Cell identity allocation and optimisation of handover parameters in self-organised LTE femtocell networks

A thesis submitted to the University of Bedfordshire in partial ful lment of the requirements for the degree of Doctor of PhilosophyFemtocell is a small cellular base station used by operators to extend indoor service coverage and enhance overall network performance. In Long Term Evolution (LTE), femtocell works under macrocell coverage and combines with the macrocell to constitute the two-tier network. Compared to the traditional single-tier network, the two-tier scenario creates many new challenges, which lead to the 3rd Generation Partnership Project (3GPP) implementing an automation technology called Self-Organising Network (SON) in order to achieve lower cost and enhanced network performance. This thesis focuses on the inbound and outbound handovers (handover between femtocell and macrocell); in detail, it provides suitable solutions for the intensity of femtocell handover prediction, Physical Cell Identity (PCI) allocation and handover triggering parameter optimisation. Moreover, those solutions are implemented in the structure of SON. In order to e ciently manage radio resource allocation, this research investigates the conventional UE-based prediction model and proposes a cell-based prediction model to predict the intensity of a femtocell's handover, which overcomes the drawbacks of the conventional models in the two-tier scenario. Then, the predictor is used in the proposed dynamic group PCI allocation approach in order to solve the problem of PCI allocation for the femtocells. In addition, based on SON, this approach is implemented in the structure of a centralised Automated Con guration of Physical Cell Identity (ACPCI). It overcomes the drawbacks of the conventional method by reducing inbound handover failure of Cell Global Identity (CGI). This thesis also tackles optimisation of the handover triggering parameters to minimise handover failure. A dynamic hysteresis-adjusting approach for each User Equipment (UE) is proposed, using received average Reference Signal-Signal to Interference plus Noise Ratio (RS-SINR) of the UE as a criterion. Furthermore, based on SON, this approach is implemented in the structure of hybrid Mobility Robustness Optimisation (MRO). It is able to off er the unique optimised hysteresis value to the individual UE in the network. In order to evaluate the performance of the proposed approach against existing methods, a System Level Simulation (SLS) tool, provided by the Centre for Wireless Network Design (CWiND) research group, is utilised, which models the structure of two-tier communication of LTE femtocell-based networks

Project Final Report – FREEDOM ICT-248891

This document is the final publishable summary report of the objective and work carried out within the European Project FREEDOM, ICT-248891.This document is the final publishable summary report of the objective and work carried out within the European Project FREEDOM, ICT-248891.Preprin

Predictive QoS for cellular connected UAV payload communication

Unmanned aerial vehicles (UAVs), or drones, are revolutionizing industries due to their versatility, affordability and applicability. Reliable communication links are essential for UAV operations, especially for beyond visual line of sight scenarios where drones are flown beyond the operator’s line of sight. Cellular networks, particularly in the context of 5G and beyond, offer potential solutions to meet the data-intensive demands of UAV applications. This study explores the feasibility of predictive quality of service for forecasting uplink (UL) throughput quality of service (QoS) parameter in UAV payload communication links. Comprehensive field tests were conducted to ensure accurate real-world results, as simulations may not fully capture real-world complexities. Field trial measurements were conducted in a sub-urban area to evaluate drone performance at various altitudes and bands. This sheds light on potential challenges and trade-offs for cellular-connected drones and their coexistence with terrestrial users. Drones flying at high altitudes often experience line of sight propagation, causing them to undergo frequent handovers between multiple base stations. Field trials demonstrated that drones connected to a 700 MHz signal encountered minimal interference and no handovers. Conversely, drones connected to the 3500 MHz frequency band faced multiple handovers, highlighting the complexities of UAV-cellular integration and emphasizing the significance of frequency band selection in drone applications. By harnessing machine learning (ML) models and comparative analysis of centralized and federated learning methods, this research investigates ML model performances in forecasting UL throughput based on prediction accuracy. The findings emphasize the importance of diverse training data and highlight the impact of frequency bands on UAV communication. These insights lay the groundwork for addressing UAV communication complexities and advancing the integration of machine learning and network dynamics for improving UAV operations

Models and optimisation methods for interference coordination in self-organising cellular networks

