A Tractable Line-of-Sight Product Channel Model: Application to Wireless Powered Communications

We here present a general and tractable fading model for line-of-sight (LOS) scenarios, which is based on the product of two independent and non-identically distributed κ- μ shadowed random variables. Simple closed-form expressions for the probability density function and cumulative distribution function are derived, which are as tractable as the corresponding expressions derived from a product of Nakagami-m random variables. This newly proposed model simplifies the challenging characterization of LOS product channels, as well as combinations of LOS channels with non-LOS ones. Results are used to analyze performance measures of interest in the context of wireless powered communications.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysis of Gaussian Quadratic Forms with Application to Statistical Channel Modeling

Finalmente, en el contexto de modelado de canal, la metodología de análisis de variables propuesta permite obtener dos nuevas generalizaciones del conocido modelo de desvanecimiento kappa-mu shadowed. Estas dos nuevas distribuciones, nombradas Beckmann fluctuante y kappa-mu shadowed correlado, incluyen como casos particulares a la gran mayoría de distribuciones de desvanecimientos usadas en la literatura, abarcando desde los modelos clásicos de Rayleigh y Rice hasta otros más generales y complejos como el Beckmann y el kappa-mu. Para ambas distribuciones, se presenta su caracterización estadística de primer orden, i.e., función generadora de momentos (MGF), PDF y CDF; así como los estadísticos de segundo orden del modelo Beckmann fluctuante. Fecha de lectura de Tesis Doctoral: 24 Enero 2020En esta tesis se presenta una nueva aproximación a la distribución de de formas cuadráticas gaussianas (FCGs) no centrales tanto en variables reales como complejas. Para ello, se propone un nuevo método de análisis de variables aleatorias que, en lugar de centrarse en el estudio de la variable en cuestión, se basa en la caracterización estadística de una secuencia de variables aleatorias auxiliares convenientemente definida. Como consecuencia, las expresiones obtenidas, con independencia del grado de precisión adquirido, siempre representan una distribución válida, siendo ésta su principal ventaja. Aplicando este método, se obtienen simples expresiones recursivas para la función densidad de probabilidad (PDF) y la función de distribución (CDF) de las FCGs reales definidas positivas. En el caso de las formas complejas, esta nueva forma de análisis conduce a aproximaciones para los estadísticos de primer orden en términos de funciones elementales (exponenciales y potencias), siendo más convenientes para cálculos posteriores que otras soluciones disponibles en la literatura. La tratabilidad matemática se ejemplifica mediante el análisis de sistemas de combinación por razón máxima (MRC) sobre canales Rice correlados, proporcionando aproximaciones cerradas para la probabilidad de outage y la probabilidad de error de bit

Approximations for Performance Analysis in Wireless Communications and Applications to Reconfigurable Intelligent Surfaces

In the last few decades, the field of wireless communications has witnessed significant technological advancements to meet the needs of today’s modern world. The rapidly emerging technologies, however, are becoming increasingly sophisticated, and the process of investigating their performance and assessing their applicability in the real world is becoming more challenging. That has aroused a relatively wide range of solutions in the literature to study the performance of the different communication systems or even draw new results that were difficult to obtain. These solutions include field measurements, computer simulations, and theoretical solutions such as alternative representations, approximations, or bounds of classic functions that commonly appear in performance analyses. Field measurements and computer simulations have significantly improved performance evaluation in communication theory. However, more advanced theoretical solutions can be further developed in order to avoid using the ex- pensive and time-consuming wireless communications measurements, replace the numerical simulations, which can sometimes be unreliable and suffer from failures in numerical evaluation, and achieve analytically simpler results with much higher accuracy levels than the existing theoretical ones. To this end, this thesis firstly focuses on developing new approximations and bounds using unified approaches and algorithms that can efficiently and accurately guide researchers through the design of their adopted wireless systems and facilitate the conducted performance analyses in the various communication systems. Two performance measures are of primary interest in this study, namely the average error probability and the ergodic capacity, due to their valuable role in conducting a better understanding of the systems’ behavior and thus enabling systems engineers to quickly detect and resolve design issues that might arise. In particular, several parametric expressions of different analytical forms are developed to approximate or bound the Gaussian Q-function, which occurs in the error probability analysis. Additionally, any generic function of the Q-function is approximated or bounded using a tractable exponential expression. Moreover, a unified logarithmic expression is proposed to approximate or bound the capacity integrals that occur in the capacity analysis. A novel systematic methodology and a modified version of the classical Remez algorithm are developed to acquire optimal coefficients for the accompanying parametric approximation or bound in the minimax sense. Furthermore, the quasi-Newton algorithm is implemented to acquire optimal coefficients in terms of the total error. The average symbol error probability and ergodic capacity are evaluated for various applications using the developed tools. Secondly, this thesis analyzes a couple of communication systems assisted with reconfigurable intelligent surfaces (RISs). RIS has been gaining significant attention lately due to its ability to control propagation environments. In particular, two communication systems are considered; one with a single RIS and correlated Rayleigh fading channels, and the other with multiple RISs and non-identical generic fading channels. Both systems are analyzed in terms of outage probability, average symbol error probability, and ergodic capacity, which are derived using the proposed tools. These performance measures reveal that better performance is achieved when assisting the communication system with RISs, increasing the number of reflecting elements equipped on the RISs, or locating the RISs nearer to either communication node. In conclusion, the developed approximations and bounds, together with the optimized coefficients, provide more efficient tools than those available in the literature, with richer capabilities reflected by the more robust closed-form performance analysis, significant increase in accuracy levels, and considerable reduction in analytical complexity which in turns can offer more understanding into the systems’ behavior and the effect of the different parameters on their performance. Therefore, they are expected to lay the groundwork for the investigation of the latest communication technologies, such as RIS technology, whose performance has been studied for some system models in this thesis using the developed tools

Neural-Kalman Schemes for Non-Stationary Channel Tracking and Learning

This Thesis focuses on channel tracking in Orthogonal Frequency-Division Multiplexing (OFDM), a widely-used method of data transmission in wireless communications, when abrupt changes occur in the channel. In highly mobile applications, new dynamics appear that might make channel tracking non-stationary, e.g. channels might vary with location, and location rapidly varies with time. Simple examples might be the di erent channel dynamics a train receiver faces when it is close to a station vs. crossing a bridge vs. entering a tunnel, or a car receiver in a route that grows more tra c-dense. Some of these dynamics can be modelled as channel taps dying or being reborn, and so tap birth-death detection is of the essence. In order to improve the quality of communications, we delved into mathematical methods to detect such abrupt changes in the channel, such as the mathematical areas of Sequential Analysis/ Abrupt Change Detection and Random Set Theory (RST), as well as the engineering advances in Neural Network schemes. This knowledge helped us nd a solution to the problem of abrupt change detection by informing and inspiring the creation of low-complexity implementations for real-world channel tracking. In particular, two such novel trackers were created: the Simpli- ed Maximum A Posteriori (SMAP) and the Neural-Network-switched Kalman Filtering (NNKF) schemes. The SMAP is a computationally inexpensive, threshold-based abrupt-change detector. It applies the three following heuristics for tap birth-death detection: a) detect death if the tap gain jumps into approximately zero (memoryless detection); b) detect death if the tap gain has slowly converged into approximately zero (memory detection); c) detect birth if the tap gain is far from zero. The precise parameters for these three simple rules can be approximated with simple theoretical derivations and then ne-tuned through extensive simulations. The status detector for each tap using only these three computationally inexpensive threshold comparisons achieves an error reduction matching that of a close-to-perfect path death/birth detection, as shown in simulations. This estimator was shown to greatly reduce channel tracking error in the target Signal-to-Noise Ratio (SNR) range at a very small computational cost, thus outperforming previously known systems. The underlying RST framework for the SMAP was then extended to combined death/birth and SNR detection when SNR is dynamical and may drift. We analyzed how di erent quasi-ideal SNR detectors a ect the SMAP-enhanced Kalman tracker's performance. Simulations showed SMAP is robust to SNR drift in simulations, although it was also shown to bene t from an accurate SNR detection. The core idea behind the second novel tracker, NNKFs, is similar to the SMAP, but now the tap birth/death detection will be performed via an arti cial neuronal network (NN). Simulations show that the proposed NNKF estimator provides extremely good performance, practically identical to a detector with 100% accuracy. These proposed Neural-Kalman schemes can work as novel trackers for multipath channels, since they are robust to wide variations in the probabilities of tap birth and death. Such robustness suggests a single, low-complexity NNKF could be reusable over di erent tap indices and communication environments. Furthermore, a di erent kind of abrupt change was proposed and analyzed: energy shifts from one channel tap to adjacent taps (partial tap lateral hops). This Thesis also discusses how to model, detect and track such changes, providing a geometric justi cation for this and additional non-stationary dynamics in vehicular situations, such as road scenarios where re ections on trucks and vans are involved, or the visual appearance/disappearance of drone swarms. An extensive literature review of empirically-backed abrupt-change dynamics in channel modelling/measuring campaigns is included. For this generalized framework of abrupt channel changes that includes partial tap lateral hopping, a neural detector for lateral hops with large energy transfers is introduced. Simulation results suggest the proposed NN architecture might be a feasible lateral hop detector, suitable for integration in NNKF schemes. Finally, the newly found understanding of abrupt changes and the interactions between Kalman lters and neural networks is leveraged to analyze the neural consequences of abrupt changes and brie y sketch a novel, abrupt-change-derived stochastic model for neural intelligence, extract some neuro nancial consequences of unstereotyped abrupt dynamics, and propose a new portfolio-building mechanism in nance: Highly Leveraged Abrupt Bets Against Failing Experts (HLABAFEOs). Some communication-engineering-relevant topics, such as a Bayesian stochastic stereotyper for hopping Linear Gauss-Markov (LGM) models, are discussed in the process. The forecasting problem in the presence of expert disagreements is illustrated with a hopping LGM model and a novel structure for a Bayesian stereotyper is introduced that might eventually solve such problems through bio-inspired, neuroscienti cally-backed mechanisms, like dreaming and surprise (biological Neural-Kalman). A generalized framework for abrupt changes and expert disagreements was introduced with the novel concept of Neural-Kalman Phenomena. This Thesis suggests mathematical (Neural-Kalman Problem Category Conjecture), neuro-evolutionary and social reasons why Neural-Kalman Phenomena might exist and found signi cant evidence for their existence in the areas of neuroscience and nance. Apart from providing speci c examples, practical guidelines and historical (out)performance for some HLABAFEO investing portfolios, this multidisciplinary research suggests that a Neural- Kalman architecture for ever granular stereotyping providing a practical solution for continual learning in the presence of unstereotyped abrupt dynamics would be extremely useful in communications and other continual learning tasks.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretaria: Ana García Armada.- Vocal: José Antonio Portilla Figuera

SRML: Space Radio Machine Learning

Space-based communications systems to be employed by future artificial satellites, or spacecraft during exploration missions, can potentially benefit from software-defined radio adaptation capabilities. Multiple communication requirements could potentially compete for radio resources, whose availability of which may vary during the spacecraft\u27s operational life span. Electronic components are prone to failure, and new instructions will eventually be received through software updates. Consequently, these changes may require a whole new set of near-optimal combination of parameters to be derived on-the-fly without instantaneous human interaction or even without a human in-the-loop. Thus, achieving a sufficiently set of radio parameters can be challenging, especially when the communication channels change dynamically due to orbital dynamics as well as atmospheric and space weather-related impairments. This dissertation presents an analysis and discussion regarding novel algorithms proposed in order to enable a cognition control layer for adaptive communication systems operating in space using an architecture that merges machine learning techniques employing wireless communication principles. The proposed cognitive engine proof-of-concept reasons over time through an efficient accumulated learning process. An implementation of the conceptual design is expected to be delivered to the SDR system located on the International Space Station as part of an experimental program. To support the proposed cognitive engine algorithm development, more realistic satellite-based communications channels are proposed along with rain attenuation synthesizers for LEO orbits, channel state detection algorithms, and multipath coefficients function of the reflector\u27s electrical characteristics. The achieved performance of the proposed solutions are compared with the state-of-the-art, and novel performance benchmarks are provided for future research to reference