312 research outputs found
Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT
Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability
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Improving next-generation wireless network performance and reliability with deep learning
A rudimentary question whether machine learning in general, or deep learning in particular, could add to the well-established field of wireless communications, which has been evolving for close to a century, is often raised. While the use of deep learning based methods is likely to help build intelligent wireless solutions, this use becomes particularly challenging for the lower layers in the wireless communication stack. The introduction of the fifth generation of wireless communications (5G) has triggered the demand for “network intelligence” to support its promises for very high data rates and extremely low latency. Consequently, 5G wireless operators are faced with the challenges of network complexity, diversification of services, and personalized user experience. Industry standards have created enablers (such as the network data analytics function), but these enablers focus on post-mortem analysis at higher stack layers and have a periodicity in the time scale of seconds (or larger). The goal of this dissertation is to show a solution for these challenges and how a data-driven approach using deep learning could add to the field of wireless communications. In particular, I propose intelligent predictive and prescriptive abilities to boost reliability and eliminate performance bottlenecks in 5G cellular networks and beyond, show contributions that justify the value of deep learning in wireless communications across several different layers, and offer in-depth analysis and comparisons with baselines and industry standards. First, to improve multi-antenna network reliability against wireless impairments with power control and interference coordination for both packetized voice and beamformed data bearers, I propose the use of a joint beamforming, power control, and interference coordination algorithm based on deep reinforcement learning. This algorithm uses a string of bits and logic operations to enable simultaneous actions to be performed by the reinforcement learning agent. Consequently, a joint reward function is also proposed. I compare the performance of my proposed algorithm with the brute force approach and show that similar performance is achievable but with faster run-time as the number of transmit antennas increases. Second, in enhancing the performance of coordinated multipoint, I propose the use of deep learning binary classification to learn a surrogate function to trigger a second transmission stream instead of depending on the popular signal to interference plus noise measurement quantity. This surrogate function improves the users' sum-rate through focusing on pre-logarithmic terms in the sum-rate formula, which have larger impact on this rate. Third, performance of band switching can be improved without the need for a full channel estimation. My proposal of using deep learning to classify the quality of two frequency bands prior to granting the band switching leads to a significant improvement in users' throughput. This is due to the elimination of the industry standard measurement gap requirement—a period of silence where no data is sent to the users so they could measure the frequency bands before switching. In this dissertation, a group of algorithms for wireless network performance and reliability for downlink are proposed. My results show that the introduction of user coordinates enhance the accuracy of the predictions made with deep learning. Also, the choice of signal to interference plus noise ratio as the optimization objective may not always be the best choice to improve user throughput rates. Further, exploiting the spatial correlation of channels in different frequency bands can improve certain network procedures without the need for perfect knowledge of the per-band channel state information. Hence, an understanding of these results help develop novel solutions to enhancing these wireless networks at a much smaller time scale compared to the industry standards todayElectrical and Computer Engineerin
Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing
Wireless communication at the terahertz (THz) frequency bands (0.1-10THz) is
viewed as one of the cornerstones of tomorrow's 6G wireless systems. Owing to
the large amount of available bandwidth, THz frequencies can potentially
provide wireless capacity performance gains and enable high-resolution sensing.
