785 research outputs found
Interference charecterisation, location and bandwidth estimation in emerging WiFi networks
Wireless LAN technology based on the IEEE 802.11 standard, commonly referred
to as WiFi, has been hugely successful not only for the last hop access to the Internet
in home, office and hotspot scenarios but also for realising wireless backhaul in mesh
networks and for point -to -point long- distance wireless communication. This success
can be mainly attributed to two reasons: low cost of 802.11 hardware from reaching
economies of scale, and operation in the unlicensed bands of wireless spectrum.The popularity of WiFi, in particular for indoor wireless access at homes and offices,
has led to significant amount of research effort looking at the performance issues
arising from various factors, including interference, CSMA/CA based MAC protocol
used by 802.11 devices, the impact of link and physical layer overheads on application
performance, and spatio-temporal channel variations. These factors affect the performance
of applications and services that run over WiFi networks. In this thesis, we
experimentally investigate the effects of some of the above mentioned factors in the
context of emerging WiFi network scenarios such as multi- interface indoor mesh networks,
802.11n -based WiFi networks and WiFi networks with virtual access points
(VAPs). More specifically, this thesis comprises of four experimental characterisation
studies: (i) measure prevalence and severity of co- channel interference in urban WiFi
deployments; (ii) characterise interference in multi- interface indoor mesh networks;
(iii) study the effect of spatio-temporal channel variations, VAPs and multi -band operation
on WiFi fingerprinting based location estimation; and (iv) study the effects of
newly introduced features in 802.11n like frame aggregation (FA) on available bandwidth
estimation.With growing density of WiFi deployments especially in urban areas, co- channel
interference becomes a major factor that adversely affects network performance. To
characterise the nature of this phenomena at a city scale, we propose using a new measurement
methodology called mobile crowdsensing. The idea is to leverage commodity
smartphones and the natural mobility of people to characterise urban WiFi co- channel
interference. Specifically, we report measurement results obtained for Edinburgh, a
representative European city, on detecting the presence of deployed WiFi APs via the
mobile crowdsensing approach. These show that few channels in 2.4GHz are heavily
used and there is hardly any activity in the 5GHz band even though relatively it
has a greater number of available channels. Spatial analysis of spectrum usage reveals
that co- channel interference among nearby APs operating in the same channel
can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city -scale are similar
to those of WiFi deployments in public spaces of different indoor environments. We
validate our approach in comparison with wardriving, and also show that our findings
generally match with previous studies based on other measurement approaches. As
an application of the mobile crowdsensing based urban WiFi monitoring, we outline a
cloud based WiFi router configuration service for better interference management with
global awareness in urban areas.For mesh networks, the use of multiple radio interfaces is widely seen as a practical
way to achieve high end -to -end network performance and better utilisation of
available spectrum. However this gives rise to another type of interference (referred to
as coexistence interference) due to co- location of multiple radio interfaces. We show
that such interference can be so severe that it prevents concurrent successful operation
of collocated interfaces even when they use channels from widely different frequency
bands. We propose the use of antenna polarisation to mitigate such interference and
experimentally study its benefits in both multi -band and single -band configurations. In
particular, we show that using differently polarised antennas on a multi -radio platform
can be a helpful counteracting mechanism for alleviating receiver blocking and adjacent
channel interference phenomena that underlie multi -radio coexistence interference.
We also validate observations about adjacent channel interference from previous
studies via direct and microscopic observation of MAC behaviour.Location is an indispensable information for navigation and sensing applications.
