Lars Christoph Schmelz

A coordination framework for self-organisation in LTE networks

Self-organising Networks (SON) as introduced for 3G Long Term Evolution (LTE) will typically involve several different SON functions. These functions are not necessarily aware of each other and may have complex relations and interdependencies, for example, conflicting parameter settings, depending on their design and approach. Coordination of SON functions may become necessary in order to harmonise the actions of SON functions and allow for a stable and reliable behaviour of the SON system. This paper describes different conflict types, harmonisation approaches to avoid and resolve conflicts, and a functional framework with different roles to achieve harmonisation. An exemplary case study is given. The paper concludes that, in case several conflicting SON functions are implemented in a network, a SON Coordinator may be beneficial to prevent from network instabilities and/or to improve the performance.

Embedding Multiple Self-Organisation Functionalities in Future Radio Access Networks

Wireless network operators today allocate considerable manual effort in managing their networks. A viable solution for lowering the manual effort is to introduce self-organisation functionalities. In this paper we discuss the challenges that are encountered when embedding multiple self-organisation functionalities into an overall self-organisation concept for future wireless networks. We foresee that there will most likely be a need of a rather complex coordination mechanism for handling multiple self-organisation functionalities in future wireless access networks.

Policy-driven workflows for mobile network management automation

Future wireless networks will experience a continuous growth regarding the number of network elements with increasingly complex interrelations between the configuration of multiple network elements. Another trend is the seamless integration of multiple radio technologies into a single wireless network. Both developments increase network management complexity and require new management concepts with a very high degree of automation. For this purpose, a novel management approach based on a combination of workflow and policy technologies is presented. The goal is to simplify and automate management tasks in mobile networks in order to raise the state of self-organization while the network still remains under control of the operator. The approach provides the means for a dynamic system to automatically adapt to context changes. Moreover, an experimental system is presented for the purposes of concept validation and evaluation along with a real world use case.

Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks

Self-organizing networks (SON) aim at simplifying network management (NM) and optimizing network capital and operational expenditure through automation. Most SON functions (SFs) are rule-based control structures, which evaluate metrics and decide actions based on a set of rules. These rigid structures are, however, very complex to design since rules must be derived for each SF in each possible scenario. In practice, rules only support generic behavior, which cannot respond to the specific scenarios in each network or cell. Moreover, SON coordination becomes very complicated with such varied control structures. In this paper, we propose to advance SON toward cognitive cellular networks (CCN) by adding cognition that enables the SFs to independently learn the required optimal configurations. We propose a generalized Q-learning framework for the CCN functions and show how the framework fits to a general SF control loop. We then apply this framework to two functions on mobility robustness optimization (MRO) and mobility load balancing (MLB). Our results show that the MRO function learns to optimize handover performance while the MLB function learns to distribute instantaneous load among cells.

Impact-time concept for SON-Function coordination

Starting with the introduction of LTE, there is a high operator initiated demand for management automation. All automation requirements on network configuration, optimisation, and failure recovery are subsumed under the term Self-Organising Networks (SON). Within a SON a potentially larger number of SON-Functions individually perform specific management tasks. To prevent conflicting behaviour among different SON-Functions a SON-Coordinator is used that has to consider a variety of conflict types and the spatial and temporal impact of the SON-Functions. The Impact-time concept is composed of a set of time intervals describing the pair-wise temporal interdependency of SON-Functions, and also expressing restrictions in the availability of performance data. An experimental validation of the concept is presented.

Classification of Cells Based on Mobile Network Context Information for the Management of SON Systems

Todays networks become increasingly complex due to the presence of multiple radio access technologies (RATs) and various network layers. The introduction of Self-Organising Network (SON) Functions that continuously modify the networks operating point support the operator on dedicated network management tasks, but need to be configured themselves in order to allow for achieving objectives for the mobile network. Another complexity that arises is that those operator objectives also change depending on the environment and the current conditions of the mobile network. It is therefore indispensable to introduce a SON management automation entity. A corresponding solution is introduced in this paper.

SON management based on weighted objectives and combined SON Function models

The instrumentation of Self-Organising Network (SON) Functions in such way that they contribute to the operational objectives defined by a wireless network operator is a complex task. In order to enable automation of this configuration, we previously described the concept of a SON Objective Manager (SOM) which determines the best configuration based on conditional, ranked objectives and SON Function models describing the Key Performance Indicators (KPIs) optimised by some configuration. However, the simplicity of the models and the complexity of the design-time inference may limit the applicability of the initial concept. In this paper, we present an extended and enhanced run-time SOM with more expressive input models, namely, context-dependent, weighted operator objectives allowing a better trade-off and SON Function models defining the possible values of KPIs for some configuration. These improvements together allow for a broader application of the concept.

Self-configuration of basic LTE radio parameters using Magnetic Field Model

The success of communication service providers (CSPs) relies on efficient operational strategies for managing their networks, including the planning, optimisation and maintenance, in order to meet the desired Quality of Service (QoS), Quality of Experience (QoE) and business goals and requirements. Such business goals and requirements include, amongst others, the reduction of Operational and Capital Expenditure (OPEX and CAPEX). Self Organising Networks (SON) functionalities, including self-configuration, self-optimisation and self-healing, play a crucial role in minimising OPEX [1]. SON has been leveraged by the operator forum Next Generation Mobile Networks (NGMN) [12] and standardised by the 3rd Generation Partnership Project (3GPP) [9][11], in particular for Long Term Evolution (LTE) radio access networks. This paper presents a new solution for the self-configuration of the basic LTE radio parameters Physical Cell ID (PCI) and Physical Random Access Channel (PRACH) using Magnetic Field Model (MFM) techniques, providing a new approach with respect to the existing solutions for PCI allocation [3] in LTE networks.

Demonstrator for adaptive SON management

This demonstrator visualises a new paradigm for the management of Self-Organising Networks (SON), and the impact of SON management decisions on a heterogeneous mobile radio network. The main aspects shown thereby cover the operability of the system from a network operator perspective, and the effects of different SON implementation options on the Key Performance Indicators (KPIs) of the network. In terms of operability, the demonstrator shows that even a complex definition of target values for KPIs through the operator, which may be weighted against each other and have different values depending on the operational and network context, can be deployed to the implemented SON functions in a fully automated way. Changes to the target values, the context or the implemented SON functions lead to an automated adaptation of the SON system such that the KPI targets are achieved in the best possible way.

Cross-domain 5G network management for seamless industrial communications

Emerging scenarios of vertical industries, such as, adaptive manufacturing, cooperative autonomous driving, and real-time logistics demand for seamless communication among mobile entities, among them ground conveyors, user terminals, cars, or sensors. In practice, this leads to challenging requirements in terms of latency, bandwidth, availability, reliability, etc. that current mobile communications technologies, including cellular networks and IEEE 802-based solutions, do not fully account for. When deployed individually, neither of them fulfills the broad range of requirements, whereas a coordinated co-deployment suffers from the lack of efficient network management solutions as well as from insufficiently defined operating and owner structures for these mixed system deployments. Therefore, this work proposes a concept for an intelligent cross-domain 5G network management system and related optimization functions. In particular, the design of a cognitive, joint management of mobile industrial and cellular networks is outlined. Further, cognitive methods and virtualization techniques are motivated as major enablers. Advantages include an operator-grade network management of local industrial networks as well as a seamless integration with cellular networks. Following 5G design principles, the suggested network management system is also extensible to emerging radio access technologies (RATs) in the 5G context.