Adaptive Cellular Layout in Self-Organizing Networks using Active Antenna Systems

The rapidly growing demand of capacity by wireless services is challenging the mobile industry with a need of new deployment strategies. Besides, the nature of the spatial and temporal distribution of user traffic has become heterogeneous and fluctuating intermittently. Those challenges are currently tackled by network densification and tighter spatial reuse of radio resources by introducing a heterogeneous deployment of small cells embedded in a macro cell layout. Since user traffic is varying both spatially and temporally, a so called busy hour planning is typically applied where enough small cells are deployed at the corresponding locations to meet the expected capacity demand. This deployment strategy, however, is inefficient as it may leave plenty of network resources under-utilized during non-busy hour, i.e., most of the operation time. Such over-provisioning strategy incurs high capital investment on infrastructure (CAPEX) as well as operating cost (OPEX) for operators. Therefore, optimal would be a network with flexible capacity accommodation by following the dynamics of the traffic situation and evading the inefficiencies and the high cost of the fixed deployment approach. The advent of a revolutionizing base station antenna technology called Active Antenna Systems (AAS) is promising to deliver the required flexibility and dynamic deployment solution desired for adaptive capacity provisioning. Having the active radio frequency (RF) components integrated with the radiating elements, AAS supports advanced beamforming features. With AAS-equipped base station, multiple cell-specific beams can be simultaneously created to densify the cell layout by means of an enhanced form of sectorization. The radiation pattern of each cell-beam can be dynamically adjusted so that a conventional cell, for instance, can be split into two distinct cells, if a high traffic concentration is detected. The traffic in such an area is shared among the new cells and by spatially reusing the frequency spectrum, the cell-splitting (sectorization) doubles the total available radio resources at the cost of an increased co-channel interference between the cells. Despite the AAS capability, the realization of flexible sectorization for dynamic cell layout adaptation poses several challenges. One of the challenges is that the expected performance gain from cell densification can be offset by the ensuing co-channel interference in the system. It is also obvious that a self-organized autonomous management and configuration is needed, if cell deployment must follow the variation of the user traffic over time and space by means of a sectorization procedure. The automated mechanism is desired to enhance the system performance and optimize the user experience by automatically controlling the sectorization process. With such a dynamic adaptation scheme, the self-organizing network (SON) facilities are getting a new dimension in terms of controlling the flexible cell layout changes as the environment including the radio propagation characteristics cannot be assumed stationary any longer. To fully exploit the flexible sectorization feature in three-dimensional space, reliable and realistic propagation models are required which are able to incorporate the dependency of the radio channel characteristics in the elevation domain. Analysis of the complex relationship among various system parameters entails a comprehensive model that properly describes the AAS-sectorization for conducting detailed investigation and carrying out precise evaluation of the ensuing system performance. A novel SON algorithm that automates the AAS-sectorization procedure is developed. The algorithm controls the activation/deactivation of cell-beams enabling the sectorization based cell layout adjustment adaptively. In order to effectively meet the dynamically varying network capacity demand that varies according to the spatial user distribution, the developed SON algorithm monitors the load of the cell, the spatial traffic concentrations and adapts the underlying cell coverage layout by autonomously executing the sectorization either in the horizontal or vertical plane. The SON algorithm specifies various procedures which rely on real time network information collected using actual signal measurement reports from users. The particular capability of the algorithm is evading unforeseen system performance degradation by properly executing the sectorization not only where in the network and when it is needed, but also only if the ensuing co-channel interference does not have adverse impact on the user experience. To guarantee the optimality of the network performance after sectorization, a performance metric that takes both the expectable gain from radio resource and impact of the co-channel interference into account is developed. In order to combat the severity of the inter-cell interference problem that arises with AAS-sectorization between the co-channel operated cells, an interference mitigation scheme is developed in this thesis. The proposed scheme coordinates the data transmission between the co-sited cells by the transmission muting principle. To ensure that the transmission muting is not degrading the overall system performance by blanking more data transmission, a new SON algorithm that controls the optimal usage the proposed scheme is developed. To appropriately characterize the spatial separation of the cell beams being activated with sectorization, a novel propagation shadowing model that incorporates elevation tilt parameter is developed. The new model addresses the deficiencies of the existing tilt-independent shadowing model which inherently assumes a stationary propagation characteristics in the elevation domain. The tilt-dependent shadowing model is able to statistically characterize the elevation channel variability with respect to the tilt configuration settings. Simplified 3D beamforming models and beam pattern synthesis approaches required for fast cell layout adaptation and dynamic configuration of the AAS parameters are developed for the realization of various forms of AAS-based sectorization. Horizontal and vertical sectorization are the two forms of AAS-based sectorization considered in this thesis where two beams are simultaneously created from a single AAS to split the underlying coverage layout in horizontal or vertical domain, respectively. The performance of the developed theoretical AAS-sectorization concepts and models are examined by means of system level simulations considering the Long Term Evolution-Advanced (LTE-A) macro-site deployment within exemplifying scenarios. Simulation results have demonstrated that the SON mechanism is able to follow the different conditions when and where the sectorization delivers superior performance or adversely affects the user experience. Impacts on the performance of existing SON operations, like Mobility Robustness Optimization (MRO), which are relying on stationary cell layout conditions have been studied. Further investigations are carried out in combination with the cell layout changes triggered by the dynamic AAS-based sectorization. The observed results have confirmed that proper coordination is needed between the SON scheme developed for AAS sectorization and the MRO operation to evade unforeseen performance degradation and to ensure a seamless user experience. The technical concepts developed in this thesis further have impacted the $3^\textrm{rd}$ Generation Partnership Project (3GPP) SON for AAS Work Item (WI) discussed in the Radio Access Network (RAN)-3 Work Group (WG). In particular, the observed study results dealing with the interworking of the existing SON features and AAS sectorization have been noted in the standardization work