The rapidly growing adoption of smartphones has resulted in a plethora of mobile
applications that rely on position information (e.g., shopping apps that use user position
information to recommend products to users and help them to find what they want
in the store). WiFi fingerprinting is a popular and well studied approach for indoor
location estimation that leverages the existing WiFi infrastructure and works based on
the difference in strengths of the received AP signals at different locations. However,
understanding the impact of WiFi network deployment aspects such as multi -band
APs and VAPs has not received much attention in the literature. We first examine the
impact of various aspects underlying a WiFi fingerprinting system. Specifically, we
investigate different definitions for fingerprinting and location estimation algorithms
across different indoor environments ranging from a multi- storey office building to
shopping centres of different sizes. Our results show that the fingerprint definition
is as important as the choice of location estimation algorithm and there is no single
combination of these two that works across all environments or even all floors of a given environment. We then consider the effect of WiFi frequency bands (e.g., 2.4GHz
and 5GHz) and the presence of virtual access points (VAPs) on location accuracy with
WiFi fingerprinting. Our results demonstrate that lower co- channel interference in the
5GHz band yields more accurate location estimation. We show that the inclusion of
VAPs has a significant impact on the location accuracy of WiFi fingerprinting systems;
we analyse the potential reasons to explain the findings.End -to -end available bandwidth estimation (ABE) has a wide range of uses, from
adaptive application content delivery, transport-level transmission rate adaptation and
admission control to traffic engineering and peer node selection in peer -to- peer /overlay
networks [ 1, 2]. Given its importance, it has been received much research attention in
both wired data networks and legacy WiFi networks (based on 802.11 a/b /g standards),
resulting in different ABE techniques and tools proposed to optimise different criteria
and suit different scenarios. However, effects of new MAC/PHY layer enhancements
in new and next generation WiFi networks (based on 802.11n and 802.11ac
standards) have not been studied yet. We experimentally find that among different
new features like frame aggregation, channel bonding and MIMO modes (spacial division
multiplexing), frame aggregation has the most harmful effect as it has direct
effect on ABE by distorting the measurement probing traffic pattern commonly used
to estimate available bandwidth. Frame aggregation is also specified in both 802.11n
and 802.1 lac standards as a mandatory feature to be supported. We study the effect of
enabling frame aggregation, for the first time, on the performance of the ABE using an
indoor 802.11n wireless testbed. The analysis of results obtained using three tools -
representing two main Probe Rate Model (PRM) and Probe Gap Model (PGM) based
approaches for ABE - led us to come up with the two key principles of jumbo probes
and having longer measurement probe train sizes to counter the effects of aggregating
frames on the performance of ABE tools. Then, we develop a new tool, WBest+ that
is aware of the underlying frame aggregation by incorporating these principles. The
experimental evaluation of WBest+ shows more accurate ABE in the presence of frame
aggregation.Overall, the contributions of this thesis fall in three categories - experimental
characterisation, measurement techniques and mitigation/solution approaches for performance
problems in emerging WiFi network scenarios. The influence of various factors
mentioned above are all studied via experimental evaluation in a testbed or real - world setting. Specifically, co- existence interference characterisation and evaluation
of available bandwidth techniques are done using indoor testbeds, whereas characterisation of urban WiFi networks and WiFi fingerprinting based location estimation are
carried out in real environments. New measurement approaches are also introduced
to aid better experimental evaluation or proposed as new measurement tools. These
include mobile crowdsensing based WiFi monitoring; MAC/PHY layer monitoring of
co- existence interference; and WBest+ tool for available bandwidth estimation. Finally,
new mitigation approaches are proposed to address challenges and problems
identified throughout the characterisation studies. These include: a proposal for crowd - based interference management in large scale uncoordinated WiFi networks; exploiting
antenna polarisation diversity to remedy the effects of co- existence interference
in multi -interface platforms; taking advantage of VAPs and multi -band operation for
better location estimation; and introducing the jumbo frame concept and longer probe
train sizes to improve performance of ABE tools in next generation WiFi networks
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Leveraging Backscatter for Ultra-low Power Wireless Sensing Systems
The past few years have seen a dramatic growth in wireless sensing systems, with millions of wirelessly connected sensors becoming first-class citizens of the Internet. The number of wireless sensing devices is expected to surpass 6.75 billion by 2017, more than the world\u27s population as well as the combined market of smartphones, tablets, and PCs. However, its growth faces two pressing challenges: battery energy density and wireless radio power consumption. Battery energy density looms as a fundamental limiting factor due to slow improvements over the past several decades (3x over 22 years). Wireless radio power consumption is another key challenge because high-speed wireless communication is often far more expensive energy-wise than computation, storage and sensing. To make matters worse, wireless sensing devices are generating an increasing amount of data. These challenges raise a fundamental question --- how should we power and communicate with wireless sensing devices. More specifically, instead of using batteries, can we leverage other energy sources to reduce, if not eliminate, the dependence on