2,394 research outputs found
Coordinating Coupled Self-Organized Network Functions in Cellular Radio Networks
Nutzer der Mobilfunknetze wünschen und fordern eine Steigerung des
Datendurchsatzes, die zur Erhöhung der Netzlast führt. Besonders seit der
Einführung von LTE erhöht sich daher die Anzahl und Dichte der Zellen in
Mobilfunknetzen. Dies führt zusätzlich zur Zunahme der Investitions- und
Betriebskosten, sowie einer höheren Komplexität des Nerzbetriebs. Der
Einsatz selbstorganisierter Netze (SONs) wird vorgeschlagen, um diese drei
Herausforderungen zu bewältigen. Einige SON-Funktionen (SF) wurden sowohl
von Seiten der Netzbetreiber als auch von den Standardisierungsgremien
vorgeschlagen. Eine SF repräsentiert hierbei eine Netzfunktion, die
automatisiert werden kann. Ein Beispiel ist die Optimierung der Robustheit
des Netzes (Mobility Robustness Optimization, MRO) oder der Lastausgleich
zwischen Funkzellen (Mobility Load Balancing, MLB).
Die unterschiedlichen SON-Funktionen werden innerhalb eines Mobilfunknetzes
eingesetzt, wobei sie dabei häufig gleiche oder voneinander abhängige
Parameter optimieren. Zwangsläufig treten daher beim Einsatz paralleler
SON-Funktionen Konflikte auf, die Mechanismen erfordern, um diese
Konflikte aufzulösen oder zu minimieren. In dieser Dissertation werden
Lösungen aufgezeigt und untersucht, um die Koordination der SON-Funktionen
zu automatisieren und, soweit möglich, gleichmä{\ss}ig zu verteilen.
Im ersten Teil werden grundsätzliche Entwürfe für SFs evaluiert, um die
SON-Koordination zu vereinfachen. Basierend auf der Beobachtung, dass die
Steurung der SON-Funktion sich ähnlich dem generischen Q-Learning Problem
verhält, werden die SFs als Q-Learning-Agenten entworfen. Dieser Ansatz
wurde mit sehr positiven Ergebnissen auf zwei SFs (MRO und MLB) angewandt.
Die als Q-Learning-Agenten entworfenen SFs werden für zwei
unterschiedliche Ansätze der SON-Koordination evaluiert. Beide
Koordinierungsansätze betrachten dabei die SON-Umgebung als ein
Multi-Agenten-System. Der erste Ansatz basierend auf einer
räumlich-zeitlichen Entkoppelung separiert die Ausführung von
SF-Instanzen sowohl räumlich als auch zeitlich, um die Konflikte zwischen
den SF-Instanzen zu minimieren. Der zweite Ansatz wendet kooperatives
Lernen in Multi-Agenten-Systemen als automatisierten Lösungsansatz zur
SON-Koordination an. Die einzelnen SF-Instanzen lernen anhand von
Utility-Werten, die sowohl die eigenen Metriken als auch die Metriken der
Peer-SF-Instanzen auswerten. Die Intention dabei ist, durch die erlernte
Zustands-Aktions-Strategie Aktionen auszuführen, die das beste Resultat
für die aktive SF, aber auch die geringste Auswirkung auf Peer-SFs
gewährleisten. In der Evaluation des MRO-MLB-Konflikts zeigten beide
Koordinierungsansätze sehr gute Resultate.Owing to increase in desired user throughput and to the subsequent increase
in network traffic, the number and density of cells in cellular networks
have increased, especially starting with LTE. This directly translates into
higher capital and operational expenses as well as increased complexity of
network operation. To counter all three challenges, Self-Organized
Networks (SON) have been proposed. A number of SON Functions (SFs) have
been defined both from the network operator community as well as from the
standardization bodies. In this respect, a SF represents a network
function that can be automated e.g. Mobility Robustness Optimization (MRO)
or Mobility Load balancing (MLB).
The different SFs operate on the same radio network, in many cases
adjusting the same or related parameters. Conflicts are as such bound to
occur during the parallel operation of such SFs and mechanisms are required
to resolve or minimize the conflicts. This thesis studies the solutions
through which SON functions can be coordinated in an automated and
preferably distributed manner.
In the first part we evaluate the design principles of SFs that aim at
easing the coordination. With the observation that the SON control loop is
similar to a generic Q-learning problem, we propose designing SFs as
Q-learning agents. This framework is applied to two SFs (MRO and MLB) with
very positive results. Given the designed QL based SFs, we then
evaluate two SON coordination approaches that consider the SON environment
as a Multi-Agent System (MAS). The first approach based on
Spatial-Temporal Decoupling (STD) separates the execution of SF instances
in space and time so as to minimize the conflicts among instances. The
second approach applies multi-agent cooperative learning for an automated
solution towards SON coordination. In this case individual SF instances
learn based on utilities that aggregate their own metrics as well as the
metrics of peer SF instances. The intention in this case is to ensure that
the learned state-action policy functions apply actions that guarantee the
best result for the active SF but also have the least effect on the peer
SFs. Both coordination approaches have been evaluated with very positive
results in simulations that consider the MRO - MLB conflict
Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions
The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions
Distribution games: a new class of games with application to user provided networks
User Provided Network (UPN) is a promising solution for sharing the limited network resources by utilizing user capabilities as a part of the communication infrastructure. In UPNs, it is an important problem to decide how to share the resources among multiple clients in decentralized manner. Motivated by this problem, we introduce a new class of games termed distribution games that can be used to distribute efficiently and fairly the bandwidth capacity among users. We show that every distribution game has at least one pure strategy Nash equilibrium (NE) and any best response dynamics always converges to such an equilibrium. We consider social welfare functions that are weighted sums of bandwidths allocated to clients. We present tight upper bounds for the price of anarchy and price of stability of these games provided that they satisfy some reasonable assumptions. We define two specific practical instances of distribution games that fit these assumptions. We conduct experiments on one of these instances and demonstrate that in most of the settings the social welfare obtained by the best response dynamics is very close to the optimum. Simulations show that this game also leads to a fair distribution of the bandwidth.Publisher's Versio
A New Paradigm for Proactive Self-Healing in Future Self-Organizing Mobile Cellular Networks
Mobile cellular network operators spend nearly a quarter of their revenue on network management and maintenance. Remarkably, a significant proportion of that budget is spent on resolving outages that degrade or disrupt cellular services. Historically, operators have mainly relied on human expertise to identify, diagnose and resolve such outages while also compensating for them in the short-term. However, with ambitious quality of experience expectations from 5th generation and beyond mobile cellular networks spurring research towards technologies such as ultra-dense heterogeneous networks and millimeter wave spectrum utilization, discovering and compensating coverage lapses in future networks will be a major challenge. Numerous studies have explored heuristic, analytical and machine learning-based solutions to autonomously detect, diagnose and compensate cell outages in legacy mobile cellular networks, a branch of research known as self-healing. This dissertation focuses on self-healing techniques for future mobile cellular networks, with special focus on outage detection and avoidance components of self-healing.