Self-Optimization of Coverage and Capacity in LTE using Adaptive Antenna Systems

In cellular radio networks, the selection of antenna parameters and techniques for antennas plays a key role for capacity and coverage area. Not only network performance is affected by suboptimal network planning but also it is affected by the dynamic radio environment. Therefore, antenna parameters should be adjusted adaptively. Since reacting to the changed situation manually is very expensive and time consuming, The Third Generation Partnership Project (3GPP) introduced the Coverage and Capacity Optimization (CCO) use case for Long Term Evolution (LTE) under the topic of Self-Organizing Network (SON). This thesis work provides a detailed analysis of the optimization space of antenna parameters and compares different tilt techniques as well as discusses vertical sectorization as a novel capacity optimization approach. The work continues by further focusing on the self optimization of coverage and capacity using Adaptive Antenna Systems (AAS) on the basis of findings in the previous simulations on antenna parameters. Evaluations are performed by mapping link-level simulation results into a system level LTE simulator that models antennas in details and propagation in three dimensions

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Self-Organized Coverage and Capacity Optimization for Cellular Mobile Networks

Die zur Erfüllung der zu erwartenden Steigerungen übertragener Datenmengen notwendige größere Heterogenität und steigende Anzahl von Zellen werden in der Zukunft zu einer deutlich höheren Komplexität bei Planung und Optimierung von Funknetzen führen. Zusätzlich erfordern räumliche und zeitliche Änderungen der Lastverteilung eine dynamische Anpassung von Funkabdeckung und -kapazität (Coverage-Capacity-Optimization, CCO). Aktuelle Planungs- und Optimierungsverfahren sind hochgradig von menschlichem Einfluss abhängig, was sie zeitaufwändig und teuer macht. Aus diesen Grnden treffen Ansätze zur besseren Automatisierung des Netzwerkmanagements sowohl in der Industrie, als auch der Forschung auf groes Interesse.Selbstorganisationstechniken (SO) haben das Potential, viele der aktuell durch Menschen gesteuerten Abläufe zu automatisieren. Ihnen wird daher eine zentrale Rolle bei der Realisierung eines einfachen und effizienten Netzwerkmanagements zugeschrieben. Die vorliegende Arbeit befasst sich mit selbstorganisierter Optimierung von Abdeckung und Übertragungskapazität in Funkzellennetzwerken. Der Parameter der Wahl hierfür ist die Antennenneigung. Die zahlreichen vorhandenen Ansätze hierfür befassen sich mit dem Einsatz heuristischer Algorithmen in der Netzwerkplanung. Im Gegensatz dazu betrachtet diese Arbeit den verteilten Einsatz entsprechender Optimierungsverfahren in den betreffenden Netzwerkknoten. Durch diesen Ansatz können zentrale Fehlerquellen (Single Point of Failure) und Skalierbarkeitsprobleme in den kommenden heterogenen Netzwerken mit hoher Knotendichte vermieden werden.Diese Arbeit stellt einen "Fuzzy Q-Learning (FQL)"-basierten Ansatz vor, ein einfaches Maschinenlernverfahren mit einer effektiven Abstraktion kontinuierlicher Eingabeparameter. Das CCO-Problem wird als Multi-Agenten-Lernproblem modelliert, in dem jede Zelle versucht, ihre optimale Handlungsstrategie (d.h. die optimale Anpassung der Antennenneigung) zu lernen. Die entstehende Dynamik der Interaktion mehrerer Agenten macht die Fragestellung interessant. Die Arbeit betrachtet verschiedene Aspekte des Problems, wie beispielsweise den Unterschied zwischen egoistischen und kooperativen Lernverfahren, verteiltem und zentralisiertem Lernen, sowie die Auswirkungen einer gleichzeitigen Modifikation der