11 research outputs found

    Performance Evaluation over Indoor Channels of an Unsupervised Decision-Aided Method for OSTBC Systems

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    Abstract. Unsupervised algorithms can be used in digital communications to estimate the channel at the receiver without using pilot symbols, thus obtaining a considerable improvement in terms of data rate, spectral efficiency, and energy consumption. Unfortunately, the computational load is considerably high since they require to estimate Higher Order Statistics. For addressing this issue, it has been recently presented a decision-aided channel estimation strategy, which implemented a decision rule to determine if a new channel estimate was required or not. If channel estimation is not needed, a previous estimate was used to recover the transmitted signals. Based on this idea, we propose a lowercomplexity decision criterion and we evaluate its performance over realworld indoor channels measured using a hardware platform working at the Industrial, Scientific and Medical band at 5 GHz

    Detection of channel variations to improve channel estimation methods

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    “The final publication is available at Springer via http://dx.doi.org/[10.1007/s00034-014-9767-8]”[Abstract] In current digital communication systems, channel information is typically acquired by supervised approaches that use pilot symbols included in the transmit frames. Given that pilot symbols do not convey user data, they penalize throughput spectral efficiency, and transmit energy consumption of the system. Unsupervised channel estimation algorithms could be used to mitigate the aforementioned drawbacks although they present higher computational complexity than that offered by supervised ones. This paper proposes a simple decision method suitable for slowly varying channels to determine whether the channel has suffered a significant variation, which requires to estimate the matrix of the recently changed channel. Otherwise, a previous estimate is used to recover the transmitted symbols. The main advantage of this method is that the decision criterion is only based on information acquired during the time frame synchronization, which is carried out at the receiver. We show that the proposed criterion provides a considerable improvement of computational complexity for both supervised and unsupervised methods, without incurring in a penalization in terms of symbol error ratio. Specifically, we consider systems that make use of the popular Alamouti code. Performance evaluation is accomplished by means of simulated channels as well as making use of indoor wireless channels measured using a testbed

    Blind channel estimation for space-time block codes : novel methods and performance Studies

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    [abstract] This work is based on a study of blind source separation techniques in order to estimate coe cients in transmission systems using Alamouti codi cation with two transmit antennas and one receive antenna. Most of present standards include pilot symbols to estimate the channel in reception. Since these symbols do not deliver user's data, their use decrease transferring quantity and also the system capacity. On the other hand, algorithms of blind separation are less precise when estimating channel coe cients than those supervised, but achieving a higher transferring rate. In this work we will deal with Alamouti codi cation system as a typical problem of blind sources separation where the signals transmitted and the channel coe cients must be estimated according to lineal and instantaneous mixtures (observations). Orthogonal structure required by Alamouti codi cation allows us to solve this problem by decomposing eigenvalues and eigenvectors of matrices calculated from di erent statistics of the observations. These algorithms could be classi ed as those using second order statistics and those using higher order statistics. Algorithms based on second order statistics work with correlation matrix of observations. They are computationally less expensive, but require a lineal precoder in order to balance the power of the signals transmitted. One of our contributions is being able to determine in an empirical way how the power decompensation should be done in order to reduce the proabibility of error in the system. On the other hand, algorithms dealing with high level statistics are based on diagonalize one or several high level cumulant matrices deriving into a major computational cost in the receiver. As an advantage we must point out that they do not require to include a lineal precoder to do the power decompensation. In this work we will prove that the output of these techniques depends on the level of eigenvalue of the diagonalized matrix spreading. This idea will be used by us in order to achieve the optimal cumulant matrix and also to propose a new algorithm that increases the output in relation to those already proposed by other authors. Another important contribution of this present study is to propose a detailed comparison between channel estimation techniques in simulated scenarios, considering channels with Rayleigh and Rice distribution, and in real scenarios in ISM of 2.4 GHz band, by using a MIMO testbed developed in Universidade da Coruña. [Resumen] En este trabajo se realiza un estudio de técnicas de separación ciega de fuentes para la estimación de los coeficientes en sistemas de transmisión que emplean la codificación de Alamouti con 2 antenas transmisoras y 1 antena receptora. La mayoría de los estándares actuales incluyen símbolos piloto para estimar el canal en recepción. Dado que estos símbolos no transportan datos del usuario, su utilización decrementa la tasa de transferencia y degrada el rendimiento del sistema. Por otro lado, los algoritmos de separación ciega son menos precisos en la estimación de los coeficientes de canal que los supervisados pero consiguen una tasa de transferencia mayor. En el presente trabajo, modelaremos el sistema de codificación de Alamouti como un problema típico de separación ciega de fuentes donde las se~nales transmitidas y los coeficientes del canal deben ser estimados a partir de mezclas lineales e instantáneas (observaciones). La estructura ortogonal impuesta por la codificación de Alamouti permite resolver este problema mediante la descomposición de autovalores y autovectores de matrices calculadas a partir de diferentes estadísticos de las observaciones. Estos algoritmos pueden ser clasificados en aquellos que utilizan estadísticos de segundo orden y aquellos que emplean estadísticos de orden superior. Los algoritmos que emplean estadísticos de segundo orden trabajan con la matriz de correlación de las observaciones, son computacionalmente poco costosos pero requieren de un precodificador lineal para descompensar la potencia de las se~nales transmitidas. Una de nuestras aportaciones es la de determinar de forma empírica cómo debe realizarse la descompesación de potencia de cara a reducir la probabilidad de error del sistema. Por otro lado, los algoritmos que trabajan con estadísticos de orden superior se basan en diagonalizar una o varias matrices de cumulantes de orden superior, lo que conlleva un mayor coste computacional en el receptor. Como ventaja debe resaltarse que no requieren incluir un precodificador lineal que realice la descompensación de potencia. En este trabajo mostraremos que el rendimiento de estas técnicas depende del grado de dispersión de los autovalores de la matriz que se diagonaliza. Utilizaremos esta idea para obtener la matriz de cumulantes óptima y para formular un nuevo algoritmo que supera en rendimiento a los propuestos previamente por otros autores. Otra aportación relevante del presente trabajo es presentar una detallada comparación de las técnicas de estimación de canal en entornos simulados, considerando canales con ditribución Rayleigh y Rice, y en entornos reales en la banda ISM de 2.4 GHz mediante el empleo de una plataforma de transmisión MIMO desarrollada en la Universidade da Coruña

    Proceedings of the 2021 Symposium on Information Theory and Signal Processing in the Benelux, May 20-21, TU Eindhoven

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    Identification through Finger Bone Structure Biometrics

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    Finger Vein Verification with a Convolutional Auto-encoder

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    Channelization, Link Adaptation and Multi-antenna Techniques for OFDM(A) Based Wireless Systems

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    Intelligent Circuits and Systems

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    ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

    Studies on Sensor Aided Positioning and Context Awareness

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    This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context
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