batteries? Similarly, instead of optimizing existing wireless radios, can we fundamentally change how radios transmit wireless signals to achieve lower power consumption? A promising technique to address these questions is backscatter --- a primitive that enables RF energy harvesting and ultra-low-power wireless communication. Backscatter has the potential to reduce dependence on batteries because it can obtain energy by rectifying the wireless signals transmitted by a backscatter reader. Backscatter can also work by reflecting existing wireless signals (WiFi, BLE) when these are available nearby. Because signal reflection only consumes uWs of power, backscatter can enable ultra-low-power wireless communication. However, the use of backscatter for communicating with wireless sensing devices presents several challenges. First, decreasing RF power across distance limits the operational range of micro-powered backscatter devices. This raises the question of how to maintain a communication link with a backscatter device despite tiny amount of harvested power. Second, even though the backscatter RF front-end is extremely power-efficient, the computational and sensing overhead on backscatter sensors limit its ability to operate with a few micro-Watts of power. Such overhead is a negligible factor of overall power consumption for platforms where radio power consumption is high (e.g. WiFi or Bluetooth based devices). However, it becomes the bottleneck for backscatter based platforms. Third, backscatter readers are not currently deployed in existing indoor environments to provide a continuous carrier for carrying backscattered information. As a result, backscatter deployment is not yet widespread. This thesis addresses these challenges by making the following contributions. First, we design a network stack that enables continuous operation despite decreasing harvested power across distance by employing an OS abstraction --- task fragmentation. We show that such a network stack enables packet transfer even when the whole system is powered by a 3cmx3cm solar panel under natural indoor light condition. Second, we design a hardware architecture that minimizes the computational overhead of backscatter to enable over 1Mbps backscatter transmission while consuming less than 100uWs of power, a two order of magnitude improvement over the state-of-the-art. Finally, we design a system that can leverage both ambient WiFi and BLE signals for backscatter. Our empirical evaluation shows that we can backscatter 500bps data on top of a WiFi stream and 50kbps data on top of a Bluetooth stream when the backscatter device is 3m away from the commercial WiFi and Bluetooth receivers
Cloud and mobile infrastructure monitoring for latency and bandwidth sensitive applications
This PhD thesis involves the study of cloud computing infrastructures (from the networking perspective) to assess the feasibility of applications gaining increasing popularity over recent years, including multimedia and telemedicine applications, demanding low, bounded latency and sufficient bandwidth.
I also focus on the case of telemedicine, where remote imaging applications (for example, telepathology or telesurgery) need to achieve a low and stable latency for the remote transmission of images, and also for the remote control of such equipment.
Another important use case for telemedicine is denoted as remote computation, which involves the offloading of image processing to help diagnosis; also in this case, bandwidth and latency requirements should be enforced to ensure timely results, although they are less strict compared to the previous scenario.
Nowadays, the capability of gaining access to IT resources in a rapid and on-demand fashion, according to a pay-as-you-go model, has made the cloud computing a key-enabler for innovative multimedia and telemedicine services.
However, the partial obscurity of cloud performance, and also security concerns are still hindering the adoption of cloud infrastructure.
To ensure that the requirements of applications running on the cloud are satisfied, there is the need to design and evaluate proper methodologies, according to the metric of interest.
Moreover, some kinds of applications have specific requirements that cannot be satisfied by the current cloud infrastructure.
In particular, since the cloud computing involves communication to remote servers, two problems arise: firstly, the core network infrastructure can be overloaded, considering the massive amount of data that has to flow through it to allow clients to reach the datacenters; secondly, the latency resulting from this remote interaction between clients and servers is increased.
For these, and many other cases also beyond the field of telemedicine, the Edge and Fog computing paradigms were introduced.
In these new paradigms, the IT resources are deployed not only in the core cloud datacenters, but also at the edge of the network, either in the telecom operator access network or even leveraging other users' devices.
The proximity of resources to end-users allows to alleviate the burden on the core network and at the same time to reduce latency towards users.
Indeed, the latency from users to remote cloud datacenters encompasses delays from the access and core networks, as well as the intra-datacenter delay.
Therefore, this latency is expected to be higher than that required to interconnect users to edge servers, which in the envisioned paradigm are deployed in the access network, that is, nearby final users.
Therefore, the edge latency is expected to be reduced to only a portion of the overall cloud delay.
Moreover, the edge and central resources can be used in conjunction, and therefore attention to core cloud monitoring is of capital importance even when edge architectures will have a widespread adoption, which is not the case yet.
While a lot of research work has been presented for monitoring several network-related metrics, such as bandwidth, latency, jitter and packet loss, less attention was given to the monitoring of latency in cloud and edge cloud infrastructures.
In detail, while some works target cloud-latency monitoring, the evaluation is lacking a fine-grained analysis of latency considering spatial and temporal trends.
Furthermore, the widespread adoption of mobile devices, and the Internet of Things paradigm further accelerate the shift towards the cloud paradigm for the additional benefits it can provide in this context, allowing energy savings and augmenting the computation capabilities of these devices, creating a new scenario denoted as mobile cloud.
This scenario poses additional challenges for its bandwidth constraints, accentuating the need for tailored methodologies that can ensure that the crucial requirements of the aforementioned applications can be met by the current infrastructure.
In this sense, there is still a gap of works monitoring bandwidth-related metrics in mobile networks, especially when performing in-the-wild assessment targeting actual mobile networks and operators.
Moreover, even the few works testing real scenarios typically consider only one provider in one country for a limited period of time, lacking an in-depth assessment of bandwidth variability over space and time.
In this thesis, I therefore consider monitoring methodologies for challenging scenarios, focusing on latency perceived by customers of public cloud providers, and bandwidth in mobile broadband networks.
Indeed, as described, achieving low latency is a critical requirement for core cloud infrastructures, while providing enough bandwidth is still challenging in mobile networks compared to wired settings, even with the adoption of 4G mobile broadband networks, expecting to overcome this issue only with the widespread availability of 5G connections (with half of total traffic expected to come from 5G networks by 2026).
Therefore, in the research activities carried on during my PhD, I focused on monitoring latency and bandwidth on cloud and mobile infrastructures, assessing to which extent the current public cloud infrastructure and mobile network make multimedia and telemedicine applications (as well as others having similar requirements) feasible
Cellular and Wi-Fi technologies evolution: from complementarity to competition
This PhD thesis has the characteristic to span over a long time because while working on it, I was working as a research engineer at CTTC with highly demanding development duties. This has delayed the deposit more than I would have liked. On the other hand, this has given me the privilege of witnessing and studying how wireless technologies have been evolving over a decade from 4G to 5G and beyond.
When I started my PhD thesis, IEEE and 3GPP were defining the two main wireless technologies at the time, Wi-Fi and LTE, for covering two substantially complementary market targets. Wi-Fi was designed to operate mostly indoor, in unlicensed spectrum, and was aimed to be a simple and cheap technology. Its primary technology for coexistence was based on the assumption that the spectrum on which it was operating was for free, and so it was designed with interference avoidance through the famous CSMA/CA protocol. On the other hand, 3GPP was designing technologies for licensed spectrum, a costly kind of spectrum. As a result, LTE was designed to take the best advantage of it while providing the best QoE in mainly outdoor scenarios.
The PhD thesis starts in this context and evolves with these two technologies. In the first chapters, the thesis studies radio resource management solutions for standalone operation of Wi-Fi in unlicensed and LTE in licensed spectrum. We anticipated the now fundamental machine learning trend by working on machine learning-based radio resource management solutions to improve LTE and Wi-Fi operation in their respective spectrum. We pay particular attention to small cell deployments aimed at improving the spectrum efficiency in licensed spectrum, reproducing small range scenarios typical of Wi-Fi settings.
IEEE and 3GPP followed evolving the technologies over the years: Wi-Fi has grown into a much more complex and sophisticated technology, incorporating the key features of cellular technologies, like HARQ, OFDMA, MU-MIMO, MAC scheduling and spatial reuse. On the other hand, since Release 13, cellular networks have also been designed for unlicensed spectrum. As a result, the two last chapters of this thesis focus on coexistence scenarios, in which LTE needs to be designed to coexist with Wi-Fi fairly, and NR, the radio access for 5G, with Wi-Fi in 5 GHz and WiGig in 60 GHz. Unlike LTE, which was adapted to operate in unlicensed spectrum, NR-U is natively designed with this feature, including its capability to operate in unlicensed in a complete standalone fashion, a fundamental new milestone for cellular. In this context, our focus of analysis changes. We consider that these two technological families are no longer targeting complementarity but are now competing, and we claim that this will be the trend for the years to come.
