33 research outputs found
A hybrid intelligent model for network selection in the industrial Internet of Things
Industrial Internet of Things (IIoT) plays an important role in increasing productivity and efficiency in heterogeneous wireless networks. However, different domains such as industrial wireless scenarios, small cell domains and vehicular ad hoc networks (VANET) require an efficient machine learning/intelligent algorithm to process the vertical handover decision that can maintain mobile terminals (MTs) in the preferable networks for a sufficient duration of time. The preferred quality of service parameters can be differentiated from all the other MTs. Hence, in this paper, the problem with the vertical handoff (VHO) decision is articulated as the process of the Markov decision aimed to maximize the anticipated total rewards as well as to minimize the handoffsâ average count. A rewards function is designed to evaluate the QoS at the point of when the connections take place, as that is where the policy decision for a stationary deterministic handoff can be established. The proposed hybrid model merges the biogeography-based optimization (BBO) with the Markov decision process (MDP). The MDP is utilized to establish the radio access technology (RAT) selectionâs probability that behaves as an input to the BBO process. Therefore, the BBO determines the best RAT using the described multi-point algorithm in the heterogeneous network. The numerical findings display the superiority of this paperâs proposed schemes in comparison with other available algorithms. The findings shown that the MDP-BBO algorithm is able to outperform other algorithms in terms of number of handoffs, bandwidth availability, and decision delays. Our algorithm displayed better expected total rewards as well as a reduced average account of handoffs compared to current approaches. Simulation results obtained from Monte-Carlo experiments prove validity of the proposed model
Quality of experience aware network selection model for service provisioning in heterogeneous network
Heterogeneous wireless networks (HWNs) are capable of integrating the different radio access technologies that make it possible to connect mobile users based on the performance parameters. Further quality of service (QoS) is one of the major topics for HWNs, moreover existing radio access technology (RAT) methodology are designed to provide network QoS criteria. However, limited work has been carried out for the RAT selection mechanism considering user QoS preference and existing models are developed based on the multi-mode terminal under a given minimal density network. For overcoming research issues this paper present quality of experience (QoE) RAT (QOE-RAT) selection methodology, incorporating both network performance criteria and user preference considering multiple call and multi-mode HWNs environment. First, this paper presents fuzzy preference aware weight (FPAW) and multi-mode terminal preference aware TOPSIS (MMTPA-TOPSIS) for choosing the best RAT for gaining multi-services. Experiment outcomes show the QOE-RAT selection method achieves much superior packet transmission outcomes when compared with state-of-art Rat selection methodologies
Intelligent Technique for Seamless Vertical Handover in Vehicular Networks
Seamless mobility is a challenging issue in the area of research of vehicular networks that are supportive of various applications dealing with the intelligent transportation system (ITS). The conventional mobility management plans for the Internet and the mobile ad hoc network (MANET) is unable to address the needs of the vehicular network and there is severe performance degradation because of the vehicular networksâ unique characters such as high mobility. Thus, vehicular networks require seamless mobility designs that especially developed for them. This research provides an intelligent algorithm in providing seamless mobility using the media independent handover, MIH (IEEE 802.21), over heterogeneous networks with different access technologies such as Worldwide Interoperability for Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), as well as the Universal Mobile Telecommunications System (UMTS) for improving the quality of service (QoS) of the mobile services in the vehicular networks. The proposed algorithm is a hybrid model which merges the biogeography-based optimization or BBO with the Markov chain. The findings of this research show that our method within the given scenario can meet the requirements of the application as well as the preferences of the users
Self-Organized Coverage and Capacity Optimization for Cellular Mobile Networks
ï»żDie zur ErfĂŒllung der zu erwartenden Steigerungen ĂŒbertragener
Datenmengen notwendige gröĂere HeterogenitĂ€t und steigende Anzahl von
Zellen werden in der Zukunft zu einer deutlich höheren KomplexitÀt bei
Planung und Optimierung von Funknetzen fĂŒhren. ZusĂ€tzlich erfordern
rĂ€umliche und zeitliche Ănderungen der Lastverteilung eine dynamische
Anpassung von Funkabdeckung und -kapazitÀt
(Coverage-Capacity-Optimization, CCO). Aktuelle Planungs- und
Optimierungsverfahren sind hochgradig von menschlichem Einfluss abhÀngig,
was sie zeitaufwÀndig und teuer macht. Aus diesen Grnden treffen AnsÀtze
zur besseren Automatisierung des Netzwerkmanagements sowohl in der
Industrie, als auch der Forschung auf groes
Interesse.Selbstorganisationstechniken (SO) haben das Potential, viele der
aktuell durch Menschen gesteuerten AblÀufe zu automatisieren. Ihnen wird
daher eine zentrale Rolle bei der Realisierung eines einfachen und
effizienten Netzwerkmanagements zugeschrieben. Die vorliegende Arbeit
befasst sich mit selbstorganisierter Optimierung von Abdeckung und
ĂbertragungskapazitĂ€t in Funkzellennetzwerken. Der Parameter der Wahl
hierfĂŒr ist die Antennenneigung. Die zahlreichen vorhandenen AnsĂ€tze
hierfĂŒr befassen sich mit dem Einsatz heuristischer Algorithmen in der
Netzwerkplanung. Im Gegensatz dazu betrachtet diese Arbeit den verteilten
Einsatz entsprechender Optimierungsverfahren in den betreffenden
Netzwerkknoten. Durch diesen Ansatz können zentrale Fehlerquellen (Single
Point of Failure) und Skalierbarkeitsprobleme in den kommenden heterogenen
Netzwerken mit hoher Knotendichte vermieden werden.Diese Arbeit stellt
einen "Fuzzy Q-Learning (FQL)"-basierten Ansatz vor, ein einfaches
Maschinenlernverfahren mit einer effektiven Abstraktion kontinuierlicher
Eingabeparameter. Das CCO-Problem wird als Multi-Agenten-Lernproblem
modelliert, in dem jede Zelle versucht, ihre optimale Handlungsstrategie
(d.h. die optimale Anpassung der Antennenneigung) zu lernen. Die
entstehende Dynamik der Interaktion mehrerer Agenten macht die
Fragestellung interessant. Die Arbeit betrachtet verschiedene Aspekte des
Problems, wie beispielsweise den Unterschied zwischen egoistischen und
kooperativen Lernverfahren, verteiltem und zentralisiertem Lernen, sowie
die Auswirkungen einer gleichzeitigen Modifikation der Antennenneigung auf
verschiedenen Knoten und deren Effekt auf die Lerneffizienz.Die
LeistungsfÀhigkeit der betrachteten Verfahren wird mittels eine
LTE-Systemsimulators evaluiert. Dabei werden sowohl gleichmĂ€Ăig verteilte
Zellen, als auch Zellen ungleicher GröĂe betrachtet. Die entwickelten
AnsÀtze werden mit bekannten Lösungen aus der Literatur verglichen. Die
Ergebnisse zeigen, dass die vorgeschlagenen Lösungen effektiv auf
Ănderungen im Netzwerk und der Umgebung reagieren können. Zellen stellen
sich selbsttÀtig schnell auf AusfÀlle und Inbetriebnahmen benachbarter
Systeme ein und passen ihre Antennenneigung geeignet an um die
Gesamtleistung des Netzes zu verbessern. Die vorgestellten Lernverfahren
erreichen eine bis zu 30 Prozent verbesserte Leistung als bereits bekannte
AnsĂ€tze. Die Verbesserungen steigen mit der NetzwerkgröĂe.The challenging task of cellular network planning and optimization will
become more and more complex because of the expected heterogeneity and
enormous number of cells required to meet the traffic demands of coming
years. Moreover, the spatio-temporal variations in the traffic patterns of
cellular networks require their coverage and capacity to be adapted
dynamically. The current network planning and optimization procedures are
highly manual, which makes them very time consuming and resource
inefficient. For these reasons, there is a strong interest in industry and
academics alike to enhance the degree of automation in network management.
Especially, the idea of Self-Organization (SO) is seen as the key to
simplified and efficient cellular network management by automating most of
the current manual procedures. In this thesis, we study the self-organized
coverage and capacity optimization of cellular mobile networks using
antenna tilt adaptations. Although, this problem is widely studied in
literature but most of the present work focuses on heuristic algorithms for
network planning tool automation. In our study we want to minimize this
reliance on these centralized tools and empower the network elements for
their own optimization. This way we can avoid the single point of failure
and scalability issues in the emerging heterogeneous and densely deployed
networks.In this thesis, we focus on Fuzzy Q-Learning (FQL), a machine
learning technique that provides a simple learning mechanism and an
effective abstraction level for continuous domain variables. We model the
coverage-capacity optimization as a multi-agent learning problem where each
cell is trying to learn its optimal action policy i.e. the antenna tilt
adjustments. The network dynamics and the behavior of multiple learning
agents makes it a highly interesting problem. We look into different
aspects of this problem like the effect of selfish learning vs. cooperative
learning, distributed vs. centralized learning as well as the effect of
simultaneous parallel antenna tilt adaptations by multiple agents and its
effect on the learning efficiency.We evaluate the performance of the
proposed learning schemes using a system level LTE simulator. We test our
schemes in regular hexagonal cell deployment as well as in irregular cell
deployment. We also compare our results to a relevant learning scheme from
literature. The results show that the proposed learning schemes can
effectively respond to the network and environmental dynamics in an
autonomous way. The cells can quickly respond to the cell outages and
deployments and can re-adjust their antenna tilts to improve the overall
network performance. Additionally the proposed learning schemes can achieve
up to 30 percent better performance than the available scheme from
literature and these gains increases with the increasing network size
Differential Evolution in Wireless Communications: A Review
Differential Evolution (DE) is an evolutionary computational
method inspired by the biological processes of evolution and mutation. DE has
been applied in numerous scientific fields. The paper presents a literature review
of DE and its application in wireless communication. The detailed history,
characteristics, strengths, variants and weaknesses of DE were presented. Seven
broad areas were identified as different domains of application of DE in wireless
communications. It was observed that coverage area maximisation and energy
consumption minimisation are the two major areas where DE is applied.
Others areas are quality of service, updating mechanism where candidate positions
learn from a large diversified search region, security and related field applications.
Problems in wireless communications are often modelled as multiobjective
optimisation which can easily be tackled by the use of DE or hybrid of
DE with other algorithms. Different research areas can be explored and DE will
continue to be utilized in this contex
A comprehensive survey on radio resource management in 5G HetNets: current solutions, future trends and open issues
The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and
user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions
for advanced mechanisms are presented
Concurrent Multipath Transfer: Scheduling, Modelling, and Congestion Window Management
Known as smartphones, multihomed devices like the iPhone and BlackBerry can simultaneously connect to Wi-Fi and 4G LTE networks. Unfortunately, due to the architectural constraints of standard transport layer protocols like the transmission control protocol (TCP), an Internet application (e.g., a file transfer) can use only one access network at a time. Due to recent developments, however, concurrent multipath transfer (CMT) using the stream control transmission protocol (SCTP) can enable multihomed devices to exploit additional network resources for transport layer communications.
In this thesis we explore a variety of techniques aimed at CMT and multihomed devices, such as: packet scheduling, transport layer modelling, and resource management. Some of our accomplishments include, but are not limited to: enhanced performance of CMT under delay-based disparity, a tractable framework for modelling the throughput of CMT, a comparison of modelling techniques for SCTP, a new congestion window update policy for CMT, and efficient use of system resources through optimization.
Since the demand for a better communications system is always on the horizon, it is our goal to further the research and inspire others to embrace CMT as a viable network architecture; in hopes that someday CMT will become a standard part of smartphone technology
Benefits and limits of machine learning for the implicit coordination on SON functions
Bedingt durch die EinfĂŒhrung neuer Netzfunktionen in den Mobilfunknetzen der nĂ€chsten Generation, z. B. Slicing oder Mehrantennensysteme, sowie durch die Koexistenz mehrerer Funkzugangstechnologien, werden die Optimierungsaufgaben Ă€uĂerst komplex und erhöhen die OPEX (OPerational EXpenditures). Um den Nutzern Dienste mit wettbewerbsfĂ€higer DienstgĂŒte (QoS) zu bieten und gleichzeitig die Betriebskosten niedrig zu halten, wurde von den Standardisierungsgremien das Konzept des selbstorganisierenden Netzes (SON) eingefĂŒhrt, um das Netzmanagement um eine Automatisierungsebene zu erweitern. Es wurden dafĂŒr mehrere SON-Funktionen (SFs) vorgeschlagen, um einen bestimmten Netzbereich, wie Abdeckung oder KapazitĂ€t, 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 vernachlĂ€ssigt. Daher treten viele widersprĂŒchliche Szenarien auf, wenn mehrere SFs in einem mobilen Netzwerk instanziiert werden. Solche widersprĂŒchlichen Funktionen in den Netzen verschlechtern die QoS der Benutzer und beeintrĂ€chtigen die Signalisierungsressourcen im Netz. Es wird daher erwartet, dass eine existierende Koordinierungsschicht (die auch eine EntitĂ€t im Netz sein könnte) die Konflikte zwischen SFs lösen kann. Da diese Funktionen jedoch eng miteinander verknĂŒpft sind, ist es schwierig, ihre Interaktionen und AbhĂ€ngigkeiten 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). AnschlieĂend 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. SchlieĂlich 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 usersâ 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)
Mobile Ad Hoc Networks
Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and SimulationâDescribes how MANETs operate and perform through simulations and models Communication Protocols of MANETsâPresents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETsâTackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms
Mobile Ad Hoc Networks
Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and SimulationâDescribes how MANETs operate and perform through simulations and models Communication Protocols of MANETsâPresents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETsâTackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms