Edgar Kuehn

ICIC in DL and UL with network distributed and self-organized resource assignment algorithms in LTE

Inter-cell Interference coordination (ICIC) has gained much interest in the 3rd Generation Partnership Projects (3GPPs) Long Term Evolution (LTE) standardization of a new air interface. Due to the new physical layer, interference can now be predicted and avoided on a frequency basis. Such schemes are based on cell wise usage restrictions or resource preferences. After explaining the general degrees of freedom, we present an “inverted” reuse scheme for downlink and uplink and explain its advantages. This is supported by 3GPP-compliant system simulations. The question of assignment of restrictions or preferences naturally leads to a need for self-organization. For that, the concept of semi-static ICIC based on a request-grant mechanism applicable to load balancing is explained. For the static ICIC case with frequency planning, a novel and fully distributed algorithm provides optimized assignment of resource restrictions to the cells in a self-organizing way. It is capable of resolving sub-optimal aspects and comprises methods to detect and prevent possible system instabilities. The algorithms presented lead to an integrated ICIC-self-organizing network (SON) concept that can be realized in a multi-cell network solution.

Self-organization in 4G mobile networks: Motivation and vision

Self-organization is a key factor for the future evolution of mobile networks, especially for introduction of the new radio standard LTE. Reasons are the increasing complexity, heterogeneity and management effort. Based on an overview about the current state of the art, the self-organization potential for enabling real plug and play functionality, automatic network optimization, improved end-to-end QoS and new energy saving technologies is highlighted. This is especially true for the upcoming trend towards small cell deployments. Our vision is an essentially simplified network management architecture relying on a distributed self-organization functionality which enables large steps towards autonomous operation of network elements.

Enhanced wireless network efficiency by cognitive, self-organized resource reconfiguration

Mobile communication systems are facing an increasing demand for higher bit rates and reveal a growing heterogeneity and complexity. The European funded End-to-End Efficiency (E3) project has developed a system concept for cognitive and self-managed adaptation of radio network resource usage to dynamic user traffic and service demands. The main features of that E3 concept for cognitive wireless systems are intelligent and cognitive management mechanisms for network self-configuration and self-optimization. They include self-learning and policy-based decision making. Furthermore, reconfigurable mobile access nodes like flexible base stations enable the adaptation of hardware and processing resources to varying traffic and service demands. In combination, these mechanisms provide the required flexibility for achieving an increased network efficiency and an enhanced user satisfaction.

Construction of the RSRP map using sparse MDT measurements by regression clustering

Radio environment maps are geographical maps with overlaid performance information of radio communication systems, that can be considered as one of the key enablers for self-organizing networks. After the introduction of Minimization of Drive Tests (MDT) to the standards, operators are interested to generate radio environment maps based on sparse sets of MDT measurements gathered from user equipment. In this paper, we consider the construction of the Reference Signal Received Power (RSRP) map, which is an example of a radio environment map using such a sparse MDT measurement set from user equipment. In particular, we propose a two-step algorithm for RSRP map generation which is shown to bring substantial performance improvement in terms of the mean absolute error of the predicted RSRP map as compared to the existing baseline methods, i.e., environmental-based regression and inverse distance weighting interpolation.