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Chapter 1
Introduction
Cinzia Sartori, Henning Sanneck, Jürgen Goerge, Seppo Hämäläinen and Achim Wacker
The number of mobile subscribers has impressively increased during the last decade; at the same time wireless data usage continues to accelerate at an unprecedented pace even when (for developed countries) subscriber numbers reach saturation.
With the adoption of the Global System for Mobile Communication (GSM), mobile phones have become indispensable devices for voice communication and, nowadays, mobile networks are available for 90% of the world population. However, GSM was mainly designed for carrying voice traffic and some data capability was only added subsequently. The ‘mobile data explosion’ is a quite recent phenomenon driven by the introduction of the ‘Third Generation’ (3G) mobile system with Wideband Code Division Multiple Access (WCDMA), High Speed Packet Access (HSPA) and its enhancements called High Speed Packet Access Plus (HSPA+). The introduction of HSPA has marked the beginning of the transformation from voice-dominated to packet data-dominated mobile networks. These 3G evolution technologies are crucial to allow upgrading the network at relatively low costs and hence those technologies will be still important for a long period of time to come. However, it is clear that only a new Radio Access Technology (RAT) comprising a new air interface together with a new network architecture can cope with the described data explosion in the longer term. Long-Term Evolution (LTE; Holma and Toskala, 2011) is this technology which at the time of writing had been rolled out and put into commercial use in several countries already. Chapter 2 introduces the key technical concepts and radio access network scenarios of LTE.
The exponential growth of mobile broadband traffic is certainly caused by both, the increasing demand for known and new data services, such as mobile Internet access, social networking, location-based services/personal navigation, and so on, and the data processing and storage capabilities of state-of-the-art terminals, such as smartphones and, most recently, tablets (Figure 1.1). Such ‘always-on’ devices used by humans as well as network usage by machines (Machine to Machine; M2M) also put strong requirements on the capabilities of the network control plane.
As a next step, the use of tablets may increase the demand for wireless video applications to a large extent and put tremendous stress on the wireless network infrastructure. This is the case, because high resolution displays and powerful processors enable the transmission of high-definition video. This, in turn, produces a demand for high data rates required ‘everywhere’ to satisfy the expectation of the end customers. Such ‘data hungry’ applications ask for more capacity and higher quality of service, which can only be satisfied with the introduction of LTE and its evolution called ‘LTE-Advanced’ (LTE-A).
To cope with such a huge demand for data traffic transmission, the wireless network operators need to significantly upgrade their networks and use these resources most efficiently. Traditional methods like macro site ‘densification’, along with improved receivers and higher order sectorisation, will not be fully sufficient to provide the desired capacity for the predicted traffic growth. The deployment of small cells as an additional layer to the macro layer is definitely the most promising solution for building improved spectral efficiency (and thus capacity) per area. Thus, the migration from macro-only to Multi-Layer topology as part of a ‘Heterogeneous Network’ scenario are expected to further accelerate in the near future. Also, LTE will run for a long period of time in parallel with existing 2G and 3G networks (Multi-RAT).
The described requirements for wireless service providers to upgrade their networks, to deploy LTE and to integrate their existing RATs have the effect that the network infrastructure as a whole will be rather complex and heterogeneous. Thus, operators face significant operational challenges in terms of work effort and cost. Unfortunately, those costs will not be compensated by additional revenue due to the decreasing average revenue per user (caused by pricing schemes like e.g. flat rates, induced through fierce competition in the market). Hence, the cost position as a vital interest of operators, in particular the operational expenses (OPEX), has gained much more attention recently. Especially in the early deployment phase, the efforts to set up and optimise the network are significant and traditionally lead to substantial delays before an optimal and stable system setup can be reached. In order to minimise such delays and in general reduce the network operation expenses, the Self-Organising Networks (SON) concept is considered to be an integral part of LTE.
1.1 Self-Organising Networks (SON)
The concept of SON became frequently used after it was adopted by the Next Generation Mobile Networks (NGMN) alliance to address challenges foreseen due to management of several radio access technologies along with the LTE network introduction. Chapter 3 provides an introduction to the SON vision (Section 3.1), key SON concepts and benefits and their foundations.
One of the aims of operators is to keep their operational burden at the currently existing level, that is, manage the multi-RAT (including LTE), multi-layer infrastructure as described above with their existing operational staff and cost structure. Operators have to maximise their return on investment, they need to optimise the resource utilisation in order to minimise their huge, necessary investments hence, efficiency is essential in order to be able to manage the additional network without increased workforce.
