Distributed coordination of self-organizing mechanisms in communication networks

The fast development of the Self-Organizing Network (SON) technology in mobile networks renders the problem of coordinating SON functionalities operating simultaneously critical. SON functionalities can be viewed as control loops that may need to be coordinated to guarantee conflict free operation, to enforce stability of the network and to achieve performance gain. This paper proposes a distributed solution for coordinating SON functionalities. It uses Rosen's concave games framework in conjunction with convex optimization. The SON functionalities are modeled as linear Ordinary Differential Equation (ODE)s. The stability of the system is first evaluated using a basic control theory approach. The coordination solution consists in finding a linear map (called coordination matrix) that stabilizes the system of SON functionalities. It is proven that the solution remains valid in a noisy environment using Stochastic Approximation. A practical example involving three different SON functionalities deployed in Base Stations (BSs) of a Long Term Evolution (LTE) network demonstrates the usefulness of the proposed method.Comment: submitted to IEEE TCNS. arXiv admin note: substantial text overlap with arXiv:1209.123

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

Self-organisation in LTE networks : an investigation

Mobile telecommunications networks based on Long Term Evolution (LTE) technology promise faster throughput to their users. LTE networks are however susceptible to a phenomenon known as inter-cell interference which can greatly reduce the throughput of the network causing unacceptable degradation of performance for cell edge users. A number of approaches to mitigating or minimising inter-cell interference have been presented in the literature such as randomisation, cancellation and coordination. The possibility of coordination between network nodes in an LTE network is made possible through the introduction of the X2 network link. This thesis explores approaches to reducing the effect of inter-cell interference on the throughput of LTE networks by using the X2 link to coordinate the scheduling of radio resources. Three approaches to the reduction of inter-cell interference were developed. Localised organisation is a centralised scheme in which a scheduler is optimised by a Genetic Algorithm (GA) to reduce interference. Networked organisation makes use of the X2 communications link to enable the network nodes to exchange scheduling information in a way that lowers the level of interference across the whole network. Finally a more distributed and de-centralised approach is taken in which each of the network nodes optimises its own scheduling in coordination with its neighbours. An LTE network simulator was built to allow for experimental comparison between these techniques and a number of existing approaches and to serve as a test bed for future algorithm development. These approaches were found to significantly improve the throughput of the cell edge users who were most affected by intereference. In particular the networked aspect of these approaches yielded the best initial results showing clear improvement over the existing state of the art. The distributed approach shows significant promise given further development.EPSR

Performance Analysis of Dynamic PUCCH Allocation Algorithm in LTE Network

The aim of the presented paper was to verify the impact of Dynamic PUCCH Resource Allocation Algorithm of the LTE cellular system on the maximum uplink cell throughput and call setup success rate - CSSR. Paper includes the laboratory testbed description and presents the results of an experiment confirming the improvement of both key performance indicators KPIs. Apart from the presentation of the Dynamic PUCCH Resource allocation algorithm, the paper also includes a description of legacy LTE uplink (PUCCH and PUSCH) channels dimensioning process thus filling the gap of such a tutorial in the available literature

Self-organisation in LTE networks : an investigation

Mobile telecommunications networks based on Long Term Evolution (LTE) technology promise faster throughput to their users. LTE networks are however susceptible to a phenomenon known as inter-cell interference which can greatly reduce the throughput of the network causing unacceptable degradation of performance for cell edge users. A number of approaches to mitigating or minimising inter-cell interference have been presented in the literature such as randomisation, cancellation and coordination. The possibility of coordination between network nodes in an LTE network is made possible through the introduction of the X2 network link. This thesis explores approaches to reducing the effect of inter-cell interference on the throughput of LTE networks by using the X2 link to coordinate the scheduling of radio resources. Three approaches to the reduction of inter-cell interference were developed. Localised organisation is a centralised scheme in which a scheduler is optimised by a Genetic Algorithm (GA) to reduce interference. Networked organisation makes use of the X2 communications link to enable the network nodes to exchange scheduling information in a way that lowers the level of interference across the whole network. Finally a more distributed and de-centralised approach is taken in which each of the network nodes optimises its own scheduling in coordination with its neighbours. An LTE network simulator was built to allow for experimental comparison between these techniques and a number of existing approaches and to serve as a test bed for future algorithm development. These approaches were found to significantly improve the throughput of the cell edge users who were most affected by intereference. In particular the networked aspect of these approaches yielded the best initial results showing clear improvement over the existing state of the art. The distributed approach shows significant promise given further development.EPSR

Performance Analysis of Dynamic PUCCH Allocation Algorithm in LTE Network

The aim of the presented paper was to verify the impact of Dynamic PUCCH Resource Allocation Algorithm of the LTE cellular system on the maximum uplink cell throughput and call setup success rate - CSSR. Paper includes the laboratory testbed description and presents the results of an experiment confirming the improvement of both key performance indicators KPIs. Apart from the presentation of the Dynamic PUCCH Resource allocation algorithm, the paper also includes a description of legacy LTE uplink (PUCCH and PUSCH) channels dimensioning process thus filling the gap of such a tutorial in the available literature

Business scenarios, technical challenges and system requirements - D2.1

Deliverable D2.1 del projecte Europeu OneFIT (ICT-2009-257385)Preprin

Traffic-Driven Energy Efficient Operational Mechanisms in Cellular Access Networks

Recent explosive growth in mobile data traffic is increasing energy consumption in cellular networks at an incredible rate. Moreover, as a direct result of the conventional static network provisioning approach, a significant amount of electrical energy is being wasted in the existing networks. Therefore, in recent time, the issue of designing energy efficient cellular networks has drawn significant attention, which is also the foremost motivation behind this research. The proposed research is particularly focused on the design of self-organizing type traffic-sensitive dynamic network reconfiguring mechanisms for energy efficiency in cellular systems. Under the proposed techniques, radio access networks (RANs) are adaptively reconfigured using less equipment leading to reduced energy utilization. Several energy efficient cellular network frameworks by employing inter-base station (BS) cooperation in RANs are proposed. Under these frameworks, based on the instantaneous traffic demand, BSs are dynamically switched between active and sleep modes by redistributing traffic among them and thus, energy savings is achieved. The focus is then extended to exploiting the availability of multiple cellular networks for extracting energy savings through inter-RAN cooperation. Mathematical models for both of these single-RAN and multi-RAN cooperation mechanisms are also formulated. An alternative energy saving technique using dynamic sectorization (DS) under which some of the sectors in the underutilized BSs are turned into sleep mode is also proposed. Algorithms for both the distributed and the centralized implementations are developed. Finally, a two-dimensional energy efficient network provisioning mechanism is proposed by jointly applying both the DS and the dynamic BS switching. Extensive simulations are carried out, which demonstrate the capability of the proposed mechanisms in substantially enhancing the energy efficiency of cellular networks

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)