End-to-End Data Analytics Framework for 5G Architecture

Data analytics can be seen as a powerful tool for the fifth-generation (5G) communication system to enable the transformation of the envisioned challenging 5G features into a reality. In the current 5G architecture, some first features toward this direction have been adopted by introducing new functions in core and management domains that can either run analytics on collected communication-related data or can enhance the already supported network functions with statistics collection and prediction capabilities. However, possible further enhancements on 5G architecture may be required, which strongly depend on the requirements as set by vertical customers and the network capabilities as offered by the operator. In addition, the architecture needs to be flexible in order to deal with network changes and service adaptations as requested by verticals. This paper explicitly describes the requirements for deploying data analytics in a 5G system and subsequently presents the current status of standardization activities. The main contribution of this paper is the investigation and design of an integrated data analytics framework as a key enabling technology for the service-based architectures (SBAs). This framework introduces new functional entities for application-level, data network, and access-related analytics to be integrated into the already existing analytics functionalities and examines their interactions in a service-oriented manner. Finally, to demonstrate predictive radio resource management, we showcase a particular implementation for application and radio access network analytics, based on a novel database for collecting and analyzing radio measurements

Benefits and limits of machine learning for the implicit coordination on SON functions

Bedingt durch die Einführung neuer Netzfunktionen in den Mobilfunknetzen der nächsten Generation, z. B. Slicing oder Mehrantennensysteme, sowie durch die Koexistenz mehrerer Funkzugangstechnologien, werden die Optimierungsaufgaben äußerst komplex und erhöhen die OPEX (OPerational EXpenditures). Um den Nutzern Dienste mit wettbewerbsfähiger Dienstgüte (QoS) zu bieten und gleichzeitig die Betriebskosten niedrig zu halten, wurde von den Standardisierungsgremien das Konzept des selbstorganisierenden Netzes (SON) eingeführt, um das Netzmanagement um eine Automatisierungsebene zu erweitern. Es wurden dafür mehrere SON-Funktionen (SFs) vorgeschlagen, um einen bestimmten Netzbereich, wie Abdeckung oder Kapazität, zu optimieren. Bei dem konventionellen Entwurf der SFs wurde jede Funktion als Regler mit geschlossenem Regelkreis konzipiert, der ein lokales Ziel durch die Einstellung bestimmter Netzwerkparameter optimiert. Die Beziehung zwischen mehreren SFs wurde dabei jedoch bis zu einem gewissen Grad vernachlässigt. Daher treten viele widersprüchliche Szenarien auf, wenn mehrere SFs in einem mobilen Netzwerk instanziiert werden. Solche widersprüchlichen Funktionen in den Netzen verschlechtern die QoS der Benutzer und beeinträchtigen die Signalisierungsressourcen im Netz. Es wird daher erwartet, dass eine existierende Koordinierungsschicht (die auch eine Entität im Netz sein könnte) die Konflikte zwischen SFs lösen kann. Da diese Funktionen jedoch eng miteinander verknüpft sind, ist es schwierig, ihre Interaktionen und Abhängigkeiten in einer abgeschlossenen Form zu modellieren. Daher wird maschinelles Lernen vorgeschlagen, um eine gemeinsame Optimierung eines globalen Leistungsindikators (Key Performance Indicator, KPI) so voranzubringen, dass die komplizierten Beziehungen zwischen den Funktionen verborgen bleiben. Wir nennen diesen Ansatz: implizite Koordination. Im ersten Teil dieser Arbeit schlagen wir eine zentralisierte, implizite und auf maschinellem Lernen basierende Koordination vor und wenden sie auf die Koordination zweier etablierter SFs an: Mobility Robustness Optimization (MRO) und Mobility Load Balancing (MLB). Anschließend gestalten wir die Lösung dateneffizienter (d. h. wir erreichen die gleiche Modellleistung mit weniger Trainingsdaten), indem wir eine geschlossene Modellierung einbetten, um einen Teil des optimalen Parametersatzes zu finden. Wir nennen dies einen "hybriden Ansatz". Mit dem hybriden Ansatz untersuchen wir den Konflikt zwischen MLB und Coverage and Capacity Optimization (CCO) Funktionen. Dann wenden wir ihn auf die Koordinierung zwischen MLB, Inter-Cell Interference Coordination (ICIC) und Energy Savings (ES) Funktionen an. Schließlich stellen wir eine Möglichkeit vor, MRO formal in den hybriden Ansatz einzubeziehen, und zeigen, wie der Rahmen erweitert werden kann, um anspruchsvolle Netzwerkszenarien wie Ultra-Reliable Low Latency Communications (URLLC) abzudecken.Due to the introduction of new network functionalities in next-generation mobile networks, e.g., slicing or multi-antenna systems, as well as the coexistence of multiple radio access technologies, the optimization tasks become extremely complex, increasing the OPEX (OPerational EXpenditures). In order to provide services to the users with competitive Quality of Service (QoS) while keeping low operational costs, the Self-Organizing Network (SON) concept was introduced by the standardization bodies to add an automation layer to the network management. Thus, multiple SON functions (SFs) were proposed to optimize a specific network domain, like coverage or capacity. The conventional design of SFs conceived each function as a closed-loop controller optimizing a local objective by tuning specific network parameters. However, the relationship among multiple SFs was neglected to some extent. Therefore, many conflicting scenarios appear when multiple SFs are instantiated in a mobile network. Having conflicting functions in the networks deteriorates the users’ QoS and affects the signaling resources in the network. Thus, it is expected to have a coordination layer (which could also be an entity in the network), conciliating the conflicts between SFs. Nevertheless, due to interleaved linkage among those functions, it is complex to model their interactions and dependencies in a closed form. Thus, machine learning is proposed to drive a joint optimization of a global Key Performance Indicator (KPI), hiding the intricate relationships between functions. We call this approach: implicit coordination. In the first part of this thesis, we propose a centralized, fully-implicit coordination approach based on machine learning (ML), and apply it to the coordination of two well-established SFs: Mobility Robustness Optimization (MRO) and Mobility Load Balancing (MLB). We find that this approach can be applied as long as the coordination problem is decomposed into three functional planes: controllable, environmental, and utility planes. However, the fully-implicit coordination comes at a high cost: it requires a large amount of data to train the ML models. To improve the data efficiency of our approach (i.e., achieving good model performance with less training data), we propose a hybrid approach, which mixes ML with closed-form models. With the hybrid approach, we study the conflict between MLB and Coverage and Capacity Optimization (CCO) functions. Then, we apply it to the coordination among MLB, Inter-Cell Interference Coordination (ICIC), and Energy Savings (ES) functions. With the hybrid approach, we find in one shot, part of the parameter set in an optimal manner, which makes it suitable for dynamic scenarios in which fast response is expected from a centralized coordinator. Finally, we present a manner to formally include MRO in the hybrid approach and show how the framework can be extended to cover challenging network scenarios like Ultra-Reliable Low Latency Communications (URLLC)

Advanced heterogeneous video transcoding

PhDVideo transcoding is an essential tool to promote inter-operability between different video communication systems. This thesis presents two novel video transcoders, both operating on bitstreams of the cur- rent H.264/AVC standard. The first transcoder converts H.264/AVC bitstreams to a Wavelet Scalable Video Codec (W-SVC), while the second targets the emerging High Efficiency Video Coding (HEVC). Scalable Video Coding (SVC) enables low complexity adaptation of compressed video, providing an efficient solution for content delivery through heterogeneous networks. The transcoder proposed here aims at exploiting the advantages offered by SVC technology when dealing with conventional coders and legacy video, efficiently reusing information found in the H.264/AVC bitstream to achieve a high rate-distortion performance at a low complexity cost. Its main features include new mode mapping algorithms that exploit the W-SVC larger macroblock sizes, and a new state-of-the-art motion vector composition algorithm that is able to tackle different coding configurations in the H.264/AVC bitstream, including IPP or IBBP with multiple reference frames. The emerging video coding standard, HEVC, is currently approaching the final stage of development prior to standardization. This thesis proposes and evaluates several transcoding algorithms for the HEVC codec. In particular, a transcoder based on a new method that is capable of complexity scalability, trading off rate-distortion performance for complexity reduction, is proposed. Furthermore, other transcoding solutions are explored, based on a novel content-based modeling approach, in which the transcoder adapts its parameters based on the contents of the sequence being encoded. Finally, the application of this research is not constrained to these transcoders, as many of the techniques developed aim to contribute to advance the research on this field, and have the potential to be incorporated in different video transcoding architectures

Sensing and Interactive Intelligence in Mobile Context Aware Systems

The ever increasing capabilities of mobile devices such as smartphones and their ubiquity in daily life has resulted in a large and interesting body of research into context awareness { the `awareness of a situation' { and how it could make people's lives easier. There are, however, diculties involved in realising and implementing context aware systems in the real world; particularly in a mobile environment. To address these diculties, this dissertation tackles the broad problem of designing and implementing mobile context aware systems in the eld. Spanning the elds of Articial Intelligence (AI) and Human Computer Interaction (HCI), the problem is broken down and scoped into two key areas: context sensing and interactive intelligence. Using a simple design model, the dissertation makes a series of contributions within each area in order to improve the knowledge of mobile context aware systems engineering. At the sensing level, we review mobile sensing capabilities and use a case study to show that the everyday calendar is a noisy `sensor' of context. We also show that its `signal', i.e. useful context, can be extracted using logical data fusion with context supplied by mobile devices. For interactive intelligence, there are two fundamental components: the intelligence, which is concerned with context inference and machine learning; and the interaction, which is concerned with user interaction. For the intelligence component, we use the case of semantic place awareness to address the problems of real time context inference and learning on mobile devices. We show that raw device motion { a common metric used in activity recognition research { is a poor indicator of transition between semantically meaningful places, but real time transition detection performance can be improved with the application of basic machine learning and time series processing techniques. We also develop a context inference and learning algorithm that incorporates user feedback into the inference process { a form of active machine learning. We compare various implementations of the algorithm for the semantic place awareness use case, and observe its performance using a simulation study of user feedback. For the interaction component, we study various approaches for eliciting user feedback in the eld. We deploy the mobile semantic place awareness system in the eld and show how dierent elicitation approaches aect user feedback behaviour. Moreover, we report on the user experience of interacting with the intelligent system and show how performance in the eld compares with the earlier simulation. We also analyse the resource usage of the system and report on the use of a simple SMS place awareness application that uses our system. The dissertation presents original research on key components for designing and implementing mobile context aware systems, and contributes new knowledge to the eld of mobile context awareness.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Impact of peer-to-peer trading and flexibility on local energy systems

To meet the 2050 net zero emission targets, energy systems around the globe are being revisited to achieve multi-vector decarbonisation in terms of electricity, transport, heating and cooling. As energy systems become more decentralised and digitised, local energy systems will have greater potential to self-sustain and hence, decrease reliance on fossil-fuelled central generation. While the uptake of electric vehicles, heat pumps, solar and battery systems offer a solution, the increase in electricity demand poses challenges in terms of higher peak demand, imbalance and overloading. Additionally, the current energy market structure prevents these assets in the distribution network from reaching their true techno-economic potential in flexibility services and energy trading. Peer-to-peer energy trading and community-level control algorithms achieve better matching of local demand and supply through the use of transactive energy markets, load shifting and peak shaving techniques. Existing research addresses the challenges of local energy markets and others investigate the effect of increased distributed assets on the network. However, the combined techno-economic effect requires the co-simulation of both market and network levels, coupled with simultaneous system balance, cost and carbon intensity considerations. Using bottom-up coordination and user-centric optimisation, this project investigated the potential of network-aware peer-to-peer trading and community-level control to increase self-sufficiency and self-consumption in energy communities. The techno-economic effects of these strategies are modelled while maintaining user comfort levels and healthy operation of the network and assets. The proposed strategies are evaluated according to their economic benefit, environmental impact and network stress. A case study in Scotland was employed to demonstrate the benefits of peer-to-peer trading and community self-consumption using future projections of demand, generation and storage. Additionally, the concept of energy smart contracts, embedded in blockchains, are proposed and demonstrated to overcome the major challenges of monitoring and contracting. The results indicate benefits for various energy systems stakeholders. Distribution system end-users benefit from lower energy costs while system operators obtain better visibility of the local-level flexibility along with the associated technical challenges in terms of losses, imbalance and loading. From a commercial perspective, community energy companies may utilise this study to inform investment decisions regarding storage, distributed generation and transactive market solutions. Additionally, the insights about the energy smart contracts allow blockchain and relevant technology sectors to recognise the opportunities and challenges of smart contracts and distributed ledger technologies that are specific to the energy sector. On the broader scale, energy system operators, regulators and high-level decision-makers can compare the simulated impact of community-led energy transition on the net zero goals with large-scale top-down initiatives

Graduate course catalog (Florida International University). [2002-2003]

https://digitalcommons.fiu.edu/catalogs/1008/thumbnail.jp

Graduate course catalog (Florida International University). [2001-2002]

https://digitalcommons.fiu.edu/catalogs/1009/thumbnail.jp