To enable the research in these multi-RAT scenarios, another fundamental result of this PhD thesis, besides the scientific contributions, is the release of high fidelity models for LTE and NR and their coexistence with Wi-Fi and WiGig to the ns-3 open-source community. ns-3 is a popular open-source network simulator, with the characteristic to be multi-RAT and so naturally allows the evaluation of coexistence scenarios between different technologies. These models, for which I led the development, are by academic citations, the most used open-source simulation models for LTE and NR and havereceived fundings from industry (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) and federal agencies (NIST, LLNL) over the years.Aquesta tesi doctoral té la caracterÃstica d’allargar-se durant un llarg perÃode de temps ja que mentre treballava en ella, treballava com a enginyera investigadora a CTTC amb tasques de desenvolupament molt exigents. Això ha endarrerit el dipositar-la més del que m’hagués agradat. D’altra banda, això m’ha donat el privilegi de ser testimoni i estudiar com han evolucionat les tecnologies sense fils durant més d’una dècada des del 4G fins al 5G i més enllà . Quan vaig començar la tesi doctoral, IEEE i 3GPP estaven definint les dues tecnologies sense fils principals en aquell moment, Wi-Fi i LTE, que cobreixen dos objectius de mercat substancialment complementaris. Wi-Fi va ser dissenyat per funcionar principalment en interiors, en espectre sense llicència, i pretenia ser una tecnologia senzilla i barata. La seva tecnologia primà ria per a la convivència es basava en el supòsit que l’espectre en el que estava operant era de franc, i, per tant, es va dissenyar simplement evitant interferències a través del famós protocol CSMA/CA. D’altra banda, 3GPP estava dissenyant tecnologies per a espectres amb llicència, un tipus d’espectre costós. Com a resultat, LTE està dissenyat per treure’n el mà xim profit alhora que proporciona el millor QoE en escenaris principalment a l’aire lliure. La tesi doctoral comença amb aquest context i evoluciona amb aquestes dues tecnologies. En els primers capÃtols, estudiem solucions de gestió de recursos de radio per a operacions en espectre de Wi-Fi sense llicència i LTE amb llicència. Hem anticipat l’actual tendència fonamental d’aprenentatge automà tic treballant solucions de gestió de recursos de radio basades en l’aprenentatge automà tic per millorar l’LTE i Wi-Fi en el seu espectre respectiu. Prestem especial atenció als desplegaments de cèl·lules petites destinades a millorar la eficiència d’espectre llicenciat, reproduint escenaris de petit abast tÃpics de la configuració Wi-Fi. IEEE i 3GPP van seguir evolucionant les tecnologies al llarg dels anys: El Wi-Fi s’ha convertit en una tecnologia molt més complexa i sofisticada, incorporant les caracterÃstiques clau de les tecnologies cel·lulars, com ara HARQ i la reutilització espacial. D’altra banda, des de la versió 13, també s’han dissenyat xarxes cel·lulars per a espectre sense llicència. Com a resultat, els dos darrers capÃtols d’aquesta tesi es centren en aquests escenaris de convivència, on s’ha de dissenyar LTE per conviure amb la Wi-Fi de manera justa, i NR, l’accés a la radio per a 5G amb Wi-Fi a 5 GHz i WiGig a 60 GHz. A diferència de LTE, que es va adaptar per funcionar en espectre sense llicència, NR-U està dissenyat de forma nativa amb aquesta caracterÃstica, inclosa la seva capacitat per operar sense llicència de forma autònoma completa, una nova fita fonamental per al mòbil. En aquest context, el nostre focus d’anà lisi canvia. Considerem que aquestes dues famÃlies de tecnologia ja no estan orientades cap a la complementarietat, sinó que ara competeixen, i afirmem que aquesta serà el tendència per als propers anys. Per permetre la investigació en aquests escenaris multi-RAT, un altre resultat fonamental d’aquesta tesi doctoral, a més de les aportacions cientÃfiques, és l’alliberament de models d’alta fidelitat per a LTE i NR i la seva coexistència amb Wi-Fi a la comunitat de codi obert ns-3. ns-3 és un popular simulador de xarxa de codi obert, amb la caracterÃstica de ser multi-RAT i, per tant, permet l’avaluació de manera natural d’escenaris de convivència entre diferents tecnologies. Aquests models, pels quals he liderat el desenvolupament, són per cites acadèmiques, els models de simulació de codi obert més utilitzats per a LTE i NR i que han rebut finançament de la indústria (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) i agències federals (NIST, LLNL) al llarg dels anys.Esta tesis doctoral tiene la caracterÃstica de extenderse durante mucho tiempo porque mientras trabajaba en ella, trabajaba como ingeniera de investigación en CTTC con tareas de desarrollo muy exigentes. Esto ha retrasado el depósito más de lo que me hubiera gustado. Por otro lado,
gracias a ello, he tenido el privilegio de presenciar y estudiar como las tecnologÃas inalámbricas
han evolucionado durante una década, de 4G a 5G y más allá.