However, operating a wireless system at the THz-band is limited by a highly
uncertain channel. Effectively, these channel limitations lead to unreliable
intermittent links as a result of a short communication range, and a high
susceptibility to blockage and molecular absorption. Consequently, such
impediments could disrupt the THz band's promise of high-rate communications
and high-resolution sensing capabilities. In this context, this paper
panoramically examines the steps needed to efficiently deploy and operate
next-generation THz wireless systems that will synergistically support a
fellowship of communication and sensing services. For this purpose, we first
set the stage by describing the fundamentals of the THz frequency band. Based
on these fundamentals, we characterize seven unique defining features of THz
wireless systems: 1) Quasi-opticality of the band, 2) THz-tailored wireless
architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and
communication systems, 5) PHY-layer procedures, 6) Spectrum access techniques,
and 7) Real-time network optimization. These seven defining features allow us
to shed light on how to re-engineer wireless systems as we know them today so
as to make them ready to support THz bands. Furthermore, these features
highlight how THz systems turn every communication challenge into a sensing
opportunity. Ultimately, the goal of this article is to chart a forward-looking
roadmap that exposes the necessary solutions and milestones for enabling THz
frequencies to realize their potential as a game changer for next-generation
wireless systems.Comment: 26 pages, 6 figure
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
Human Body Scattering Effects at Millimeter Waves Frequencies for Future 5G Systems and Beyond
[ES] Se espera que las futuras comunicaciones móviles experimenten una revolución técnica que vaya más allá de las velocidades de datos de Gbps y reduzca las latencias de las velocidades de datos a niveles muy cercanos al milisegundo. Se han investigado nuevas tecnologías habilitadoras para lograr estas exigentes especificaciones. Y la utilización de las bandas de ondas milimétricas, donde hay mucho espectro disponible, es una de ellas.
Debido a las numerosas dificultades técnicas asociadas a la utilización de esta banda de frecuencias, se necesitan complicados modelos de canal para anticipar las características del canal de radio y evaluar con precisión el rendimiento de los sistemas celulares en milimétricas. En concreto, los modelos de propagación más precisos son los basados en técnicas de trazado de rayos deterministas. Pero estas técnicas tienen el estigma de ser computacionalmente exigentes, y esto dificulta su uso para caracterizar el canal de radio en escenarios interiores complejos y dinámicos. La complejidad de la caracterización de estos escenarios depende en gran medida de la interacción del cuerpo humano con el entorno radioeléctrico, que en las ondas milimétricas suele ser destructiva y muy impredecible.
Por otro lado, en los últimos años, la industria de los videojuegos ha desarrollado potentes herramientas para entornos hiperrealistas, donde la mayor parte de los avances en esta emulación de la realidad tienen que ver con el manejo de la luz. Así, los motores gráficos de estas plataformas se han vuelto cada vez más eficientes para manejar grandes volúmenes de información, por lo que son ideales para emular el comportamiento de la propagación de las ondas de radio, así como para reconstruir un escenario interior complejo. Por ello, en esta Tesis se ha aprovechado la capacidad computacional de este tipo de herramientas para evaluar el canal radioeléctrico milimétricas de la forma más eficiente posible.
Esta Tesis ofrece unas pautas para optimizar la propagación de la señal en milimétricas en un entorno interior dinámico y complejo, para lo cual se proponen tres objetivos principales.
El primer objetivo es evaluar los efectos de dispersión del cuerpo humano cuando interactúa con el canal de propagación. Una vez evaluado, se propuso un modelo matemático y geométrico simplificado para calcular este efecto de forma fiable y rápida. Otro objetivo fue el diseño de un reflector pasivo modular en milimétricas, que optimiza la cobertura en entornos de interior, evitando la interferencia del ser humano en la propagación. Y, por último, se diseñó un sistema de apuntamiento del haz predictivo en tiempo real, para que opere con el sistema de radiación en milimétricas, cuyo objetivo es evitar las pérdidas de propagación causadas por el cuerpo humano en entornos interiores dinámicos y complejos.[CA] S'espera que les futures comunicacions mòbils experimenten una revolució tècnica que vaja més enllà de les velocitats de dades de Gbps i reduïsca les latències de les velocitats de dades a nivells molt pròxims al milisegundo. S'han investigat noves tecnologies habilitadoras per a aconseguir estes exigents especificacions. I la utilització de les bandes d'ones millimètriques, on hi ha molt espectre disponible, és una d'elles.
A causa de les nombroses dificultats tècniques associades a la utilització d'esta banda de freqüències, es necessiten complicats models de canal per a anticipar les característiques del canal de ràdio i avaluar amb precisió el rendiment dels sistemes cellulars en millimètriques. En concret, els models de propagació més precisos són els basats en tècniques de traçat de rajos deterministes. Però estes tècniques tenen l'estigma de ser computacionalment exigents, i açò dificulta el seu ús per a caracteritzar el canal de ràdio en escenaris interiors complexos i dinàmics. La complexitat de la caracterització d'estos escenaris depén en gran manera de la interacció del cos humà amb l'entorn radioelèctric, que en les ones millimètriques sol ser destructiva i molt impredicible.
D'altra banda, en els últims anys, la indústria dels videojocs ha desenrotllat potents ferramentes per a entorns hiperrealistes, on la major part dels avanços en esta emulació de la realitat tenen a veure amb el maneig de la llum. Així, els motors gràfics d'estes plataformes s'han tornat cada vegada més eficients per a manejar grans volums d'informació, per la qual cosa són ideals per a emular el comportament de la propagació de les ones de ràdio, així com per a reconstruir un escenari interior complex. Per això, en esta Tesi s'ha aprofitat la capacitat computacional d'este tipus de ferramentes per a avaluar el canal radioelèctric millimètriques de la manera més eficient possible.
Esta Tesi oferix unes pautes per a optimitzar la propagació del senyal en millimètriques en un entorn interior dinàmic i complex, per a la qual cosa es proposen tres objectius principals. El primer objectiu és avaluar els efectes de dispersió del cos humà quan interactua amb el canal de propagació. Una vegada avaluat, es va proposar un model matemàtic i geomètric simplificat per a calcular este efecte de forma fiable i ràpida. Un altre objectiu va ser el disseny d'un reflector passiu modular en millimètriques, que optimitza la cobertura en entorns d'interior, evitant la interferència del ser humà en la propagació, per a així evitar pèrdues de propagació addicionals. I, finalment, es va dissenyar un sistema d'apuntament del feix predictiu en temps real, perquè opere amb el sistema de radiació en millimètriques, l'objectiu del qual és evitar les pèrdues de propagació causades pel cos humà en entorns interiors dinàmics i complexos.[EN] Future mobile communications are expected to experience a technical revolution that goes beyond Gbps data rates and reduces data rate latencies to levels very close to a millisecond. New enabling technologies have been researched to achieve these demanding specifications. The utilization of mmWave bands, where a lot of spectrum is available, is one of them.
Due to the numerous technical difficulties associated with using this frequency band, complicated channel models are necessary to anticipate the radio channel characteristics and to accurately evaluate the performance of cellular systems in mmWave. In particular, the most accurate propagation models are those based on deterministic ray tracing techniques. But these techniques have the stigma of being computationally intensive, and this makes it difficult to use them to characterize the radio channel in complex and dynamic indoor scenarios. The complexity of characterizing these scenarios depends largely on the interaction of the human body with the radio environment, which at mmWaves is often destructive and highly unpredictable.
On the other hand, in recent years, the video game industry has developed powerful tools for hyper-realistic environments, where most of the progress in this reality emulation has to do with the handling of light. Therefore, the graphic engines of these platforms have become more and more efficient to handle large volumes of information, becoming ideal to emulate the radio wave propagation behavior, as well as to reconstruct a complex interior scenario. Therefore, in this Thesis one has taken advantage of the computational capacity of this type of tools to evaluate the mmWave radio channel in the most efficient way possible. This Thesis offers some guidelines to optimize the signal propagation in mmWaves in a dynamic and complex indoor environment, for which three main objectives are proposed.
The first objective has been to evaluate the scattering effects of the human body when it interacts with the propagation channel. Once evaluated, a simplified mathematical and geometrical model has been proposed to calculate this effect in a reliable and fast way. Another objective has been the design of a modular passive reflector in mmWaves, which optimizes the coverage in indoor environments, avoiding human interference in the propagation, in order to avoid its harmful scattering effects. And finally, a real-time predictive beam steering system has been designed for the mmWaves radiation system, in order to avoid propagation losses caused by the human body in dynamic and complex indoor environments.Romero Peña, JS. (2022). Human Body Scattering Effects at Millimeter Waves Frequencies for Future 5G Systems and Beyond [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19132
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