncertainties. We provide analytical approximations for the detectors statistical distribution that allow us to use the detectors in such configurations as well as to study their performance for different signal limiters and noise distributions. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

The ever-growing demand for higher quality and faster multimedia content delivery over short distances in home environments drives the quest for higher data rates in wireless personal area networks (WPANs). One of the candidate IEEE 802.15.3a WPAN proposals support data rates up to 480 Mbps by using punctured convolutional codes with quadrature phase shift keying (QPSK) modulation for a multi-band orthogonal frequency-division multiplexing (MB-OFDM) system over ultra wideband (UWB) channels. In the first part of this dissertation, we combine more powerful near-Shannon-limit turbo codes with bandwidth efficient trellis coded modulation, i.e., turbo trellis coded modulation (TTCM), to further improve the data rates up to 1.2 Gbps. A modified iterative decoder for this TTCM coded MB-OFDM system is proposed and its bit error rate performance under various impulsive noises over both Gaussian and UWB channel is extensively investigated, especially in mismatched scenarios. A robust decoder which is immune to noise mismatch is provided based on comparison of impulsive noises in time domain and frequency domain. The accurate estimation of the dynamic noise model could be very difficult or impossible at the receiver, thus a significant performance degradation may occur due to noise mismatch. In the second part of this dissertation, we prove that the minimax decoder in \cite, which instead of minimizing the average bit error probability aims at minimizing the worst bit error probability, is optimal and robust to certain noise model with unknown prior probabilities in two and higher dimensions. Besides turbo codes, another kind of error correcting codes which approach the Shannon capacity is low-density parity-check (LDPC) codes. In the last part of this dissertation, we extend the density evolution method for sum-product decoding using mismatched noises. We will prove that as long as the true noise type and the estimated noise type used in the decoder are both binary-input memoryless output symmetric channels, the output from mismatched log-likelihood ratio (LLR) computation is also symmetric. We will show the Shannon capacity can be evaluated for mismatched LLR computation and it can be reduced if the mismatched LLR computation is not an one-to-one mapping function. We will derive the Shannon capacity, threshold and stable condition of LDPC codes for mismatched BIAWGN and BIL noise types. The results show that the noise variance estimation errors will not affect the Shannon capacity and stable condition, but the errors do reduce the threshold. The mismatch in noise type will only reduce Shannon capacity when LLR computation is based on BIL.

Electromagnetic disturbance can be detrimental to the performance of a radio communication system, and in today’s society where more and more electronic devices are present in our everyday life it is increasingly vital to consider man-made interference. A communication system can take into consideration the noise characteristics and by doing so will excel in such areas, however, this follows that the algorithms utilized in such systems are more computationally complex and are therefore slow. This master thesis aims to explore the possibility of a neural network-based solution that reaches the same accuracy, as existing methods, but more quickly. Numerous different existing model alternatives have been explored and a plethora of different improvement techniques have been outlined. Two models, Hannet and Lannet, have been designed and improved to enable adaptive demodulation both including or excluding decoding at the receiver in an end-to-end communication system. The evaluation results demonstrate that the proposed models are comparable and in some cases even more accurate than current standardized methods. However, the models are unable to fully learn the decoding algorithms present in the experiments. Thus even though demodulation by itself thrives, performing decoding in conjunction with demodulation is out of reach for these models.

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