Cuando comencé mi tesis doctoral, IEEE y 3GPP estaban definiendo las dos principales
tecnologÃas inalámbricas en ese momento, Wi-Fi y LTE, cumpliendo dos objetivos de mercado
sustancialmente complementarios. Wi-Fi fue diseñado para funcionar principalmente en
interiores, en un espectro sin licencia, y estaba destinado a ser una tecnologÃa simple y barata.
Su tecnologÃa primaria para la convivencia se basaba en el supuesto en que el espectro en
el que estaba operando era gratis, y asà fue diseñado simplemente evitando interferencias a
través del famoso protocolo CSMA/CA. Por otro lado, 3GPP estaba diseñando tecnologÃas
para espectro con licencia, un tipo de espectro costoso. Como resultado, LTE está diseñado
para aprovechar el espectro al máximo proporcionando al mismo tiempo el mejor QoE en
escenarios principalmente al aire libre.
La tesis doctoral parte de este contexto y evoluciona con estas dos tecnologÃas. En los
primeros capÃtulos, estudiamos las soluciones de gestión de recursos de radio para operación
en espectro Wi-Fi sin licencia y LTE con licencia. Anticipamos la tendencia ahora fundamental
de aprendizaje automático trabajando en soluciones de gestión de recursos de radio para
mejorar LTE y funcionamiento deWi-Fi en su respectivo espectro. Prestamos especial atención
a las implementaciones de células pequeñas destinadas a mejorar la eficiencia de espectro
licenciado, reproduciendo los tÃpicos escenarios de rango pequeño de la configuración Wi-Fi.
IEEE y 3GPP siguieron evolucionando las tecnologÃas a lo largo de los años: Wi-Fi
se ha convertido en una tecnologÃa mucho más compleja y sofisticada, incorporando las
caracterÃsticas clave de las tecnologÃas celulares, como HARQ, OFDMA, MU-MIMO, MAC
scheduling y la reutilización espacial. Por otro lado, desde la Release 13, también se han
diseñado redes celulares para espectro sin licencia. Como resultado, los dos últimos capÃtulos
de esta tesis se centran en estos escenarios de convivencia, donde LTE debe diseñarse para
coexistir con Wi-Fi de manera justa, y NR, el acceso por radio para 5G con Wi-Fi en 5 GHz
y WiGig en 60 GHz. A diferencia de LTE, que se adaptó para operar en espectro sin licencia,
NR-U está diseñado de forma nativa con esta función, incluyendo su capacidad para operar
sin licencia de forma completamente independiente, un nuevo hito fundamental para los
celulares. En este contexto, cambia nuestro enfoque de análisis. Consideramos que estas dos
familias tecnológicas ya no tienen como objetivo la complementariedad, sino que ahora están
compitiendo, y afirmamos que esta será la tendencia para los próximos años.
Para permitir la investigación en estos escenarios de múltiples RAT, otro resultado fundamental
de esta tesis doctoral, además de los aportes cientÃficos, es el lanzamiento de modelos de alta
fidelidad para LTE y NR y su coexistencia con Wi-Fi y WiGig a la comunidad de código
abierto de ns-3. ns-3 es un simulador popular de red de código abierto, con la caracterÃstica
de ser multi-RAT y asÃ, naturalmente, permite la evaluación de escenarios de convivencia
entre diferentes tecnologÃas. Estos modelos, para los cuales lideré el desarrollo, son por citas
académicas, los modelos de simulación de código abierto más utilizados para LTE y NR y
han recibido fondos de la industria (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) y
agencias federales (NIST, LLNL) a lo largo de los años.Postprint (published version
FLINT: A Platform for Federated Learning Integration
Cross-device federated learning (FL) has been well-studied from algorithmic,
system scalability, and training speed perspectives. Nonetheless, moving from
centralized training to cross-device FL for millions or billions of devices
presents many risks, including performance loss, developer inertia, poor user
experience, and unexpected application failures. In addition, the corresponding
infrastructure, development costs, and return on investment are difficult to
estimate. In this paper, we present a device-cloud collaborative FL platform
that integrates with an existing machine learning platform, providing tools to
measure real-world constraints, assess infrastructure capabilities, evaluate
model training performance, and estimate system resource requirements to
responsibly bring FL into production. We also present a decision workflow that
leverages the FL-integrated platform to comprehensively evaluate the trade-offs
of cross-device FL and share our empirical evaluations of business-critical
machine learning applications that impact hundreds of millions of users.Comment: Preprint for MLSys 202
Design, analysis and optimization of visible light communications based indoor access systems for mobile and internet of things applications
Demands for indoor broadband wireless access services are expected to outstrip the spectrum capacity in the near-term spectrum crunch . Deploying additional femtocells to address spectrum crunch is cost-inefficient due to the backhaul challenge and the exorbitant system maintenance. According to an Alcatel-Lucent report, most mobile Internet access traffic happens indoors. To alleviate the spectrum crunch and the backhaul challenge problems, visible light communication (VLC) emerges as an attractive candidate for indoor wireless access in the 5G architecture. In particular, VLC utilizes LED or fluorescent lamps to send out imperceptible flickering light that can be captured by a smart phone camera or photodetector. Leveraging power line communication and the available indoor infrastructure, VLC can be utilized with a small one-time cost. VLC also facilitates the great advantage of being able to jointly perform illumination and communications. Integration of VLC into the existing indoor wireless access networks embraces many challenges, such as lack of uplink infrastructure, excessive delay caused by blockage in heterogeneous networks, and overhead of power consumption. In addition, applying VLC to Internet-of-Things (IoT) applications, such as communication and localization, faces the challenges including ultra-low power requirement, limited modulation bandwidth, and heavy computation and sensing at the device end. In this dissertation, to overcome the challenges of VLC, a VLC enhanced WiFi system is designed by incorporating VLC downlink and WiFi uplink to connect mobile devices to the Internet. To further enhance robustness and throughput, WiFi and VLC are aggregated in parallel by leveraging the bonding technique in Linux operating system. Based on dynamic resource allocation, the delay performance of heterogeneous RF-VLC network is analyzed and evaluated for two different configurations - aggregation and non-aggregation. To mitigate the power consumption overhead of VLC, a problem of minimizing the total power consumption of a general multi-user VLC indoor network while satisfying users traffic demands and maintaining an acceptable level of illumination is formulated. The optimization problem is solved by the efficient column generation algorithm. With ultra-low power consumption, VLC backscatter harvests energy from indoor light sources and transmits optical signals by modulating the reflected light from a reflector. A novel pixelated VLC backscatter is proposed and prototyped to address the limited modulation bandwidth by enabling more advanced modulation scheme than the state-of-the-art on-off keying (OOK) scheme and allowing for the first time orthogonal multiple access. VLC-based indoor access system is also suitable for indoor localization due to its unique properties, such as utilization of existing ubiquitous lighting infrastructure, high location and orientation accuracy, and no interruption to RF-based devices. A novel retroreflector-based visible light localization system is proposed and prototyped to establish an almost zero-delay backward channel using a retroreflector to reflect light back to its source. This system can localize passive IoT devices without requiring computation and heavy sensing (e.g., camera) at the device end
Contributions to QoS and energy efficiency in wi-fi networks
The Wi-Fi technology has been in the recent years fostering the proliferation of attractive mobile computing devices with broadband capabilities. Current
Wi-Fi radios though severely impact the battery duration of these devices thus limiting their potential applications.
In this thesis we present a set of contributions that address the challenge of increasing energy efficiency in Wi-Fi networks. In particular, we consider
the problem of how to optimize the trade-off between performance and energy effciency in a wide variety of use cases and applications. In this context, we introduce novel energy effcient algorithms for real-time and data applications, for distributed and centralized Wi-Fi QoS and power saving protocols and for Wi-Fi stations and Access Points.
In addition, the di¿erent algorithms presented in this thesis adhere to the following design guidelines: i) they are implemented entirely at layer two, and
can hence be easily re-used in any device with a Wi-Fi interface, ii) they do not require modi¿cations to current 802.11 standards, and can hence be readily deployed in existing Wi-Fi devices, and iii) whenever possible they favor client side solutions, and hence mobile computing devices implementing them can benefit from an increased energy efficiency regardless of the Access Point they connect to. Each of our proposed algorithms is thoroughly evaluated by means of both theoretical analysis and packet level simulations. Thus, the contributions presented in this thesis provide a realistic set of tools to improve energy efficiency in current Wi-Fi networks
Real-Time Localization Using Software Defined Radio
Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system
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