486 research outputs found
INTERFERENCE MANAGEMENT IN LTE SYSTEM AND BEYOUND
The key challenges to high throughput in cellular wireless communication system are interference, mobility and bandwidth limitation. Mobility has never been a problem until recently, bandwidth has been constantly improved upon through the evolutions in cellular wireless communication system but interference has been a constant limitation to any improvement that may have resulted from such evolution. The fundamental challenge to a system designer or a researcher is how to achieve high data rate in motion (high speed) in a cellular system that is intrinsically interference-limited.
Multi-antenna is the solution to data on the move and the capacity of multi-antenna system has been demonstrated to increase proportionally with increase in the number of antennas at both transmitter and receiver for point-to-point communications and multi-user environment. However, the capacity gain in both uplink and downlink is limited in a multi-user environment like cellular system by interference, the number of antennas at the base station, complexity and space constraint particularly for a mobile terminal.
This challenge in the downlink provided the motivation to investigate successive interference cancellation (SIC) as an interference management tool LTE system and beyond. The Simulation revealed that ordered successive interference (OSIC) out performs non-ordered successive interference cancellation (NSIC) and the additional complexity is justified based on the associated gain in BER performance of OSIC. The major drawback of OSIC is that it is not efficient in network environment employing power control or power allocation. Additional interference management techniques will be required to fully manage the interference.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
Uplink beamforming for the FDD mode of UTRA
This paper presents some link level simulation results for the evaluation of adaptive antennas in the uplink of the FDD mode of UTRA (UMTS terrestrial radio access). Two families of algorithms were initially considered, the basic difference between them being their ability/disability to suppress the contribution from W-CDMA directional interfering sources. Two distinct schemes were established as representatives for each family and their performance was evaluated in presence of some illustrative interfering scenarios. In the light of the results it is shown that time-reference beamforming algorithms suffer from severe beam pattern distortion effects when applied as such. This in turn causes harsh performance degradation in terms of raw BER, especially at high SINR levels. It is shown that these shortcomings are essentially caused by the uplink multiplexing of the traffic channel, which is seen by the base station as a powerful interfering source coming from the direction of arrival of the desired user.Peer ReviewedPostprint (published version
Analytical modeling of HSUPA-enabled UMTS networks for capacity planning
In recent years, mobile communication networks have experienced significant evolution. The 3G mobile communication system, UMTS, employs WCDMA as the air interface standard, which leads to quite different mobile network planning and dimensioning processes compared with 2G systems. The UMTS system capacity is limited by the received interference at NodeBs due to the unique features of WCDMA, which is denoted as `soft capacity'. Consequently, the key challenge in UMTS radio network planning has been shifted from channel allocation in the channelized 2G systems to blocking and outage probabilities computation under the `cell breathing' effects which are due to the relationship between network coverage and capacity. The interference characterization, especially for the other-cell interference, is one of the most important components in 3G mobile networks planning. This monograph firstly investigates the system behavior in the operation of UMTS uplink, and develops the analytic techniques to model interference and system load as fully-characterized random variables, which can be directly applicable to the performance modeling of such networks. When the analysis progresses from single-cell scenario to multi-cell scenario, as the target SIR oriented power control mechanism is employed for maximum capacity, more sophisticated system operation, `feedback behavior', has emerged, as the interference levels at different cells depend on each other. Such behaviors are also captured into the constructed interference model by iterative and approximation approaches. The models are then extended to cater for the features of the newly introduced HSUPA, which provides enhanced dedicated channels for the packet switched data services such that much higher bandwidth can be achieved for best-effort elastic traffic, which allows network operators to cope with the coexistence of both circuit-switched and packet-switched traffic and guarantee the QoS requirements. During the derivation, we consider various propagation models, traffic models, resource allocation schemes for many possible scenarios, each of which may lead to different analytical models. All the suggested models are validated with either Monte-Carlo simulations or discrete event simulations, where excellent matches between results are always achieved. Furthermore, this monograph studies the optimization-based resource allocation strategies in the UMTS uplink with integrated QoS/best-effort traffic. Optimization techniques, both linear-programming based and non-linear-programming based, are used to determine how much resource should be assigned to each enhanced uplink user in the multi-cell environment where each NodeB possesses full knowledge of the whole network. The system performance under such resource allocation schemes are analyzed and compared via Monte-Carlo simulations, which verifies that the proposed framework may serve as a good estimation and optimal reference to study how systems perform for network operators
Self organization of tilts in relay enhanced networks: a distributed solution
Despite years of physical-layer research, the capacity enhancement potential of relays is limited by the additional spectrum required for Base Station (BS)-Relay Station (RS) links. This paper presents a novel distributed solution by exploiting a system level perspective instead. Building on a realistic system model with impromptu RS deployments, we develop an analytical framework for tilt optimization that can dynamically maximize spectral efficiency of both the BS-RS and BS-user links in an online manner. To obtain a distributed self-organizing solution, the large scale system-wide optimization problem is decomposed into local small scale subproblems by applying the design principles of self-organization in biological systems. The local subproblems are non-convex, but having a very small scale, can be solved via standard nonlinear optimization techniques such as sequential quadratic programming. The performance of the developed solution is evaluated through extensive simulations for an LTE-A type system and compared against a number of benchmarks including a centralized solution obtained via brute force, that also gives an upper bound to assess the optimality gap. Results show that the proposed solution can enhance average spectral efficiency by up to 50% compared to fixed tilting, with negligible signaling overheads. The key advantage of the proposed solution is its potential for autonomous and distributed implementation
Analytical modeling of HSUPA-enabled UMTS networks for capacity planning
In recent years, mobile communication networks have experienced significant evolution. The 3G mobile communication system, UMTS, employs WCDMA as the air interface standard, which leads to quite different mobile network planning and dimensioning processes compared with 2G systems. The UMTS system capacity is limited by the received interference at NodeBs due to the unique features of WCDMA, which is denoted as `soft capacity'. Consequently, the key challenge in UMTS radio network planning has been shifted from channel allocation in the channelized 2G systems to blocking and outage probabilities computation under the `cell breathing' effects which are due to the relationship between network coverage and capacity. The interference characterization, especially for the other-cell interference, is one of the most important components in 3G mobile networks planning. This monograph firstly investigates the system behavior in the operation of UMTS uplink, and develops the analytic techniques to model interference and system load as fully-characterized random variables, which can be directly applicable to the performance modeling of such networks. When the analysis progresses from single-cell scenario to multi-cell scenario, as the target SIR oriented power control mechanism is employed for maximum capacity, more sophisticated system operation, `feedback behavior', has emerged, as the interference levels at different cells depend on each other. Such behaviors are also captured into the constructed interference model by iterative and approximation approaches. The models are then extended to cater for the features of the newly introduced HSUPA, which provides enhanced dedicated channels for the packet switched data services such that much higher bandwidth can be achieved for best-effort elastic traffic, which allows network operators to cope with the coexistence of both circuit-switched and packet-switched traffic and guarantee the QoS requirements. During the derivation, we consider various propagation models, traffic models, resource allocation schemes for many possible scenarios, each of which may lead to different analytical models. All the suggested models are validated with either Monte-Carlo simulations or discrete event simulations, where excellent matches between results are always achieved. Furthermore, this monograph studies the optimization-based resource allocation strategies in the UMTS uplink with integrated QoS/best-effort traffic. Optimization techniques, both linear-programming based and non-linear-programming based, are used to determine how much resource should be assigned to each enhanced uplink user in the multi-cell environment where each NodeB possesses full knowledge of the whole network. The system performance under such resource allocation schemes are analyzed and compared via Monte-Carlo simulations, which verifies that the proposed framework may serve as a good estimation and optimal reference to study how systems perform for network operators
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
New Benders' Decomposition Approaches for W-CDMA Telecommunication Network Design
Network planning is an essential phase in successfully operating state-of-the-art telecommunication systems. It helps carriers increase revenues by deploying the right technologies in a cost effective manner. More importantly, through the network planning phase, carriers determine the capital needed to build the network as well as the competitive pricing for the offered services. Through this phase, radio tower locations are selected from a pool of candidate locations so as to maximize the net revenue acquired from servicing a number of subscribers. In the Universal Mobile Telecommunication System (UMTS) which is based on the Wideband Code Division Multiple Access scheme (W-CDMA), the coverage area of each tower, called a cell, is not only affected by the signal's attenuation but is also affected by the assignment of the users to the towers. As the number of users in the system increases, interference levels increase and cell sizes decrease. This complicates the network planning problem since the capacity and coverage problems cannot be solved separately.
To identify the optimal base station locations, traffic intensity and potential locations are determined in advance, then locations of base stations are chosen so as to satisfy minimum geographical coverage and minimum quality of service levels imposed by licensing agencies. This is implemented through two types of power control mechanisms. The power based power control mechanism, which is often discussed in literature, controls the power of the transmitted signal so that the power at the receiver exceeds a given threshold. On the other hand, the signal-to-interference ratio (SIR) based power control mechanism controls the power of the transmitted signal so that the ratio of the power of the received signal over the power of the interfering signals exceeds a given threshold. Solving the SIR based UMTS/W-CDMA network planning problem helps network providers in designing efficient and cost effective network infrastructure. In contrast to the power based UMTS/W-CDMA network planning problem, the solution of the SIR based model results in higher profits. In SIR based models, the power of the transmitted signals is decreased which lowers the interference and therefore increases the capacity of the overall network. Even though the SIR based power control mechanism is more efficient than the power based power control mechanism, it has a more complex implementation which has gained less attention in the network planning literature.
In this thesis, a non-linear mixed integer problem that models the SIR based power control system is presented. The non-linear constraints are reformulated using linear expressions and the problem is exactly solved using a Benders decomposition approach. To overcome the computational difficulties faced by Benders decomposition, two novel extensions are presented. The first extension uses the analytic center cutting plane method for the Benders master problem, in an attempt to reduce the number of times the integer Benders master problem is solved. Additionally, we describe a heuristic that uses the analytic center properties to find feasible solutions for mixed integer problems. The second extension introduces a combinatorial Benders decomposition algorithm. This algorithm may be used for solving mixed integer problems with binary variables. In contrast to the classical Benders decomposition algorithm where the master problem is a mixed integer problem and the subproblem is a linear problem, this algorithm decomposes the problem into a mixed integer master problem and a mixed integer subproblem. The subproblem is then decomposed using classical Benders decomposition, leading to a nested Benders algorithm. Valid cuts are generated at the classical Benders subproblem and are added to the combinatorial Benders master problem to enhance the performance of the algorithm.
It was found that valid cuts generated using the analytic center
cutting plane method reduce the number of times the integer
Benders master problem is solved and therefore reduce the
computational time. It was also found that the combinatorial
Benders reduces the complexity of the integer
master problem by reducing the number of integer variables in it.
The valid cuts generated within the nested Benders algorithm
proved to be beneficial in reducing the number of times the
combinatorial Benders master problem is solved and in reducing the
computational time that the overall algorithm takes. Over 110
instances of the UMTS/W-CDMA network planning problem ranging from
20 demand points and 10 base stations to 140 demand points and 30
base stations are solved to optimality
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Radio network management in cognitive LTE-Femtocell Systems
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.There is a strong uptake of femtocell deployment as small cell application
platforms in the upcoming LTE networks. In such two-tier networks of LTEfemtocell
base stations, a large portion of the assigned spectrum is used
sporadically leading to underutilisation of valuable frequency resources.
Novel spectrum access techniques are necessary to solve these current spectrum
inefficiency problems. Therefore, spectrum management solutions should have
the features to improve spectrum access in both temporal and spatial manner.
Cognitive Radio (CR) with the Dynamic Spectrum Access (DSA) is considered
to be the key technology in this research in order to increase the spectrum
efficiency. This is an effective solution to allow a group of Secondary Users
(SUs) to share the radio spectrum initially allocated to the Primary User (PUs) at
no interference.
The core aim of this thesis is to develop new cognitive LTE-femtocell systems
that offer a 4G vision, to facilitate the radio network management in order to
increase the network capacity and further improve spectrum access probabilities.
In this thesis, a new spectrum management model for cognitive radio networks is
considered to enable a seamless integration of multi-access technology with
existing networks. This involves the design of efficient resource allocation
algorithms that are able to respond to the rapid changes in the dynamic wireless
environment and primary users activities. Throughout this thesis a variety of
network upgraded functions are developed using application simulation
scenarios. Therefore, the proposed algorithms, mechanisms, methods, and system
models are not restricted in the considered networks, but rather have a wider
applicability to be used in other technologies.
This thesis mainly investigates three aspects of research issues relating to the
efficient management of cognitive networks: First, novel spectrum resource
management modules are proposed to maximise the spectrum access by rapidly
detecting the available transmission opportunities. Secondly, a developed pilot
power controlling algorithm is introduced to minimise the power consumption by
considering mobile position and application requirements. Also, there is
investigation on the impact of deploying different numbers of femtocell base
stations in LTE domain to identify the optimum cell size for future networks.
Finally, a novel call admission control mechanism for mobility management is
proposed to support seamless handover between LTE and femtocell domains.
This is performed by assigning high speed mobile users to the LTE system to
avoid unnecessary handovers.
The proposed solutions were examined by simulation and numerical analysis to
show the strength of cognitive femtocell deployment for the required
applications. The results show that the new system design based on cognitive
radio configuration enable an efficient resource management in terms of
spectrum allocation, adaptive pilot power control, and mobile handover. The
proposed framework and algorithms offer a novel spectrum management for self organised LTE-femtocell architecture.
Eventually, this research shows that certain architectures fulfilling spectrum
management requirements are implementable in practice and display good
performance in dynamic wireless environments which recommends the
consideration of CR systems in LTE and femtocell networks
Self-Organized Coverage and Capacity Optimization for Cellular Mobile Networks
Die zur Erfüllung der zu erwartenden Steigerungen übertragener
Datenmengen notwendige größere Heterogenität und steigende Anzahl von
Zellen werden in der Zukunft zu einer deutlich höheren Komplexität bei
Planung und Optimierung von Funknetzen führen. Zusätzlich erfordern
räumliche und zeitliche Änderungen der Lastverteilung eine dynamische
Anpassung von Funkabdeckung und -kapazität
(Coverage-Capacity-Optimization, CCO). Aktuelle Planungs- und
Optimierungsverfahren sind hochgradig von menschlichem Einfluss abhängig,
was sie zeitaufwändig und teuer macht. Aus diesen Grnden treffen Ansätze
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 Abläufe 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
Übertragungskapazität in Funkzellennetzwerken. Der Parameter der Wahl
hierfür ist die Antennenneigung. Die zahlreichen vorhandenen Ansätze
hierfĂĽr 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
Leistungsfähigkeit der betrachteten Verfahren wird mittels eine
LTE-Systemsimulators evaluiert. Dabei werden sowohl gleichmäßig verteilte
Zellen, als auch Zellen ungleicher Größe betrachtet. Die entwickelten
Ansätze werden mit bekannten Lösungen aus der Literatur verglichen. Die
Ergebnisse zeigen, dass die vorgeschlagenen Lösungen effektiv auf
Änderungen im Netzwerk und der Umgebung reagieren können. Zellen stellen
sich selbsttätig schnell auf Ausfälle 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
Ansätze. Die Verbesserungen steigen mit der Netzwerkgröße.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
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