Antennenneigung auf verschiedenen Knoten und deren Effekt auf die Lerneffizienz.Die Leistungsfähigkeit der betrachteten Verfahren wird mittels eine LTE-Systemsimulators evaluiert. Dabei werden sowohl gleichmäßig verteilte Zellen, als auch Zellen ungleicher Größe betrachtet. Die entwickelten Ansätze werden mit bekannten Lösungen aus der Literatur verglichen. Die Ergebnisse zeigen, dass die vorgeschlagenen Lösungen effektiv auf Änderungen im Netzwerk und der Umgebung reagieren können. Zellen stellen sich selbsttätig schnell auf Ausfälle und Inbetriebnahmen benachbarter Systeme ein und passen ihre Antennenneigung geeignet an um die Gesamtleistung des Netzes zu verbessern. Die vorgestellten Lernverfahren erreichen eine bis zu 30 Prozent verbesserte Leistung als bereits bekannte Ansätze. Die Verbesserungen steigen mit der Netzwerkgröße.The challenging task of cellular network planning and optimization will become more and more complex because of the expected heterogeneity and enormous number of cells required to meet the traffic demands of coming years. Moreover, the spatio-temporal variations in the traffic patterns of cellular networks require their coverage and capacity to be adapted dynamically. The current network planning and optimization procedures are highly manual, which makes them very time consuming and resource inefficient. For these reasons, there is a strong interest in industry and academics alike to enhance the degree of automation in network management. Especially, the idea of Self-Organization (SO) is seen as the key to simplified and efficient cellular network management by automating most of the current manual procedures. In this thesis, we study the self-organized coverage and capacity optimization of cellular mobile networks using antenna tilt adaptations. Although, this problem is widely studied in literature but most of the present work focuses on heuristic algorithms for network planning tool automation. In our study we want to minimize this reliance on these centralized tools and empower the network elements for their own optimization. This way we can avoid the single point of failure and scalability issues in the emerging heterogeneous and densely deployed networks.In this thesis, we focus on Fuzzy Q-Learning (FQL), a machine learning technique that provides a simple learning mechanism and an effective abstraction level for continuous domain variables. We model the coverage-capacity optimization as a multi-agent learning problem where each cell is trying to learn its optimal action policy i.e. the antenna tilt adjustments. The network dynamics and the behavior of multiple learning agents makes it a highly interesting problem. We look into different aspects of this problem like the effect of selfish learning vs. cooperative learning, distributed vs. centralized learning as well as the effect of simultaneous parallel antenna tilt adaptations by multiple agents and its effect on the learning efficiency.We evaluate the performance of the proposed learning schemes using a system level LTE simulator. We test our schemes in regular hexagonal cell deployment as well as in irregular cell deployment. We also compare our results to a relevant learning scheme from literature. The results show that the proposed learning schemes can effectively respond to the network and environmental dynamics in an autonomous way. The cells can quickly respond to the cell outages and deployments and can re-adjust their antenna tilts to improve the overall network performance. Additionally the proposed learning schemes can achieve up to 30 percent better performance than the available scheme from literature and these gains increases with the increasing network size