Network operation today is based on a centralised Operation, Administration and Maintenance (OAM) architecture. Configuration and optimisation of network elements is performed centrally from an OAM system (also called the Operations and Maintenance Centre: OMC) with support of a set of planning and optimisation tools. Planning and optimisation tools are typically semi-automated and management tasks need to be tightly supervised by human operators. This manual effort is time-consuming, expensive, error-prone and requires a high degree of expertise (Laiho et al., 2006).
Increased automation of network operations is seen as a proper means to cope with the described rising complexity of the network infrastructure in order to utilise deployed network resources in an optimised way. At the same time automation aims at:
- keeping the operational effort at an acceptable level;
- protecting the network operation by reducing the probability of errors during the overall process of rolling out a network and the permanently ongoing process of managing the network;
- speeding up operational processes.
Self-organisation is an advanced mechanism to enable such automations. It is crucial that automated features are properly integrated with the existing operator processes and embedded into the architecture of the overall OAM tool chain. Automation is achieved by adding (SON) features to network equipment which facilitates network operation processes and delivery of professional services related to the network. Hence SON is a contributor to the ‘operability’ and ‘serviceability’ characteristics of a network.
3GPP has created the actual SON standards upon NGMN long-term objectives for a ‘SON-enabled mobile broadband network’ by defining the necessary use cases, measurements, procedures and open interfaces to support better operability in a multi-vendor environment. SON standardisation is still an ongoing activity (Figure 1.2). SON standardisation has started with LTE in 3GPP Release 8 and continued in Release 9 and 10 (Release 10 was completed in June 2011). Release 11, which will contain additional SON features and enhancements to existing ones, is in definition phase at the time of writing this book.
SON Use Cases (NGMN, 2008), cf. Section 3.2, are categorised into functional areas along the key OAM areas of configuration, optimisation and troubleshooting (cf. Figure 1.3):
- Self-Configuration (Chapter 4);
- Self-Optimisation (Chapter 5) including traffic steering between different type of radio resources; and
- Self-Healing (Chapter 6).
A common characteristic is that the degree of ‘human-in-the-loop’ for OAM use cases is reduced as much as possible reaching even fully ‘closed loop’ automation for some of the use cases.
‘Minimisation of Drive Tests’ (MDT) functionality has been specified in 3GPP Release 10 for LTE and Universal Terrestrial Radio Access Network (UTRAN). MDT addresses the issue that often drive tests have to be executed to monitor and assess mobile network performance. Such drive tests are very expensive since the actual testing needs significant human operator involvement. Key characteristics of MDT are measurements collected on User Equipments (UEs), which may contain location information, thereby allowing to have a much more fine-grain view of a cell's performance. Because such a view is useful not only for a human operator but also for the automated SON functions, MDT is considered to be an important enabler for SON. MDT is discussed in detail in Chapter 7.
SON research and standardisation is mainly focused on the radio access domain, due to its intrinsic complexity (high number of widely distributed network elements) and thus significant cost share of the overall network infrastructure and its operations. Nevertheless, SON for Core Networks (Chapter 8) is also relevant from the perspective of properly configuring and optimising the network end-to-end. Note that backhaul aspects contributing to the end-to-end view are treated where relevant in Chapters 4–6 which discuss the SON functional areas.
Figure 1.3 shows some examples for SON use cases. There exists a significant number of different SON use cases which have partially conflicting goals, overlapping input or output parameters. Examples for such SON function interactions as well as technical solutions to control the interactions are discussed as the main topic for SON Operation in Chapter 9.
Like mentioned above, on one hand LTE needs to be integrated with existing RATs; on the other hand even the resource capabilities of LTE macro cells will not be sufficient in the long term but need to be complemented by smaller cells for capacity. In Heterogeneous Networks (cf. Figure 1.4) operators will have to deal with handovers in inter-technology and macro/femto scenarios; interference management of macro/pico and macro/femto is definitely an outstanding issue. At the same time network capacity needs to be optimised via an efficient utilisation of all available resources (multi-RAT, multi-layer) while assuring desired end user experience with appropriate Quality of Service (QoS) and Quality of Experience (QoE). SON for Heterogeneous Networks and related challenges are described in Chapter 10.
While most of the concepts of the ‘classical’ SON use cases are now getting assessed and operators start their deployment, the SON concept keeps evolving by integrating new use cases and solutions based on existing and novel technologies. Chapter 11 describes that evolution which may lead to a true ‘Cognitive Network’.
1.2 The Transition from Conventional Network Operation to SON
While SON concepts are very appealing for network operators on the one hand, on the other hand they need to be carefully integrated into existing tool chains realising OAM processes. Hence, in the following, basics of ‘conventional’ network rollout and operation are introduced and the potential automation possibilities, which are the targets for SON, are discussed.
Business targets must be broken down to an optimal deployment of the network infrastructure and to an optimised ...
