Cell identification based on received signal strength fingerprints: concept and application towards energy saving in cellular networks

The increasing deployment of small cells aimed at off-loading data traffic from macrocells in heterogeneous networks has resulted in a drastic increase in energy consumption in cellular networks. Energy consumption can be optimized in a selforganized way by adapting the number of active cells in response to the current traffic demand. In this paper we concentrate on the complex problem of how to identify small cells to be reactivated in situations where multiple cells are concurrently inactive. Solely based on the received signal strength, we present cell-specific patterns for the generation of unique cell fingerprints. The cell fingerprints of the deactivated cells are matched with measurements from a high data rate demanding mobile device to identify the most appropriate candidate. Our scheme results in a matching success rate of up to 100% to identify the best cell depending on the number of cells to be activated

Self-optimized energy saving using cell fingerprinting for future radio access networks

Environmental sustainability and the strongly raising energy bill of network operators demand the implementation of energy reduction strategies in future radio access systems. The sharp rise in energy consumption, mostly caused by the exponential increase of data traffic, demands the deployment of a huge number of additional base stations (BSs). As the BS consumes the largest share of the energy in a cellular network, they offer a high energy saving potential. Energy consumption can be reduced in a self-organized way by adapting the network capacity in response to the instantaneous traffic demand. Thus, cells are deactivated and reactivated in line with the changing traffic demand. In this thesis, we concentrate on the complex problem of how to identify cells to be reactivated in situations of rising traffic demand. Reliable cell identification under any given traffic condition is the key for the self-optimized energy saving approach. The fingerprint method is used to identify the best fitting cell to take over the increasing traffic volume from highly loaded neighbor cells. The first step is to generate the cell individual fingerprints. Cells are found to be characterized by the received signal strength (RSS) measured by mobile device as observed in the neighbor cells. Consequently, a fingerprint consists of the list of neighbor cells and the associated RSS metrics that map the neighbor cell RSS distributions. The second step is to identify and subsequently activate the most suitable sleeping cell to relieve the active cell in overload. Initially, the overloaded cell requests mobiles to measure the RSS of the active neighbor cells. The measurement samples are matched with each cell fingerprint representing a sleeping cell. The cell fingerprint that corresponds best to the sample is expected to provide the best radio conditions. Results show that the accuracy increases with traffic load and number of metrics used for the matching, both of which provide more matching events. Finally, a simple model is created to evaluate the energy saving potential of cell fingerprinting. Input for the model is the hit rate of the most suitable cell achieved during the preceding cell fingerprinting simulation studies. The saving potential approaches closely the optimum results, if the most suitable cell would have been known.Ökologische Nachhaltigkeit, aber auch die steigenden Energiekosten, verlangen nach neuen Strategien zur Senkung des Stromverbrauchs zukünftiger Mobilfunknetze. Der Anstieg des Stromverbrauchs wird weitgehend durch das exponentiell wachsende Datenvolumen und den dadurch zusätzlich benötigten Basisstationen (BS) verursacht. Die BS bietet als größter Stromverbraucher eines Mobilfunknetzes ein hohes Einsparpotential. Durch selbstorganisierte Verfahren kann die verfügbare Netzkapazität kontinuierlich an die aktuell benötigte Kapazität angepasst werden, indem Funkzellen deaktiviert und bei Bedarf reaktiviert werden. Die zentrale Fragestellung dieser Arbeit ist, wie bei steigenden Datenverkehrsaufkommen geeignete, inaktive Zellen identifiziert und somit reaktiviert werden können. Voraussetzung dafür ist es, eine zuverlässige Zell-Identifizierung unter jeder beliebigen Verkehrsbedingung zu gewährleisten. Dafür wird das Fingerprinting-Verfahren eingesetzt. Als ersten Schritt generiert jede Zelle ihren individuellen "Fingerabdruck". Dafür messen die mobilen Endgeräte im gesamten Zellbereich die Empfangsfeldstärke der Nachbarzellen. Dementsprechend besteht der "Fingerabdruck" einer Zelle aus der Liste der Nachbarzellen und Metriken, die die Verteilung der Empfangsfeldstärke der jeweiligen Nachbarzelle abbilden. Als zweiter Schritt wird die inaktive Zelle identifiziert, die am besten geeignet ist, das zunehmende Datenvolumen zu übernehmen. Dafür fordert die überlastete Zelle Endgeräte auf, die Empfangsfeldstärke der aktiven Nachbarzellen zu messen. Diese Messwerte werden mit den Messwerten jedes "Fingerabdrucks" einer inaktiven Nachbarzelle verglichen. Die inaktive Zelle, deren "Fingerabdruck" am besten mit den Messwerten der Endgeräte übereingestimmt, verfügt über die besten Funkbedingungen, um Endgeräte der überlasteten Zelle zu bedienen. Die erzielten Ergebnisse zeigen, dass die Genauigkeit die passende Zelle zu identifizieren, sowohl von der Anzahl aktiver Nachbarzellen als auch von der Anzahl und Art der Metriken abhängt. Abschließend wird das Einsparpotential durch Einsatz von Fingerprinting berechnet. Als Input werden die in den vorangegangenen Simulationsstudien ermittelten Genauigkeiten der Zell-Identifizierung eingesetzt. Das Einsparpotential nähert sich dabei der maximal erzielbaren Stromeinsparung an

