Performance Analysis of Multihop Wireless Links over Generalized-K Fading Channels

The performance of multihop links is studied in this contribution by both analysis and simulations, when communicating over Generalized-$K$ ($K_G$) fading channels. The performance metrics considered include symbol error rate (SER), outage probability, level crossing rate (LCR) and average outage duration (AOD). First, the expressions for both the SER and outage probability are derived by approximating the probability density function (PDF) of the end-to-end signal-to-noise ratio (SNR) using an equivalent end-to-end PDF. We show that this equivalent end-to-end PDF is accurate for analyzing the outage probability. Then, the second-order statistics of LCR and AOD of multihop links are analyzed. Finally, the performance of multihop links is investigated either by simulations or by evaluation of the expressions derived. Our performance results show that the analytical expressions obtained can be well justified by the simulation results. The studies show that the $K_G$ channel model as well as the expressions derived in this paper are highly efficient for predicting the performance metrics and statistics for design of multihop communication links

Uma extensão para os modelos de desvanecimento n-µ e k-µ

Orientador: Michel Daoud YacoubTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O resumo poderá ser visualizado no texto completo da tese digitalAbstract: The abstract is available with the full electronic digital documentDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétrica141804/2014-9CNP

On the Second-Order Statistics of Correlated Cascaded Rayleigh Fading Channels

The second-order statistics of two correlated cascaded (double) Rayleigh fading channels are analyzed, where different relevant second-order cross-correlation functions of in-phase and quadrature components of the cascaded Rayleigh channels are derived. The level crossing rate (LCR) and average fade duration (AFD) of the cascaded channels are evaluated, and a single-integral form of the LCR is derived. Numerical results of the LCR and AFD are presented, and the effect of the correlation is illustrated

Doppler spread estimation in mobile fading channels

The Doppler spread, or equivalently, the mobile speed, is a measure of the spectral dispersion of a mobile fading channel. Accurate estimation of the mobile speed is important in wireless mobile applications which require such as knowledge of the rate of channel variations. In this dissertation, first the performance of classical crossing- and covariance-based speed estimators is studied. Next, the problem of mobile speed estimation using diversity combining is investigated. Then, a nonparametric estimation technique is proposed that is robust to different channel variations. Finally, cyclostationarity-based speed estimators which can be applied either blindly or with the aid of pilot data, are developed. A unified framework for the performance analysis of well-known crossing and covariance based speed estimation techniques is presented. This allows a fair analytical comparison among all the methods. Interestingly, it is proved that all these methods are asymptotically equivalent, i.e., for large observation intervals. The extensive performance analysis, supported by Monte Carlo simulations, has revealed that depending on the channel condition and the observation interval, one needs to use a crossing or a covariance based technique to achieve the desired estimation accuracy over a large range of mobile speeds. Two common diversity schemes, selection combining (SC) and maximal ratio combining (MRC), are considered for Doppler spread estimation. Four new estimators are derived which rely on the inphase zero crossing rate, inphase rate of maxima, phase zero crossing rate, and the instantaneous frequency zero crossing rate of the output of SC. Two estimators, which work based on the level crossing rates of the envelopes at the output of SC and MRC, are also proposed. The performances of all these estimators are investigated in realistic noisy environments with different kinds of scatterings and different numbers of diversity branches. Then a novel speed estimation technique is proposed that is applicable to both mobile and base stations, based on the characteristics in the power spectrum of mobile fading channels. The analytic performance analysis, verified by Monte Carlo simulations, shows that this low-complexity estimator is not only robust to both Gaussian and non-Gaussian noises, but also insensitive to nonisotropic scattering observed at the mobile. The estimator performs very well in both two- and three-dimensional propagation environments. By taking advantage of resolvable paths in wideband fading channels, the robustness against both nonisotropic scattering and line of sight can be further increased, due to the differences among the Doppler spectra observed at different paths. This technique is also extended to base stations with antenna arrays. By exploiting the spatial information, the proposed space-time estimator exhibits excellent performance over a wide range of noise power, nonisotropic scattering, and the line-of-sight component. This is all verified by simulation. The utility of the new method is further demonstrated by applying it to the measured data. Finally, to design robust blind and data-aided mobile speed estimators, a proposal is made to exploit the inherent cyclostationarity of linearly modulated signals transmitted through fading channels. Two categories of cyclic-correlation- and cyclic-spectrum-based methods are developed. Extension to space-time speed estimation at the base station in macrocells is also provided. In comparison with the existing methods, the new estimators can be used without any need for pilot tones and are robust to additive stationary noise or interference of any color or distribution. Unlike the conventional multi-antenna based method, the proposed space-time speed estimator does not assume the receiver noise to be spatially white. A suboptimal training sequence is also devised for pilot-symbol assisted methods, to reduce the estimation error. The performance of the proposed estimators are illustrated via extensive Monte Carlo simulations

Deep Neural Network Architectures for Modulation Classification

This thesis investigates the value of employing deep learning for the task of wireless signal modulation recognition. Recently in deep learning research on AMC, a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real wireless channel, and uses 10 different modulation types. Further, a CNN architecture was developed and shown to deliver performance that exceeds that of expert-based approaches. Here, we follow the framework of O’shea [1] and find deep neural network architectures that deliver higher accuracy than the state of the art. We tested the architecture of O’shea [1] and found it to achieve an accuracy of approximately 75% of correctly recognizing the modulation type. We first tune the CNN architecture and find a design with four convolutional layers and two dense layers that gives an accuracy of approximately 83.8% at high SNR. We then develop architectures based on the recently introduced ideas of Residual Networks (ResNet) and Densely Connected Network (DenseNet) to achieve high SNR accuracies of approximately 83% and 86.6%, respectively. We also introduce a CLDNN to achieve an accuracy of approximately 88.5% at high SNR. To improve the classification accuracy of QAM, we calculate the high order cumulants of QAM16 and QAM64 as the expert feature and improve the total accuracy to approximately 90%. Finally, by preprocessing the input and send them into a LSTM model, we improve all classification success rates to 100% except the WBFM which is 46%. The average modulation classification accuracy got a improvement of roughly 22% in this thesis