The phenomenon of self-organization is pervasive in many areas of nature. Fish organize themselves to swim in structured clouds, ants find the shortest routes to food, and fireflies emit synchronized light flashes. Other examples of self-organization can be observed in the economy, psychology, population dynamics, and brain theory. In all of the above examples, “individual” elements interact directly with each other, and to changes in their environment. Typically, such self-organizing systems are flexible, adaptive, resilient, and scalable.
The application of self-organization concepts has recently gained momentum in many innovative fields, including Mobile Telecom Networks. In this domain, Self-Organized-Networks (SON) are expected to be a key driver for the improvement of Operation and Maintenance efficacy and effectiveness, as SON can reduce the costs of installation and management. Inherently supported by 4G Long Term Evolution (LTE), SON simplifies operational tasks through automated mechanisms of self-configuration, self-optimization and self-healing, which constitute the three main areas of the SON paradigm.
In detail, the self-configuration phase is triggered by events of intentional nature, e.g. the addition of a new network site or the introduction of a new feature. These upgrades generally require an initial configuration of a large set of parameters, which can be “automatically” configured by SON with a “plug-and-play” paradigm. In the self-optimization phase, intelligent methods apply to derive a regularly updated configuration, based on the overall context. Finally, triggered by events of non-intentional nature, such as the failure of a cell or site, self-healing aims to reduce any loss induced by such events.
SON is targeted to have a relevant impact both on Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). It reduces OPEX by decreasing human intervention in network design, build, and operation. It also reduces CAPEX by optimizing the usage of resources. In addition, it protects revenue by limiting human errors and allowing fast network reconfiguration. Finally, it can improve user experience, by optimizing network parameters and mitigating degradations.
Therefore, with analysts’ estimations of mobile traffic to growth by 25-fold, and mobile-connected devices to reach 10 billion in 2016, SON may help wireless operators to tackle the greatest challenge that they face today, which is the rapidly growing demand for wireless broadband. Challenge exacerbated by the increasing adoption of smartphones and the desire for greater ‘cloud’ connectivity. All of this waiting for the anticipated invasion of the Internet of Everything.
To sum-up, with networks becoming more and more complicated and cells higher and higher in number, the cooperation introduced by SON should be a mandatory path for all Telco Operators. Yet, despite the great interest demonstrated everywhere by Telco Operators and Network Vendors, the Self-Organization of Network is spreading at a radically different pace in different parts of the world and in different countries. The USA and Japan are in fact significantly anticipating the rest of the Telecom World in this Network Transformation.
In the self-configuration phase, SON automates the configuration of a large set of radio parameters and resource management algorithms, e.g. pilot powers and neighbors’ lists. These parameters are “automatically” configured by SON prior to operations, and before they become part of the continuous self-optimization of SON processes.
In the self-optimization phase, SON methods are applied in order to derive an updated set of radio (resource management) parameters, including e.g. antenna parameters (tilt, azimuth), power settings (including pilot, control and traffic channels), neighbors’ lists (cell IDs and associated weights), and other radio resource management parameters (admission, congestion, handover control, and packet scheduling).
In the self-healing phase, SON methods aim to resolve the loss of coverage or capacity induced by any negative event, by appropriately adjusting the parameters and algorithms in surrounding cells. Once the actual failure has been repaired, all parameters are restored to their original settings.