Integration of a genetic optimisation algorithm in a simulation framework for optimising femtocell networks.

The developments in mobile communication systems from 1G to 4G have increased demands on the network due to the increased number of devices and increasing volume of data and 5G is expected to significantly increase demands further. Therefore, networks need to be more efficient to deliver the expected increase in volume. An energy and cost efficient way to cope with such an anticipated increase in the demand of voice and data is the dense deployment of small cells i.e. femtocells. Femtocells are identified as a crucial way to the delivery of the increased demands for heterogeneous networks in which macrocells work in combination with femtocells to provide coverage to offices, homes and enterprise. A survey of the literature is conducted to examine the mechanisms and approaches different authors have used to optimise the network. One of the major activities in this project before the transfer was the identification of the parameters. The literature was analysed and key performance parameters were identified. Based on the identified key performance parameters, a simulation framework is used to perform the experiments and to analyse the performance of a two-tier LTE-A system having femtocell overlays. A comprehensive and easy to use graphical user interface has been set up with the desired two- tier network topologies. It estimates the throughput and path loss of all the femto and macro users for all the supported bandwidths of an LTE-A system using different modulation schemes. A series of tests are carried out using the described simulation framework for a range of scenarios. The modulation scheme that yield highest throughput for a femtocell user is identified, and path loss is found to be independent from the modulation scheme but is dependent on the distance from its base station. In another series of experiments, the effects that walls inside buildings have on connectivity are examined and positioning of the femtocells is changed for each scenario inside buildings to analyse the performance. These results are used to find the optimised location of femtocells in different room layouts of the building. The simulation framework is further developed to be able to optimise the whole femtocell network by finding the optimised positioning of femtocells using the genetic optimisation algorithm. The end user can provide the inputs of the desired network topology to the simulation framework through a graphical user interface. The throughput and path loss of all the femto users are calculated before and after optimisation. The simulation results are generated in the form of tables before and after optimisation for comparison and analysis. The layouts depicting the indoor environment of the building before and after optimisation can be seen and analysed through the graphical user interface developed as a part of this simulation framework. Two case studies are defined and described to test the capacity and capability of the developed simulation framework and to show how the simulation framework can be used to identify the optimum positions of the femtocells under different configurations of room designs and number of users that represent contrasting loads on the network. Any desired network topology can be created and analysed on the basis of throughput and path loss by using this simulation framework to optimise the femtocell networks in an indoor environment of the building. The results of the experiments are compared against the claims in other published research

An Intelligent Expert System for Decision Analysis and Support in Multi-Attribute Layout Optimization

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc

D11.2 Consolidated results on the performance limits of wireless communications

Deliverable D11.2 del projecte europeu NEWCOM#The report presents the Intermediate Results of N# JRAs on Performance Limits of Wireless Communications and highlights the fundamental issues that have been investigated by the WP1.1. The report illustrates the Joint Research Activities (JRAs) already identified during the first year of the project which are currently ongoing. For each activity there is a description, an illustration of the adherence and relevance with the identified fundamental open issues, a short presentation of the preliminary results, and a roadmap for the joint research work in the next year. Appendices for each JRA give technical details on the scientific activity in each JRA.Peer ReviewedPreprin

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

Spectral and Energy Efficiency in Cellular Mobile Radio Access Networks

Driven by the widespread use of smartphones and the release of a wide range of online packet data services, an unprecedented growth in the mobile data usage has been observed over the last decade. Network operators recently realised that the traditional approach of deploying more macrocells could not cope with this continuous growth in mobile data traffic and if no actions are taken, the energy demand to run the networks, which are able to support such traffic volumes risks to become unmanageable. In this context, comprehensive investigations of different cellular network deployments, and various algorithms have been evaluated and compared against each other in this thesis, to determine the best deployment options which are able to deliver the required capacity at a minimum level of energy consumption. A new scalable base station power consumption model was proposed and a joint evaluation framework for the relative improvements in throughput, energy consumption,and energy efficiency is adopted to avoid the inherent ambiguity of using only the bit/J energy efficiency metric. This framework was applied to many cellular network cases studies including macro only, small cell only and heterogeneous networks to show that pure small cell deployments outperform the macro and heterogeneous networks in terms of the energy consumption even if the backhaul power consumption is included in the analysis. Interestingly, picocell only deployments can attain up to 3 times increase in the throughput and 2.27 times reduction in the energy consumed when compared with macro only RANs at high target capacities, while it offers 2 times more throughput and reduces the energy consumption by 12% when compared with the macro/pico HetNet deployments. Further investigations have focused on improving the macrocell RAN by adding more sectors and more antennas. Importantly, the results have shown that adding small cells to the macrocell RAN is more energy efficient than adding more sectors even if adaptive sectorisation techniques are employed. While dimensioning the network by using MIMO base stations results in less consumed energy than using SISO base stations. The impact of traffic offloading to small cell, sleep mode, and inter-cell interference coordination techniques on the throughput and energy consumption in dense heterogeneous network deployments have been investigated. Significant improvements in the throughput and energy efficiency in bit/J were observed. However, a decrease in the energy consumption is obtained only in heterogeneous networks with small cells deployed to service clusters of users. Finally, the same framework is used to evaluate the throughput and energy consumption of massive MIMO deployments to show the superiority of massive MIMOs versus macrocell RANs, small cell deployments and heterogeneous networks in terms of achieving the target capacity with a minimum level of energy consumption. 1.6 times reduction in the energy consumption is achieved by massive MIMOs when compared with picocell only RAN at the same target capacity and when the backhaul power consumption is included in the analysis

On the Use of Directed Moves for Placement in VLSI CAD

Search-based placement methods have long been used for placing integrated circuits targeting the field programmable gate array (FPGA) and standard cell design styles. Such methods offer the potential for high-quality solutions but often come at the cost of long run-times compared to alternative methods. This dissertation examines strategies for enhancing local search heuristics---and in particular, simulated annealing---through the application of directed moves. These moves help to guide a search-based optimizer by focusing efforts on states which are most likely to yield productive improvement, effectively pruning the size of the search space. The engineering theory and implementation details of directed moves are discussed in the context of both field programmable gate array and standard cell designs. This work explores the ways in which such moves can be used to improve the quality of FPGA placements, improve the robustness of floorplan repair and legalization methods for mixed-size standard cell designs, and enhance the quality of detailed placement for standard cell circuits. The analysis presented herein confirms the validity and efficacy of directed moves, and supports the use of such heuristics within various optimization frameworks