Network outages can be classified into two primary types: 1) full and 2) partial. Full outages result from failed soft or hard components of network entities while partial outages are generally a consequence of parametric misconfiguration. To this end, chapter 2 of this dissertation is dedicated to a detailed survey of research on detecting, diagnosing and compensating full outages as well as a detailed analysis of studies on proactive outage avoidance schemes and their challenges.
A key observation from the analysis of the state-of-the-art outage detection techniques is their dependence on full network coverage data, susceptibility to noise or randomness in the data and inability to characterize outages in both spacial domain and temporal domain. To overcome these limitations, chapters 3 and 4 present two unique and novel outage detection techniques. Chapter 3 presents an outage detection technique based on entropy field decomposition which combines information field theory and entropy spectrum pathways theory and is robust to noise variance. Chapter 4 presents a deep learning neural network algorithm which is robust to data sparsity and compares it with entropy field decomposition and other state-of-the-art machine learning-based outage detection algorithms including support vector machines, K-means clustering, independent component analysis and deep auto-encoders.
Based on the insights obtained regarding the impact of partial outages, chapter 5 presents a complete framework for 5th generation and beyond mobile cellular networks that is designed to avoid partial outages caused by parametric misconfiguration. The power of the proposed framework is demonstrated by leveraging it to design a solution that tackles one of the most common problems associated with ultra-dense heterogeneous networks, namely imbalanced load among small and macro cells, and poor resource utilization as a consequence. The optimization problem is formulated as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization and load balancing self-organizing network functions
Distributed optimisation techniques for wireless networks
Alongside the ever increasing traffic demand, the fifth generation (5G) cellular network architecture is being proposed to provide better quality of service, increased data rate, decreased latency, and increased capacity. Without any doubt, the 5G cellular network will comprise of ultra-dense networks and multiple input multiple output technologies. This will make the current centralised solutions impractical due to increased complexity. Moreover, the amount of coordination information that needs to be transported over the backhaul links will be increased. Distributed or decentralised solutions are promising to provide better alternatives.
This thesis proposes new distributed algorithms for wireless networks which aim to reduce the amount of system overheads in the backhaul links and the system complexity. The analysis of conflicts amongst transmitters, and resource allocation are conducted via the use of game theory, convex optimisation, and auction theory.
Firstly, game-theoretic model is used to analyse a mixed quality of service (QoS) strategic non-cooperative game (SNG), for a two-user multiple-input single-output (MISO) interference channel. The players are considered to have different objectives. Following this, the mixed QoS SNG is extended to a multicell multiuser network in terms of signal-to-interference-and-noise ratio (SINR) requirement. In the multicell multiuser setting, each transmitter is assumed to be serving real time users (RTUs) and non-real time users (NRTUs), simultaneously. A novel mixed QoS SNG algorithm is proposed, with its operating point identified as the Nash equilibrium-mixed QoS (NE-mixed QoS). Nash, Kalai-Smorodinsky, and Egalitarian bargain solutions are then proposed to improve the performance of the NE-mixed QoS. The performance of the bargain solutions are observed to be comparable to the centralised solutions.
Secondly, user offloading and user association problems are addressed for small cells using auction theory. The main base station wishes to offload some of its users to privately owned small cell access points. A novel bid-wait-auction (BWA) algorithm, which allows single-item bidding at each auction round, is designed to decompose the combinatorial mathematical nature of the problem. An analysis on the existence and uniqueness of the dominant strategy equilibrium is conducted. The BWA is then used to form the forward BWA (FBWA) and the backward BWA (BBWA). It is observed that the BBWA allows more users to be admitted as compared to the FBWA.
Finally, simultaneous multiple-round ascending auction (SMRA), altered SMRA (ASMRA), sequential combinatorial auction with item bidding (SCAIB), and repetitive combinatorial auction with item bidding (RCAIB) algorithms are proposed to perform user offloading and user association for small cells. These algorithms are able to allow bundle bidding. It is then proven that, truthful bidding is individually rational and leads to Walrasian equilibrium. The performance of the proposed auction based algorithms is evaluated. It is observed that the proposed algorithms match the performance of the centralised solutions when the guest users have low target rates. The SCAIB algorithm is shown to be the most preferred as it provides high admission rate and competitive revenue to the bidders
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