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

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

Long Term Evolution (LTE) Network Planning in Ipoh City

Long Term Evolution (LTE) is an evolutionary step towards improving telecommunication to higher step. Long Term Evolution (LTE) or also known as 4G is a radio platform technology which provides minimum latency with maximum data rates and speed. LTE have been implemented in latest phones and still some places does not have this facility due to transformation process. Another issue that we can look through is the capability of the network to support users in the area. The coverage and capacity that have been produced may not satisfy user demand and it might be the factor of a network planning. This project will investigate the impact of the antenna parameters on network planning as well identify range of coverage and capacity that can be provided with a LTE network architecture for Ipoh city. In addition to that, transmission power will be investigated with 2 type of sector which is 3 sector and 6 sector

Interference model and antenna parameters setting effects on 4G-LTE networks coverage

International audienceThe currently emerging Long Term Evolution 4G-LTE cellular networks are based on new technique of transmission called the Orthogonal Frequency Division Multiple Access (OFDMA). This paper shows the interest of robust approach due to the uncertainty of traffic distribution. First, we develop and validate the interference model based on SINR metric for the deployment of the LTE network, and then we use greedy algorithms to show how frequency and tilt parameter settings can impact the coverage performance metric. Two frequency schemes have been compared to validate our model: the frequency reuse 1 scheme whereby the whole available bandwidth is used in each cell/sector and the frequency reuse 3 scheme in which the entire bandwidth is divided into 3 non-overlapping groups and assigned to 3 co-site sectors within each cell

RF Coverage Planning And Analysis With Adaptive Cell Sectorization In Millimeter Wave 5G Networks

The advancement of Fifth Generation Network (5G) technology is well underway, with Mobile Network Operators (MNOs) globally commencing the deployment of 5G networks within the mid-frequency spectrum range (3GHz–6GHz). Nevertheless, the escalating demands for data traffic are compelling MNOs to explore the high-frequency spectrum (24GHz–100GHz), which offers significantly larger bandwidth (400MHz-800 MHz) compared to the mid-frequency spectrum (3GHz–6GHz), which typically provides 50MHz-100MHz of bandwidth. However, it is crucial to note that the higher-frequency spectrum imposes substantial challenges due to exceptionally high free space propagation loss, resulting in 5G cell site coverage being limited to several hundred meters, in contrast to the several kilometers achievable with 4G. Consequently, MNOs are faced with the formidable task of accurately planning and deploying hundreds of new 5G cells to cover the same areas served by a single 4G cell.This dissertation embarks on a comprehensive exploration of Radio Frequency (RF) coverage planning for 5G networks, initially utilizing a conventional three-sector cell architecture. The coverage planning phase reveals potential challenges, including coverage gaps and poor Signal-to-Interference-plus-Noise Ratio (SINR). In response to these issues, the dissertation introduces an innovative cell site architecture that embraces both nine and twelve sector cells, enhancing RF coverage through the adoption of an advanced antenna system designed with subarrays, offering adaptive beamforming and beam steering capabilities. To further enhance energy efficiency, the dissertation introduces adaptive higher-order cell-sectorization (e.g., nine sector cells and twelve sector cells). In this proposed method, all sectors within a twelve-sector cell remain active during peak hours (e.g., daytime) and are reduced to fewer sectors (e.g., nine sectors or six sectors per cell) during off-peak hours (e.g., nighttime). This dynamic adjustment is facilitated by an advanced antenna system utilizing sub-array architecture, which employs adaptive beamforming and beam steering to tailor the beamwidth and radiation angle of each active sector. Simulation results unequivocally demonstrate significant enhancements in RF coverage and SINR with the implementation of higher-order cell-sectorization. Furthermore, the proposed adaptive cell-sectorization method significantly reduces energy consumption during off-peak hours. In addition to addressing RF coverage planning, this dissertation delves into the numerous challenges associated with deploying 5G networks in the higher frequency spectrum (30GHz-300GHz). It encompasses issues such as precise cell site planning, location acquisition, propagation modeling, energy efficiency, backhauling, and more. Furthermore, the dissertation offers valuable insights into future research directions aimed at effectively surmounting these challenges and optimizing the deployment of 5G networks in the high-frequency spectrum

Antenna Downtilt Selection Methods for Radio Access Network Rollout Automation

At Elisa, a project called Mass Rollout Automation has been ongoing for a while, with the goal of automating as many parts of the rollout process as possible. This includes the automation of the Radio Access Network planning process. This type of automation differs from the Self-Organizing Network concept, where automation has a focus on operation and maintenance of a network after deployment. One of the parameters that must be selected during RAN planning is the antenna downtilt, which has an effect on network coverage, capacity and quality of service. The first research question is to determine if automation can reliably enough select the antenna downtilt angle prior to the deployment of a base station. The second research question is to find a downtilt selection method best suited for the Mass Rollout Automation project. To answer the questions two simulation scenarios were created based on the Elisa live network, and quality and coverage related predictions for three proposed downtilt methods were calculated. After analysis of the results, automation was found to be reliable enough for the selection of the downtilt angle, when comparing the predictions against the live network scenario. One method was found to be the best if quality were to be prioritized over coverage. Another method was found to be the overall safest pick, as it kept the network coverage and quality almost equal to the live network