A thesis submitted for the degree of Doctor of PhilosophyWe are at that moment of network evolution when we have realised that our telecommunication systems should mimic features of human kind, e.g., the ability to understand the medium and take advantage of its changes. Looking towards the future, the mobile industry envisions the use of fully automatised cells able to self-organise all their parameters and procedures. A fully self-organised network is the one that is able to avoid human involvement and react to the fluctuations of network, traffic and channel through the automatic/autonomous nature of its functioning. Nowadays, the mobile community is far from this fully self-organised kind of network, but they are taken the first steps to achieve this target in the near future. This thesis hopes to contribute to the automatisation of cellular networks, providing models and tools to understand the behaviour of these networks, and algorithms and optimisation approaches to enhance their performance. This work focuses on the next generation of cellular networks, in more detail, in the DownLink (DL) of Orthogonal Frequency Division Multiple Access (OFDMA) based networks. Within this type of cellular system, attention is paid to interference mitigation in self-organising macrocell scenarios and femtocell deployments. Moreover, this thesis investigates the interference issues that arise when these two cell types are jointly deployed, complementing each other in what is currently known as a two-tier network. This thesis also provides new practical approaches to the inter-cell interference problem in both macro cell and femtocell OFDMA systems as well as in two-tier networks by means of the design of a novel framework and the use of mathematical optimisation. Special attention is paid to the formulation of optimisation problems and the development of well-performing solving methods (accurate and fast)

Kanavien yhdistämistekniikan suorituskyvyn arviointi edistyneissä LTE-järjestelmissä

Carrier Aggregation (CA) is an essential technology component in LTE-Advanced (LTE-A). CA is capable of combining up to five Long Term Evolution (LTE) carriers to be used for multicarrier transmission in both downlink and uplink. CA provides increased throughputs, additional capacity and possibilities for load balancing. This thesis presents the key features of CA. Furthermore, the results from CA performance measurements are analyzed and presented. The measurements were conducted in live network to evaluate the end-user experience. The objective was to determine whether CA is capable of delivering the performance that could be theoretically expected. The performance was measured in LTE-A radio network using 2x20 MHz bandwidth with 2x2 MIMO configuration and Category 6 User Equipment (UE). Only downlink CA was measured, since uplink CA capable UEs were not commercially available. The performance was evaluated with stationary and mobility measurements. The results indicate that CA is capable of providing the expected performance gain. In good radio conditions the maximum downlink throughput is close to the 300 Mbit/s. Furthermore, CA performs well in poor radio conditions. The performance gain can be more than 100 %, if the additional carrier is on an unused band. In CA, a secondary component carrier is configured for the UE, in addition to the primary carrier. The operation is performed in connected state either after connection setup or radio handover. The delay in secondary carrier addition was measured to evaluate the impact on user experience. The results indicate that the secondary carrier addition after connection setup or handover is sufficiently fast, and do not have an impact to the user experience.Kanavien yhdistäminen (engl. Carrier Aggregation) on oleellinen tekniikka edistyneessä Long Term Evolution järjestelmässä. Sen avulla on mahdollista yhdistää enintään viisi LTE taajuutta käytettäväksi monikanavaiseen ala- ja ylälinkin lähetykseen. CA mahdollistaa aiempaa suuremmat siirtonopeudet, lisää verkon kapasiteettia sekä antaa mahdollisuuden kuormanjakoon eri taajuuksien välillä. Tässä työssä esitellään CA:n keskeiset ominaisuudet. Lisäksi CA:n suorituskykymittauksien tulokset on analysoitu ja esitelty. Mittaukset toteutettiin operaattorin tuotantoverkossa, jotta loppukäyttäjän saamaa kokemusta on mahdollista arvioida. Tavoitteena oli selvittää, pystyykö CA tarjoamaan sellaista suorituskykyä, jota voidaan teorian perusteella odottaa. Suorituskykyä mitattiin LTE-radioverkossa käyttäen 2x20 MHz kaistanleveyttä ja 2x2 MIMO:n kokoonpanoa sekä kategorian 6 päätelaitetta. Mittaukset suoritettiin vain alalinkissä, sillä ylälinkin CA-kykyisiä päätelaitteita ei ollut kaupallisesti saatavilla. Suorituskykyä on arvioitu sekä piste- että mobiliteettimittauksilla. Tulokset osoittavat, että CA pystyy tarjoamaan oletetun suorituskyvyn parannuksen. Hyvissä radio olosuhteissa maksimi datanopeus alalinkissä on lähes 300 Mbit/s. Lisäksi CA toimii hyvin myös huonoissa radio-olosuhteissa. Suorituskyvyn parannus voi olla enemmän kuin 100 %, jos lisätty toinen kanava on vähemmän käytetyllä taajuuskaistalla. CA:ssa toissijainen kanava konfiguroidaan päätelaitteelle ensisijaisen lisäksi. Operaatio suoritetaan yhteystilassa joko yhteyden muodostamisen tai solunvaihdon jälkeen. Toissijaisen kanavan lisäämisen aiheuttama viive mitattiin, jotta sen vaikutus käyttökokemukseen voitiin arvioida. Tulokset osoittavat, että toissijaisen kanavan lisääminen yhteyden muodostamisen tai solunvaihdon jälkeen on riittävän nopea operaatio, eikä sillä ole vaikutusta käyttökokemukseen

A Study about Heterogeneous Network Issues Management based on Enhanced Inter-cell Interference Coordination and Machine Learning Algorithms

Under the circumstance of fast growing demands for mobile data, Heterogeneous Networks (HetNets) has been considered as one of the key technologies to solve 1000 times mobile data challenge in the coming decade. Although the unique multi-tier topology of HetNets has achieved high spectrum efficiency and enhanced Quality of Service (QoS), it also brings a series of critical issues. In this thesis, we present an investigation on understanding the cause of HetNets challenges and provide a research on state of arts techniques to solve three major issues: interference, offloading and handover. The first issue addressed in the thesis is the cross-tier interference of HetNets. We introduce Almost Blank Subframes (ABS) to free small cell UEs from cross-tier interference, which is the key technique of enhanced Inter-Cell Interference Coordination (eICIC). Nash Bargain Solution (NBS) is applied to optimize ABS ratio and UE partition. Furthermore, we propose a power based multi-layer NBS Algorithm to obtain optimal parameters of Further enhanced Inter-cell Interference Coordination (FeICIC), which significantly improve macrocell efficiency compared to eICIC. This algorithm not only introduces dynamic power ratio but also defined opportunity cost for each layer instead of conventional zero-cost partial fairness. Simulation results show the performance of proposed algorithm may achieve up to 31.4% user throughput gain compared to eICIC and fixed power ratio FeICIC. This thesis’ second focusing issue is offloading problem of HetNets. This includes (1) UE offloading from macro cell and (2) small cell backhaul offloading. For first aspect, we have discussed the capability of machine learning algorithms tackling this challenge and propose the User-Based K-means Algorithm (UBKCA). The proposed algorithm establishes a closed loop Self-Organization system on our HetNets scenario to maintain desired offloading factor of 50%, with cell edge user factor 17.5% and CRE bias of 8dB. For second part, we further apply machine learning clustering method to establish cache system, which may achieve up to 70.27% hit-ratio and reduce request latency by 60.21% for Youtube scenario. K-Nearest Neighbouring (KNN) is then applied to predict new users’ content preference and prove our cache system’s suitability. Besides that, we have also proposed a system to predict users’ content preference even if the collected data is not complete. The third part focuses on offloading phase within HetNets. This part detailed discusses CRE’s positive effect on mitigating ping-pong handover during UE offloading, and CRE’s negative effect on increasing cross-tier interference. And then a modified Markov Chain Process is established to map the handover phases for UE to offload from macro cell to small cell and vice versa. The transition probability of MCP has considered both effects of CRE so that the optimal CRE value for HetNets can be achieved, and result for our scenario is 7dB. The combination of CRE and Handover Margin is also discussed

System optimisation and radio planning for future LTE-advanced

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis work is related to wireless communication. In this Thesis three main issues are addressed for future cellular networks: power consumption, interference and mobility. These issues continue to be a burden on the system’s performance as long as technology keeps evolving. In the presented chapters, the focus was to introduce greater intelligence to the LTE system algorithms and bring to them a dynamic and self-organizing approach. The first approach concerns power consumption in wireless terminals. The currently applied solution to save energy is the DRX mechanism. It organizes the time when the terminal wakes up and starts receiving data, and when it goes into sleep mode in order to save its battery power. The current DRX is described as static or fixed which makes its parameters unsuitable for the nature of the bursty traffic. In this work an adaptive DRX mechanism is proposed and evaluated as the wireless terminal battery saving algorithm. The second approach is co-channel interference mitigation. To increase the system’s capacity and avoid spectrum scarcity, small cells such as Femtocells are deployed and operate on the same frequency bands as the Macrocell. Although these small nodes increase the system capacity, however, the challenges will be in the femtocells planning and management in addition to the interference issues. Here a dynamic interference cancellation approach is presented to enable the Femtocell to track the allocated resources to the Macro-users, and to avoid using them. The third approach concerns mobility management in heterogeneous networks. The wireless terminal may have different mobility levels during handover which increases the handover failures due to failure in handover commands and aging of the reported parameters. This issue is presented in detail with the aim to avoid performance degradation and improve the reporting mechanisms during fast mobility levels. For this regard the presented method proposes more cooperation between the serving cell and the end-user so that the large amount of overhead and measurement are reduced. Simulations with different configurations are conducted to present the results of the proposed models. Results show that the proposed models bring improvements to the LTE system. The enhanced self-organized architecture in the three presented approaches performs well in terms of power saving, dynamic spectrum utilization by Femtocells, and mitigation of sudden throughput degradation due to the serving cell’s downlink signal outage during mobility.Brunel University Londo

Study of Resource Allocation Schemes for a 2-Hop Network in a Multi-cell Environment

東北工業大学平成26年