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

A PARADIGM SHIFTING APPROACH IN SON FOR FUTURE CELLULAR NETWORKS

The race to next generation cellular networks is on with a general consensus in academia and industry that massive densification orchestrated by self-organizing networks (SONs) is the cost-effective solution to the impending mobile capacity crunch. While the research on SON commenced a decade ago and is still ongoing, the current form (i.e., the reactive mode of operation, conflict-prone design, limited degree of freedom and lack of intelligence) hinders the current SON paradigm from meeting the requirements of 5G. The ambitious quality of experience (QoE) requirements and the emerging multifarious vision of 5G, along with the associated scale of complexity and cost, demand a significantly different, if not totally new, approach to SONs in order to make 5G technically as well as financially feasible. This dissertation addresses these limitations of state-of-the-art SONs. It first presents a generic low-complexity optimization framework to allow for the agile, on-line, multi-objective optimization of future mobile cellular networks (MCNs) through only top-level policy input that prioritizes otherwise conflicting key performance indicators (KPIs) such as capacity, QoE, and power consumption. The hybrid, semi-analytical approach can be used for a wide range of cellular optimization scenarios with low complexity. The dissertation then presents two novel, user-mobility, prediction-based, proactive self-optimization frameworks (AURORA and OPERA) to transform mobility from a challenge into an advantage. The proposed frameworks leverage mobility to overcome the inherent reactiveness of state-of-the-art self-optimization schemes to meet the extremely low latency and high QoE expected from future cellular networks vis-à-vis 5G and beyond. The proactiveness stems from the proposed frameworks’ novel capability of utilizing past hand-over (HO) traces to determine future cell loads instead of observing changes in cell loads passively and then reacting to them. A semi-Markov renewal process is leveraged to build a model that can predict the cell of the next HO and the time of the HO for the users. A low-complexity algorithm has been developed to transform the predicted mobility attributes to a user-coordinate level resolution. The learned knowledge base is used to predict the user distribution among cells. This prediction is then used to formulate a novel (i) proactive energy saving (ES) optimization problem (AURORA) that proactively schedules cell sleep cycles and (ii) proactive load balancing (LB) optimization problem (OPERA). The proposed frameworks also incorporate the effect of cell individual offset (CIO) for balancing the load among cells, and they thus exploit an additional ultra-dense network (UDN)-specific mechanism to ensure QoE while maximizing ES and/or LB. The frameworks also incorporates capacity and coverage constraints and a load-aware association strategy for ensuring the conflict-free operation of ES, LB, and coverage and capacity optimization (CCO) SON functions. Although the resulting optimization problems are combinatorial and NP-hard, proactive prediction of cell loads instead of reactive measurement allows ample time for combination of heuristics such as genetic programming and pattern search to find solutions with high ES and LB yields compared to the state of the art. To address the challenge of significantly higher cell outage rates in anticipated in 5G and beyond due to higher operational complexity and cell density than legacy networks, the dissertation’s fourth key contribution is a stochastic analytical model to analyze the effects of the arrival of faults on the reliability behavior of a cellular network. Assuming exponential distributions for failures and recovery, a reliability model is developed using the continuous-time Markov chains (CTMC) process. Unlike previous studies on network reliability, the proposed model is not limited to structural aspects of base stations (BSs), and it takes into account diverse potential fault scenarios; it is also capable of predicting the expected time of the first occurrence of the fault and the long-term reliability behavior of the BS. The contributions of this dissertation mark a paradigm shift from the reactive, semi-manual, sub-optimal SON towards a conflict-free, agile, proactive SON. By paving the way for future MCN’s commercial and technical viability, the new SON paradigm presented in this dissertation can act as a key enabler for next-generation MCNs

Energy efficiency comparison between 2.1 GHz and 28 GHz based communication networks

Mobile communications have revolutionized the way we communicate around the globe, making communication easier, faster and cheaper. In the first three generations of mobile networks, the primary focus was on voice calls, and as such, the traffic on the networks was not as heavy as it currently is. Towards the fourth generation however, there was an explosive increase in mobile data traffic, driven in part by the heavy use of smart phones, tablets and cloud services, that is in turn increasing heavy energy consumption by the mobile networks to meet increased demand. Addition of power conditioning equipment adds on to the overall energy consumption of the base stations, necessitating deployment of energy efficient solutions to deal with the impacts and costs of heavy energy consumption. This thesis investigates the energy efficiency performance of mobile networks in various scenarios in a dense urban environment. Consideration is given to the future deployment of 5G networks, and simulations are carried out at 2.1 GHz and 28 GHz frequencies with a channel bandwidth of 20 MHz in the 2.1 GHz simulation and 20 MHz in 28 GHz scenario. The channel bandwidth of the 28 GHz system is then increased ten-fold and another system performance evaluation is then done. Parameters used for evaluating the system performance include the received signal strength, signal-to-interference-plus-noise-ratio, spectral efficiency and power efficiency are also considered. The results suggest that deployment of networks using mmWave frequencies with the same parameters as the 2.1 GHz does not improve the overall performance of the system but improves the throughput when a bandwidth of 200 MHz band is allocated. The use of antenna masking with down tilting improves the gains of the system in all three systems. The conclusion drawn is that if all factors are the same, mmWave systems can be installed in the same site locations as 2.1 GHz systems. However, to achieve better performance, some significant modifications would need to be considered, like the use of antenna arrays and beam steering techniques. This simulation has considered outdoor users only, with indoor users eliminated. The parameters in a real network deployment might differ and the results could change, which in turn could change the performance of the system

Capacity Enhancement Approaches for Long Term Evolution networks: Capacity Enhancement-Inspired Self-Organized Networking to Enhance Capacity and Fairness of Traffic in Long Term Evolution Networks by Utilising Dynamic Mobile Base-Stations

The long-term evolution (LTE) network has been proposed to provide better network capacity than the earlier 3G network. Driven by the market, the conventional LTE (3G) network standard could not achieve the expectations of the international mobile telecommunications advanced (IMT-Advanced) standard. To satisfy this gap, the LTE-Advanced was introduced with additional network functionalities to meet up with the IMT-Advanced Standard. In addition, due to the need to minimize operational expenditure (OPEX) and reduce human interventions, the wireless cellular networks are required to be self-aware, self-reconfigurable, self-adaptive and smart. An example of such network involves transceiver base stations (BTSs) within a self-organizing network (SON). Besides these great breakthroughs, the conventional LTE and LTE-Advanced networks have not been designed with the intelligence of scalable capacity output especially in sudden demographic changes, namely during events of football, malls, worship centres or during religious and cultural festivals. Since most of these events cannot be predicted, modern cellular networks must be scalable in terms of capacity and coverage in such unpredictable demographic surge. Thus, the use of dynamic BTSs is proposed to be used in modern and future cellular networks for crowd and demographic change managements. Dynamic BTSs are complements of the capability of SONs to search, determine and deploy less crowded/idle BTSs to densely crowded cells for scalable capacity management. The mobile BTSs will discover areas of dark coverages and fill-up the gap in terms of providing cellular services. The proposed network relieves the LTE network from overloading thus reducing packet loss, delay and improves fair load sharing. In order to trail the best (least) path, a bio-inspired optimization algorithm based on swarm-particle optimization is proposed over the dynamic BTS network. It uses the ant-colony optimization algorithm (ACOA) to find the least path. A comparison between an optimized path and the un-optimized path showed huge gain in terms of delay, fair load sharing and the percentage of packet loss

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio