QoS constrained cellular ad hoc augmented networks

In this dissertation, based on different design criteria, three novel quality of service (QoS) constrained cellular ad hoc augmented network (CAHAN) architectures are proposed for next generation wireless networks. The CAHAN architectures have a hybrid architecture, in which each MT of CDMA cellular networks has ad hoc communication capability. The CAHAN architectures are an evolutionary approach to conventional cellular networks. The proposed architectures have good system scalability and high system reliability. The first proposed architecture is the QoS constrained minimum-power cellular ad hoc augmented network architecture (QCMP CAHAN). The QCMP CAHAN can find the optimal minimum-power routes under the QoS constraints (bandwidth, packet-delay, or packet-error-rate constraint). The total energy consumed by the MTs is lower in the case of QCMP CAHAN than in the case of pure cellular networks. As the ad hoc communication range of each MT increases, the total transmitted power in QCMP CAHAN decreases. However, due to the increased number of hops involved in information delivery between the source and the destination, the end-to-end delay increases. The maximum end-to-end delay will be limited to a specified tolerable value for different services. An MT in QCMP CAHAN will not relay any messages when its ad hoc communication range is zero, and if this is the case for all MTs, then QCMP CAHAN reduces to the traditional cellular network. A QoS constrained network lifetime extension cellular ad hoc augmented network architecture (QCLE CAHAN) is proposed to achieve the maximum network lifetime under the QoS constraints. The network lifetime is higher in the case of QCLE CAHAN than in the case of pure cellular networks or QCMP CAHAN. In QCLE CAHAN, a novel QoS-constrained network lifetime extension routing algorithm will dynamically select suitable ad-hoc-switch-to-cellular points (ASCPs) according to the MT remaining battery energy such that the selection will balance all the MT battery energy and maximizes the network lifetime. As the number of ASCPs in an ad hoc subnet decreases, the network lifetime will be extended. Maximum network lifetime can be increased until the end-to-end QoS in QCLE CAHAN reaches its maximum tolerable value. Geocasting is the mechanism to multicast messages to the MTs whose locations lie within a given geographic area (target area). Geolocation-aware CAHAN (GA CAHAN) architecture is proposed to improve total transmitted power expended for geocast services in cellular networks. By using GA CAHAN for geocasting, saving in total transmitted energy can be achieved as compared to the case of pure cellular networks. When the size of geocast target area is large, GA CAHAN can save larger transmitted energy

Soft Handoff in MC-CDMA Cellular Networks Supporting Multimedia Services

An adaptive resource reservation and handoff priority scheme, which jointly considers the characteristics from the physical, link and network layers, is proposed for a packet switching Multicode (MC)-CDMA cellular network supporting multimedia applications. A call admission region is derived for call admission control (CAC) and handoff management with the satisfaction of quality of service (QoS) requirements for all kinds of multimedia traffic, where the QoS parameters include the wireless transmission bit error rate (BER), the packet loss rate (PLR) and delay requirement. The BER requirement is guaranteed by properly arranging simultaneous packet transmissions, whereas the PLR and delay requirements are guaranteed by the proposed packet scheduling and partial packet integration scheme. To give service priority to handoff calls, a threshold-based adaptive resource reservation scheme is proposed on the basis of a practical user mobility model and a proper handoff request prediction scheme. The resource reservation scheme gives handoff calls a higher admission priority over new calls, and is designed to adjust the reservation-request time threshold adaptively according to the varying traffic load. The individual reservation requests form a common reservation pool, and handoff calls are served on a first-come-first-serve basis. By exploiting the transmission rate adaptability of video calls to the available radio resources, the resources freed from rate-adaptive high-quality video calls by service degradation can be further used to prioritize handoff calls. With the proposed resource reservation and handoff priority scheme, the dynamic properties of the system can be closely captured and a better grade of service (GoS) in terms of new call blocking and handoff call dropping probabilities(rates) can be achieved compared to other schemes in literature. Numerical results are presented to show the improvement of the GoS performance and the efficient utilization of the radio resources

Stochastische Analyse und lernbasierte Algorithmen zur Ressourcenbereitstellung in optischen Netzwerken

The unprecedented growth in Internet traffic has driven the innovations in provisioning of optical resources as per the need of bandwidth demands such that the resource utilization and spectrum efficiency could be maximized. With the advent of the next generation flexible optical transponders and switches, the flexible-grid-based elastic optical network (EON) is foreseen as an alternative to the widely deployed fixed-grid-based wavelength division multiplexing networks. At the same time, the flexible resource provisioning also raises new challenges for EONs. One such challenge is the spectrum fragmentation. As network traffic varies over time, spectrum gets fragmented due to the setting up and tearing down of non-uniform bandwidth requests over aligned (i.e., continuous) and adjacent (i.e., contiguous) spectrum slices, which leads to a non-optimal spectrum allocation, and generally results in higher blocking probability and lower spectrum utilization in EONs. To address this issue, the allocation and reallocation of optical resources are required to be modeled accurately, and managed efficiently and intelligently. The modeling of routing and spectrum allocation in EONs with the spectrum contiguity and spectrum continuity constraints is well-investigated, but existing models do not consider the fragmentation issue resulted by these constraints and non-uniform bandwidth demands. This thesis addresses this issue and considers both the constraints to computing exact blocking probabilities in EONs with and without spectrum conversion, and with spectrum reallocation (known as defragmentation) for the first time using the Markovian approach. As the exact network models are not scalable with respect to the network size and capacity, this thesis proposes load-independent and load-dependent approximate models to compute approximate blocking probabilities in EONs. Results show that the connection blocking due to fragmentation can be reduced by using a spectrum conversion or a defragmentation approach, but it can not be eliminated in a mesh network topology. This thesis also deals with the important network resource provisioning task in EONs. To this end, it first presents algorithmic solutions to efficiently allocate and reallocate spectrum resources using the fragmentation factor along spectral, time, and spatial dimensions. Furthermore, this thesis highlights the role of machine learning techniques in alleviating issues in static provisioning of optical resources, and presents two use-cases: handling time-varying traffic in optical data center networks, and reducing energy consumption and allocating spectrum proportionately to traffic classes in fiber-wireless networks.Die flexible Nutzung des Spektrums bringt in Elastischen Optischen Netze (EON) neue Herausforderungen mit sich, z.B., die Fragmentierung des Spektrums. Die Fragmentierung entsteht dadurch, dass die Netzwerkverkehrslast sich im Laufe der Zeit ändert und so wird das Spektrum aufgrund des Verbindungsaufbaus und -abbaus fragmentiert. Das für eine Verbindung notwendige Spektrum wird durch aufeinander folgende (kontinuierliche) und benachbarte (zusammenhängende) Spektrumsabschnitte (Slots) gebildet. Dies führt nach den zahlreichen Reservierungen und Freisetzungen des Spektrums zu einer nicht optimalen Zuordnung, die in einer höheren Blockierungs-wahrscheinlichkeit der neuen Verbindungsanfragen und einer geringeren Auslastung von EONs resultiert. Um dieses Problem zu lösen, müssen die Zuweisung und Neuzuordnung des Spektrums in EONs genau modelliert und effizient sowie intelligent verwaltet werden. Diese Arbeit beschäftigt sich mit dem Fragmentierungsproblem und berücksichtigt dabei die beiden Einschränkungen: Kontiguität und Kontinuität. Unter diesen Annahmen wurden analytische Modelle zur Berechnung einer exakten Blockierungswahrscheinlichkeit in EONs mit und ohne Spektrumskonvertierung erarbeitet. Außerdem umfasst diese Arbeit eine Analyse der Blockierungswahrscheinlichkeit im Falle einer Neuzuordnung des Sprektrums (Defragmentierung). Diese Blockierungsanalyse wird zum ersten Mal mit Hilfe der Markov-Modelle durchgeführt. Da die exakten analytischen Modelle hinsichtlich der Netzwerkgröße und -kapazität nicht skalierbar sind, werden in dieser Dissertation verkehrslastunabhängige und verkehrslastabhängige Approximationsmodelle vorgestellt. Diese Modelle bieten eine Näherung der Blockierungswahrscheinlichkeiten in EONs. Die Ergebnisse zeigen, dass die Blockierungswahrscheinlichkeit einer Verbindung aufgrund von einer Fragmentierung des Spektrums durch die Verwendung einer Spektrumkonvertierung oder eines Defragmentierungsverfahrens verringert werden kann. Eine effiziente Bereitstellung der optischen Netzwerkressourcen ist eine wichtige Aufgabe von EONs. Deswegen befasst sich diese Arbeit mit algorithmischen Lösungen, die Spektrumressource mithilfe des Fragmentierungsfaktors von Spektral-, Zeit- und räumlichen Dimension effizient zuweisen und neu zuordnen. Darüber hinaus wird die Rolle des maschinellen Lernens (ML) für eine verbesserte Bereitstellung der optischen Ressourcen untersucht und das ML basierte Verfahren mit der statischen Ressourcenzuweisung verglichen. Dabei werden zwei Anwendungsbeispiele vorgestellt und analysiert: der Umgang mit einer zeitveränderlichen Verkehrslast in optischen Rechenzentrumsnetzen, und eine Verringerung des Energieverbrauchs und die Zuweisung des Spektrums proportional zu Verkehrsklassen in kombinierten Glasfaser-Funknetzwerken

The new enhancement of UMTS: HSDPA and HSUPA

During the last two decades, the world of the mobile communications grew a lot, as a consequence of the increasing necessity of people to communicate. Now, the mobile communications still need to improve for satisfies the user demands. The new enhancement of UMTS in concrete HSDPA and HSUPA is one of these improvements that the society needs. HSDPA and HSUPA which together are called HSPA, give to the users higher data rates in downlink and uplink. The higher data rates permit to the operators give more different types of services and at the same time with better quality. As a result, people can do several new applications with their mobile terminals like applications that before a computer and internet connection were required, now it is possible to do directly with the mobile terminal. This thesis consists in study these new technologies denominated HSDPA and HSUPA and thus know better the last tendencies in the mobile communications. Also it has a roughly idea about the future tendencies

Convergence of packet communications over the evolved mobile networks; signal processing and protocol performance

In this thesis, the convergence of packet communications over the evolved mobile networks is studied. The Long Term Evolution (LTE) process is dominating the Third Generation Partnership Project (3GPP) in order to bring technologies to the markets in the spirit of continuous innovation. The global markets of mobile information services are growing towards the Mobile Information Society. The thesis begins with the principles and theories of the multiple-access transmission schemes, transmitter receiver techniques and signal processing algorithms. Next, packet communications and Internet protocols are referred from the IETF standards with the characteristics of mobile communications in the focus. The mobile network architecture and protocols bind together the evolved packet system of Internet communications to the radio access network technologies. Specifics of the traffic models are shortly visited for their statistical meaning in the radio performance analysis. Radio resource management algorithms and protocols, also procedures, are covered addressing their relevance for the system performance. Throughout these Chapters, the commonalities and differentiators of the WCDMA, WCDMA/HSPA and LTE are covered. The main outcome of the thesis is the performance analysis of the LTE technology beginning from the early discoveries to the analysis of various system features and finally converging to an extensive system analysis campaign. The system performance is analysed with the characteristics of voice over the Internet and best effort traffic of the Internet. These traffic classes represent the majority of the mobile traffic in the converged packet networks, and yet they are simple enough for a fair and generic analysis of technologies. The thesis consists of publications and inventions created by the author that proposed several improvements to the 3G technologies towards the LTE. In the system analysis, the LTE showed by the factor of at least 2.5 to 3 times higher system measures compared to the WCDMA/HSPA reference. The WCDMA/HSPA networks are currently available with over 400 million subscribers and showing increasing growth, in the meanwhile the first LTE roll-outs are scheduled to begin in 2010. Sophisticated 3G LTE mobile devices are expected to appear fluently for all consumer segments in the following years

Learning for Network Applications and Control

The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements. There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms. In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds. In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics. In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs. We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy. In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling

Multimedia in mobile networks: Streaming techniques, optimization and User Experience

1.UMTS overview and User Experience 2.Streaming Service & Streaming Platform 3.Quality of Service 4.Mpeg-4 5.Test Methodology & testing architecture 6.Conclusion