Hybrid analog-digital transmit beamforming for spectrum sharing backhaul networks

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper deals with the problem of analog-digital transmit beamforming under spectrum sharing constraints for backhaul systems. In contrast to fully digital designs, where the spatial processing is done at baseband unit with all the flexible computational resources of digital processors, analog-digital beamforming schemes require that certain processing is done through analog components, such as phase-shifters or switches. These analog components do not have the same processing flexibility as the digital processor, but on the other hand, they can substantially reduce the cost and complexity of the beamforming solution. This paper presents the joint optimization of the analog and digital parts, which results in a nonconvex, NP-hard, and coupled problem. In order to solve it, an alternating optimization with a penalized convex-concave method is proposed. According to the simulation results, this novel iterative procedure is able to find a solution that behaves close to the fully digital beamforming upper bound scheme.Peer ReviewedPostprint (author's final draft

A collaborative statistical actor-critic learning approach for 6G network slicing control

Artificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital use-cases. In this paper, we propose a novel model-free deep reinforcement learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC) that enables a scalable and farsighted slice performance management in a 6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC targets the optimization of the latency cost under a long-term statistical service-level agreement (SLA). In particular, we consider the Q-th delay percentile SLA metric and enforce some slice-specific preset constraints on it. Moreover, to implement distributed learners, we propose a developed variant of soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we present numerical results to showcase the gain of the adopted approach on our built OpenAI-based network slicing environment and verify the performance in terms of latency, SLA Q-th percentile, and time efficiency. To the best of our knowledge, this is the first work that studies the feasibility of an AI-driven approach for massive network slicing under statistical SLA.This work has been supported in part by the research projects MonB5G (871780), ZEROTO6G, AGAUR (2017-SGR-891), and PROGRESSUS (876868).Peer ReviewedPostprint (author's final draft

Spectrum sharing backhaul satellite-terrestrial systems via analog beamforming

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Current satellite and terrestrial backhaul systems are deployed in disjoint frequency bands. This fact precludes an efficient use of the spectrum and limits the evolution of wireless backhauling networks. In this paper, we propose an interference mitigation technique in order to allow the spectrum coexistence between satellite and terrestrial backhaul links. This interference reliever is implemented at the terrestrial backhaul nodes, which are assumed to be equipped with multiple antennas. Due to the large bandwidth and huge number of antennas required in these systems, we consider pure analog beamforming. Precisely, we assume a phased array beamforming configuration so that the terrestrial backhaul node can only reduce the interference by changing the phases of each beamforming weight. Two cases are considered: the 18 and 28 GHz band where transmit and receive beamforming optimization problems shall be tackled, respectively. In both cases, the optimization problem results in a nonconvex problem that we propose to solve via two alternative convex approximation methods. These two approaches are evaluated and they present less than 1 dB array gain loss with respect to the upper bound solution. Finally, the spectral efficiency gains of the proposed spectrum sharing scenarios are validated in numerical simulations.Peer ReviewedPostprint (published version

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Subset selection in signal processing using sparsity-inducing norms

This dissertation deals with different subset selection problems in wireless communications systems. These type of problems have a combinatorial nature, which makes them computationally intractable for medium and large-scale sizes. In particular, two different types of problems are addressed in this thesis: cardinality minimization and cardinality-constrained problems. Different mathematical relaxations are proposed with the aim of obtaining algorithms that approximately solve the proposed problems with a tractable computational cost. The first part of the dissertation deals with the angle of arrival estimation in an antenna array and falls within the so-called sparse signal representation framework. A simple, fast and accurate algorithm is proposed for finding the angles of arrival of multiple sources that impinge on an array of antennas. In contrast to other methods in the literature, the considered technique is not based on ad-hoc hyperparameters and does not require the previous knowledge of the number of incoming sources or a previous initialization. The second part of the thesis addresses the selection of the appropriate subset of cooperative nodes in dense relay-assisted wireless networks and constitutes the main focus of the research activities carried out in this thesis. In order to cope with the huge data traffic in the next generation of wireless networks, the number of access nodes and communication links will be densified, having as a result, an increase of the network complexity and its optimization. Within this framework, subset selection problems naturally arise to reduce the overall system management. The activation of many relay links, in dense relay-assisted wireless networks, is impractical due to the communications and processing overhead required to maintain the synchronization amongst all the spatially distributed nodes in the wireless network, which makes the network complexity unaffordable. The selection of the most suitable subset of spatially distributed relays in this context, is a key issue, since it has a dramatic effect in the overall system performance. In particular, the thesis addresses the joint distributed beamforming optimization and relay subset assignment in a multi-user scenario with non-orthogonal transmission and in a scenario with a single source-destination pair. Different design criteria are analyzed, all of them lead to challenging combinatorial nonlinear problems, which are non-convex and non-smooth. Dealing with the multiple relay selection in an ad-hoc wireless network with one source-destination, a new algorithm is proposed for finding the best subset of cooperative relays, and their beamforming weights, so that the SNR is maximized at the destination terminal. This problem is addressed taking into account per-relay power constraints and second-order channel state information. In this context, a sub-optimal method, based on a semidefinite programming relaxation, is proposed. It achieves a near-optimal performance with a reduced computational complexity. The joint relay assignment and distributed beamforming optimization in multi-user wireless relay networks deserves a special attention. Two major problems are addressed: i) the selection of the minimum number of cooperative nodes that guarantees some predefined Quality of Service (QoS) requirements at the destination nodes and; ii) the selection of the best subset of K relays that minimizes the total relay transmit power, satisfying QoS constraints at the destinations. The mathematical formulations of these problems involve non-convex objective functions coupled with non-convex constraints. They are fit into the DC optimization framework and solved using novel path-following methods based on the penalty convex-concave procedure. The proposed techniques exhibit a low complexity and are able to achieve high-quality solutions close to the global optimum.Esta tesis aborda diversos problemas de selección de subconjuntos en comunicaciones inalámbricas. Este tipo de problemas tiene una naturaleza combinatoria que los hace computacionalmente intratables. En concreto, se consideran dos tipos de problemas: i) la minimización de la cardinalidad y ii) la optimización de problemas con restricciones de cardinalidad. Se proponen diversas relajaciones matemáticas, con el fin de desarrollar algoritmos capaces de obtener un rendimiento casi óptimo y una baja complejidad computacional. La primera parte de la tesis se centra en el problema de estimación de los ángulos de llegada en una agrupación de antenas y se enmarca dentro de los llamados problemas de representación sparse. En este contexto, se propone un algoritmo rápido, preciso y simple para la estimación de las direcciones de llegada en una agrupación de antenas. Al contrario que otras técnicas en la literatura, el método propuesto, no se basa en hiperparámetros y no requiere el conocimiento a priori del número de fuentes o una inicialización previa. La segunda parte de la tesis aborda el problema de selección del mejor subconjunto de nodos cooperativos en una red densa de relays inalámbricos. Con el fin de lidiar con el inmenso tráfico de datos en las próximas generaciones de redes inalámbricas, la densidad del número de puntos de acceso y de conexiones aumentará, teniendo como resultado un incremento de la complejidad de la red y de su optimización. Dentro de este marco, los problemas de selección de subconjuntos aparecen de modo natural con el fin de reducir la complejidad de la gestión de la red. La activación de un gran número de terminales, en redes densas de nodos cooperativos, no es factible debido al gran intercambio de información adicional que se requiere entre los terminales, que se encuentran espacialmente distribuidos en diferentes localizaciones. En este contexto, la selección del mejor subconjunto de estos nodos es de gran importancia dado el elevado impacto que tiene en el rendimiento del sistema. Esta tesis aborda la optimización conjunta del conformador de haz (beamformer) y la selección del mejor subconjunto nodos en diversos escenarios cooperativos. Se analizan diversos criterios de diseño, todos ellos conducen a complejos problemas combinatorios no lineales y no convexos. Dentro del contexto de la selección de múltiples nodos en redes inalámbricas cooperativas con un par origen-destino, se propone un algoritmo para encontrar el mejor subconjunto de terminales cooperativos y sus respectivos pesos, de manera que la SNR se maximice en el nodo de destino. Este problema se trata teniendo en cuenta restricciones individuales de potencia en los nodos cooperativos e información estadística de segundo orden de los canales inalámbricos de la red. En este contexto, se presenta un algoritmo con una baja complejidad y un rendimiento cercano al óptimo. La optimización conjunta de los pesos del conformador distribuido y la selección del subconjunto de nodos cooperativos en redes inalámbricas multiusuario merece una atención especial. En esta tesis se abordan dos problemas relevantes: 1) la selección del mínimo número de nodos cooperativos capaces de garantizar una cierta calidad de servicio en los nodos destino y; 2) la selección del mejor subconjunto de K terminales repetidores que minimiza la potencia total transmitida, cumpliendo con ciertas restricciones de calidad de servicio en los nodos destino. La formulación matemática de estos problemas involucra funciones objetivo no convexas acopladas con restricciones que tampoco son convexas. Para aliviar la elevada complejidad de estos problemas se proponen nuevos algoritmos con una baja carga computacional y un rendimiento casi óptimo.Postprint (published version

Sparse covariance fitting for source location

[ANGLÈS] This master thesis proposes a new algorithm for finding the angles of arrival of multiple uncorrelated sources impinging on a uniform linear array of sensors. The method is based on sparse signal representation and does not require either the knowledge of the number of the sources or a previous initialization. The proposed technique considers a covariance matrix model based on overcomplete basis representation and tries to fit the unknown signal powers to the sample covariance matrix.[CASTELLA] Este documento propone un nuevo algoritmo para encontrar los ángulos de llegada de diversas fuentes incorreladas que inciden sobre un array lineal de sensores. El método está basado en sparse signal representation y no requiere ni un conocimiento "a priori" del número de fuentes ni inicialización previa. La técnica propuesta considera un modelo de la matriz de covarianza basada en bases sobrecompletas e intenta estimar la potencia de las señales incidentes a partir de la sample covariance matrix

Sparse covariance fitting for source location

[ANGLÈS] This master thesis proposes a new algorithm for finding the angles of arrival of multiple uncorrelated sources impinging on a uniform linear array of sensors. The method is based on sparse signal representation and does not require either the knowledge of the number of the sources or a previous initialization. The proposed technique considers a covariance matrix model based on overcomplete basis representation and tries to fit the unknown signal powers to the sample covariance matrix.[CASTELLA] Este documento propone un nuevo algoritmo para encontrar los ángulos de llegada de diversas fuentes incorreladas que inciden sobre un array lineal de sensores. El método está basado en sparse signal representation y no requiere ni un conocimiento "a priori" del número de fuentes ni inicialización previa. La técnica propuesta considera un modelo de la matriz de covarianza basada en bases sobrecompletas e intenta estimar la potencia de las señales incidentes a partir de la sample covariance matrix

Sparse covariance fitting for source location

[ANGLÈS] This master thesis proposes a new algorithm for finding the angles of arrival of multiple uncorrelated sources impinging on a uniform linear array of sensors. The method is based on sparse signal representation and does not require either the knowledge of the number of the sources or a previous initialization. The proposed technique considers a covariance matrix model based on overcomplete basis representation and tries to fit the unknown signal powers to the sample covariance matrix.[CASTELLA] Este documento propone un nuevo algoritmo para encontrar los ángulos de llegada de diversas fuentes incorreladas que inciden sobre un array lineal de sensores. El método está basado en sparse signal representation y no requiere ni un conocimiento "a priori" del número de fuentes ni inicialización previa. La técnica propuesta considera un modelo de la matriz de covarianza basada en bases sobrecompletas e intenta estimar la potencia de las señales incidentes a partir de la sample covariance matrix

Hybrid analog-digital transmit beamforming for spectrum sharing backhaul networks

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper deals with the problem of analog-digital transmit beamforming under spectrum sharing constraints for backhaul systems. In contrast to fully digital designs, where the spatial processing is done at baseband unit with all the flexible computational resources of digital processors, analog-digital beamforming schemes require that certain processing is done through analog components, such as phase-shifters or switches. These analog components do not have the same processing flexibility as the digital processor, but on the other hand, they can substantially reduce the cost and complexity of the beamforming solution. This paper presents the joint optimization of the analog and digital parts, which results in a nonconvex, NP-hard, and coupled problem. In order to solve it, an alternating optimization with a penalized convex-concave method is proposed. According to the simulation results, this novel iterative procedure is able to find a solution that behaves close to the fully digital beamforming upper bound scheme.Peer Reviewe