On Energy Efficient Inter-Frequency Small Cell Discovery in Heterogeneous Networks

In this paper, we investigate the optimal inter-frequency small cell discovery (ISCD) periodicity for small cells deployed on carrier frequency other than that of the serving macro cell. We consider that the small cells and user terminals (UTs) positions are modelled according to a homogeneous Poisson Point Process (PPP). We utilize polynomial curve fitting to approximate the percentage of time the typical UT missed small cell offloading opportunity, for a fixed small cell density and fixed UT speed. We then derive analytically, the optimal ISCD periodicity that minimizes the average UT energy consumption (EC). Furthermore, we also derive the optimal ISCD periodicity that maximizes the average energy efficiency (EE), i.e. bit-per-joule capacity. Results show that the EC optimal ISCD periodicity always exceeds the EE optimal ISCD periodicity, with the exception of when the average ergodic rates in both tiers are equal, in which the optimal ISCD periodicity in both cases also becomes equal

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

Channel Charting: Locating Users within the Radio Environment using Channel State Information

We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC works in a fully unsupervised manner, i.e., learning is only based on channel state information (CSI) that is passively collected at a single point in space, but from multiple transmit locations in the area over time. The method then extracts channel features that characterize large-scale fading properties of the wireless channel. Finally, the channel charts are generated with tools from dimensionality reduction, manifold learning, and deep neural networks. The network element performing CC may be, for example, a multi-antenna base-station in a cellular system and the charted area in the served cell. Logical relationships related to the position and movement of a transmitter, e.g., a user equipment (UE), in the cell can then be directly deduced from comparing measured radio channel characteristics to the channel chart. The unsupervised nature of CC enables a range of new applications in UE localization, network planning, user scheduling, multipoint connectivity, hand-over, cell search, user grouping, and other cognitive tasks that rely on CSI and UE movement relative to the base-station, without the need of information from global navigation satellite systems.Comment: To appear in IEEE Acces

LTE-Advanced radio access enhancements: A survey

Long Term Evolution Advanced (LTE-Advanced) is the next step in LTE evolution and allows operators to improve network performance and service capabilities through smooth deployment of new techniques and technologies. LTE-Advanced uses some new features on top of the existing LTE standards to provide better user experience and higher throughputs. Some of the most significant features introduced in LTE-Advanced are carrier aggregation, enhancements in heterogeneous networks, coordinated multipoint transmission and reception, enhanced multiple input multiple output usage and deployment of relay nodes in the radio network. Mentioned features are mainly aimed to enhance the radio access part of the cellular networks. This survey article presents an overview of the key radio access features and functionalities of the LTE-Advanced radio access network, supported by the simulation results. We also provide a detailed review of the literature together with a very rich list of the references for each of the features. An LTE-Advanced roadmap and the latest updates and trends in LTE markets are also presented

Challenges Imposed by User's Mobility in Future HetNet: Offloading and Mobility Management

The users' mobility imposes challenges to mobility management and, the offloading process, which hinder the conventional heterogeneous networks (HetNets) in meeting the huge data traffic requirements of the future. In this thesis, a trio-connectivity (TC), which includes a control-plane (C-plane), a user-plane (U-plane) and an indication-plane (I-plane), is proposed to tackle these challenges. Especially, the I-plane is created as an indicator to help the user equipment (UE) identify and discover the small cells in the system prior to offloading her from the overloaded cells e.g. macro cells, to the cells with abundant resources e.g. small cells. In order to show the advantages of the proposed TC structure, a comparison between the TC and the dual-connectivity (DC) is presented in this thesis, in terms of uplink energy efficiency (ULEE) and energy consumption. Furthermore, the complexity of mobility management is addressed in this thesis as the HetNets will have to handle a large number of UEs and their frequent handoffs due to very dense small-footprint small cells. Considering an accurate mobility framework is essential not only to find the potential offloading to the small cells but also to show the mobility impact on the quality of service (QoS). This thesis presents a framework to model and derive the coverage of small cells, the cell sojourn time and the handoff rate in a multi-tier HetNet by taking into account the overlap coverage among the small cells. The results show the effects of a number of parameters, including the density and the transmit power of the small cells and the power control factor, on the system performance. They also show that the TC can outperform the DC in dense HetNets in terms of energy efficiency and energy consumption

Analytical Performance Evaluation of Cooperative and Multi-Radio Concepts in Emerging Wireless Networks

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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