Enabling self organisation for future cellular networks.

The rapid growth in mobile communications due to the exponential demand for wireless access is causing the distribution and maintenance of cellular networks to become more complex, expensive and time consuming. Lately, extensive research and standardisation work has been focused on the novel paradigm of self-organising network (SON). SON is an automated technology that allows the planning, deployment, operation, optimisation and healing of the network to become faster and easier by reducing the human involvement in network operational tasks, while optimising the network coverage, capacity and quality of service. However, these SON autonomous features cannot be achieved with the current drive test coverage assessment approach due to its lack of automaticity which results in huge delays and cost. Minimization of drive test (MDT) has recently been standardized by 3GPP as a key self- organising network (SON) feature. MDT allows coverage to be estimated at the base station using user equipment (UE) measurement reports with the objective to eliminate the need for drive tests. However, most MDT based coverage estimation methods recently proposed in literature assume that UE position is known at the base station with 100% accuracy, an assumption that does not hold in reality. In this work, we develop a novel and accurate analytical model that allows the quantification of error in MDT based autonomous coverage estimation (ACE) as a function of error in UE as well as base station (user deployed cell) positioning. We first consider a circular cell with an omnidirectional antenna and then we use a three-sectored cell and see how the system is going to be affected by the UE and the base station (user deployed cell) geographical location information errors. Our model also allows characterization of error in ACE as function of standard deviation of shadowing in addition to the path-loss

D4.2 Final report on trade-off investigations

Research activities in METIS WP4 include several as pects related to the network-level of future wireless communication networks. Thereby, a large variety of scenarios is considered and solutions are proposed to serve the needs envis ioned for the year 2020 and beyond. This document provides vital findings about several trade-offs that need to be leveraged when designing future network-level solutions. In more detail, it elaborates on the following trade- offs: • Complexity vs. Performance improvement • Centralized vs. Decentralized • Long time-scale vs. Short time-scale • Information Interflow vs. Throughput/Mobility enha ncement • Energy Efficiency vs. Network Coverage and Capacity Outlining the advantages and disadvantages in each trade-off, this document serves as a guideline for the application of different network-level solutions in different situations and therefore greatly assists in the design of future communication network architectures.Aydin, O.; Ren, Z.; Bostov, M.; Lakshmana, TR.; Sui, Y.; Svensson, T.; Sun, W.... (2014). D4.2 Final report on trade-off investigations. http://hdl.handle.net/10251/7676

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods