Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective

This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex optimization for signal processin

Analysis of multiuser MIMO downlink networks using linear transmitter and receivers

In contrast to dirty-paper coding (DPC) which is largely information theoretic, this paper proposes a linear codec that can spatially multiplex the multiuser signals to realize the rich capacity of multiple-input multiple-output (MIMO) downlink broadcast (point-to-multipoint) channels when channel state information (CSI) is available at the transmitter. Assuming single-stream (or single-mode) communication for each user, we develop an iterative algorithm, which is stepwise optimal, to obtain the multiuser antenna weights accomplishing orthogonal space-division multiplexing (OSDM). The steady state solution has a straightforward interpretation and requires only maximal-ratio combiners (MRC) at the mobile stations to capture the optimized spatial modes. Our main contribution is that the proposed scheme can greatly reduce the processing complexity (at least by a factor of the number of base station antennas) while maintaining the same error performance when compared to a recently published OSDM method. Intensive computer simulations show that the proposed scheme promises to provide multiuser diversity in addition to user separation in the spatial domain so that both diversity and multiplexing can be obtained at the same time for multiuser scenario. © 2004 Hindawi Publishing Corporation.published_or_final_versio

New Optimal Approach for the Identification of Takagi-Sugeno Fuzzy Model

A novel optimal method is developed to improve the identification and estimation of Takagi-Sugeno (TS) fuzzy model. The idea comes from the fact that the main drawback of T-S model is that it can not be applied when the membership functions are overlapped by pairs. This limits the application of the T-S model because this type of membership function has been widely used in the stability and controller design of fuzzy systems. It is also very popular in industrial control applications. The method presented here can be considered as a generalized version of T-S fuzzy model with optimized performance in approximating nonlinear functions. Various examples are chosen to show the high function approximation accuracy and fast convergence obtained by applying the proposed method in approximating nonlinear systems locally and globally in comparison with the original T-S model

MPC: Relevant Identification and Control in the Latent Variable Space

Control predictivo basado en modelos (MPC) es una metodología de control ampliamente utilizada en la industria por su habilidad para controlar procesos multivariable con restricciones en sus entradas y sus salidas. Se distinguen dos fases en la implementación de MPC: identificación y control. El propósito de esta tesis es doble: realizar contribuciones en la identificación para MPC y proponer una nueva metodología de control MPC. La respuesta en bucle cerrado de una implementación de MPC depende, en gran medida, de la capacidad de predicción del modelo; luego la identificación del modelo es un punto crucial en MPC y la parte que a menudo exige la mayor parte del tiempo del proyecto. El primer objetivo que cubre la tesis es la identificación para MPC. Puesto que un modelo es una aproximación del comportamiento de un proceso, dicha aproximación se puede hacer teniendo en cuenta el fin que se le va a dar al modelo. En MPC, el modelo se utiliza para realizar predicciones dentro de una ventana futura, luego la identificación para MPC (MRI) tiene en cuenta dicho uso del modelo y considera los errores de predicción dentro de dicha ventana para el ajuste de los parámetros del modelo. En esta tesis, se cubren tres temas dentro de MRI. Primero se define MRI y las distintas formas de abordarlo. Luego se compara en términos de MRI el ajuste de un modelo con múltiples entradas y múltiples salidas con el ajuste de varios modelos con múltiples entradas y una salida concluyendo que el ajuste de un único modelo con múltiples entradas y múltiples salidas proporciona mejores resultados en términos de MRI para horizontes de predicción lo suficientemente grandes. Por último, se propone el algoritmo PLS-PH para implementar MRI con modelos paramétricos en el caso de correlación en los datos de identificación. PLS-PH es un método de optimización numérica por búsqueda lineal basado en PLS (mínimos cuadrados parciales). Se muestra en un ejemplo como PLS-PH es capaz de proporcionar mejores modelos que las técnicas convencionales de MRI en modelos paramétricos en el caso de correlación en los datos de identi ficación. Una vez obtenido el modelo se puede formular el controlador predictivo. En esta tesis se propone LV-MPC, un controlador predictivo para procesos continuos que implementa la optimización en el espacio de las componentes principales.Laurí Pla, D. (2012). MPC: Relevant Identification and Control in the Latent Variable Space [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15178Palanci

An Optimal T-S Model for the Estimation and Identification of Nonlinear Functions

A novel optimal method is developed to improve the identification and estimation of Takagi-Sugeno (TS) fuzzy model. The idea comes from the fact that the main drawback of T-S model is that it can not be applied when the membership functions are overlapped by pairs. This limits the application of the T-S model because this type of membership function has been widely used in the stability and controller design of fuzzy systems. It is also very popular in industrial control applications. The method presented here can be considered as a generalized version of T-S fuzzy model with optimized performance in approximating nonlinear functions. Various examples are chosen to show the high function approximation accuracy and fast convergence obtained by applying the proposed method in approximating nonlinear systems locally and globally in comparison with the original T-S model

Contribution to dimensionality reduction of digital predistorter behavioral models for RF power amplifier linearization

The power efficiency and linearity of radio frequency (RF) power amplifiers (PAs) are critical in wireless communication systems. The main scope of PA designers is to build the RF PAs capable to maintain high efficiency and linearity figures simultaneously. However, these figures are inherently conflicted to each other and system-level solutions based on linearization techniques are required. Digital predistortion (DPD) linearization has become the most widely used solution to mitigate the efficiency versus linearity trade-off. The dimensionality of the DPD model depends on the complexity of the system. It increases significantly in high efficient amplification architectures when considering current wideband and spectrally efficient technologies. Overparametrization may lead to an ill-conditioned least squares (LS) estimation of the DPD coefficients, which is usually solved by employing regularization techniques. However, in order to both reduce the computational complexity and avoid ill-conditioning problems derived from overparametrization, several efforts have been dedicated to investigate dimensionality reduction techniques to reduce the order of the DPD model. This dissertation contributes to the dimensionality reduction of DPD linearizers for RF PAs with emphasis on the identification and adaptation subsystem. In particular, several dynamic model order reduction approaches based on feature extraction techniques are proposed. Thus, the minimum number of relevant DPD coefficients are dynamically selected and estimated in the DPD adaptation subsystem. The number of DPD coefficients is reduced, ensuring a well-conditioned LS estimation while demanding minimum hardware resources. The presented dynamic linearization approaches are evaluated and compared through experimental validation with an envelope tracking PA and a class-J PA The experimental results show similar linearization performance than the conventional LS solution but at lower computational cost.La eficiencia energetica y la linealidad de los amplificadores de potencia (PA) de radiofrecuencia (RF) son fundamentales en los sistemas de comunicacion inalambrica. El principal objetivo a alcanzar en el diserio de amplificadores de radiofrecuencia es lograr simultaneamente elevadas cifras de eficiencia y de linealidad. Sin embargo, estas cifras estan inherentemente en conflicto entre si, y se requieren soluciones a nivel de sistema basadas en tecnicas de linealizacion. La linealizacion mediante predistorsion digital (DPD) se ha convertido en la solucion mas utilizada para mitigar el compromise entre eficiencia y linealidad. La dimension del modelo del predistorsionador DPD depende de la complejidad del sistema, y aumenta significativamente en las arquitecturas de amplificacion de alta eficiencia cuando se consideran los actuales anchos de banda y las tecnologfas espectralmente eficientes. El exceso de parametrizacion puede conducir a una estimacion de los coeficientes DPD, mediante minimos cuadrados (LS), mal condicionada, lo cual generalmente se resuelve empleando tecnicas de regularizacion. Sin embargo, con el fin de reducir la complejidad computacional y evitar dichos problemas de mal acondicionamiento derivados de la sobreparametrizacion, se han dedicado varies esfuerzos para investigar tecnicas de reduccion de dimensionalidad que permitan reducir el orden del modelo del DPD. Esta tesis doctoral contribuye a aportar soluciones para la reduccion de la dimension de los linealizadores DPD para RF PA, centrandose en el subsistema de identificacion y adaptacion. En concrete, se proponen varies enfoques de reduccion de orden del modelo dinamico, basados en tecnicas de extraccion de caracteristicas. El numero minimo de coeficientes DPD relevantes se seleccionan y estiman dinamicamente en el subsistema de adaptacion del DPD, y de este modo la cantidad de coeficientes DPD se reduce, lo cual ademas garantiza una estimacion de LS bien condicionada al tiempo que exige menos recursos de hardware. Las propuestas de linealizacion dinamica presentados en esta tesis se evaluan y comparan mediante validacion experimental con un PA de seguimiento de envolvente y un PA tipo clase J. Los resultados experimentales muestran unos resultados de linealizacion de los PA similares a los obtenidos cuando se em plea la solucion LS convencional, pero con un coste computacional mas reducido.Postprint (published version

Simultaneous Wireless Information and Power Transfer Based on Generalized Triangular Decomposition

The rapidly growing number of wireless devices has raised the need for designing self-sustained wireless systems. Simultaneous wireless information and power transfer (SWIPT) has been advocated as a promising solution. Various approaches have emerged to design wireless systems that enable SWIPT. In this thesis, we propose a novel approach for spatial switching (SS) based SWIPT using the generalized triangular decomposition (GTD) for point-to-point multiple-input-multiple-output (MIMO) systems. The GTD structure allows the transmitter to use the highest gain subchannels jointly for energy and information transmissions and these joint transmissions can be separated at the receiver. We first derive the optimal GTD structure to attain optimal performance in SS based SWIPT systems. This structure is then extended to design three novel transceivers where each transceiver achieves a certain objective and meets specific constraints. The first transceiver focuses on minimizing the total transmitted power while satisfying the energy harvesting and data rate constraints at the receiver. The second transceiver targets the data rate maximization while meeting a certain amount of energy at the receiver. The third transceiver considers the energy harvesting maximization and guarantees to satisfy the required data rate constraint. The proposed transceivers are designed assuming two transmitted power constraints at the transmitter; the instantaneous total transmit power and the limited transmit power per subchannel. For each designed transceiver, optimal and/or suboptimal solutions are developed to obtain joint power allocation and subchannel assignment under a linear energy harvesting model. Additionally, a novel extension to the SS based SWIPT system is proposed considering a non-linear energy harvesting model. Thereafter, the case of maximizing the energy harvesting for a given data rate and instantaneous total transmitted power constraints is studied. A solution is developed that obtains jointly the optimal power allocation and the subchannel assignment alongside the optimal and/or suboptimal split ratios at the energy harvesters. The theoretical and simulation results show that our novel proposed GTD designs for both linear and non-linear energy harvesting models outperform the state-of-the-art singular value decomposition (SVD) based SWIPT designs