Aspects of knowledge mining on minimizing drive tests in self-organizing cellular networks

The demand for mobile data traffic is about to explode and this drives operators to find ways to further increase the offered capacity in their networks. If networks are deployed in the traditional way, this traffic explosion will be addressed by increasing the number of network elements significantly. This is expected to increase the costs and the complexity of planning, operating and optimizing the networks. To ensure effective and cost-efficient operations, a higher degree of automation and self-organization is needed in the next generation networks. For this reason, the concept of self-organizing networks was introduced in LTE covering multitude of use cases. This was specifically done in the areas of self-configuration, self-optimization and selfhealing of networks. From an operator’s perspective, automated collection and analysis of field measurements while complementing the traditional drive test campaigns is one of the top use cases that can provide significant cost savings in self-organizing networks. This thesis studies the Minimization of Drive Tests in self-organizing cellular networks from three different aspects. The first aspect is network operations, and particularly the network fault management process, as the traditional drive tests are often conducted for troubleshooting purposes. The second aspect is network functionality, and particularly the technical details about the specified measurement and signaling procedures in different network elements that are needed for automating the collection of the field measurement data. The third aspect concerns the analysis of the measurement databases that is a process used for increasing the degree of automation and self-awareness in the networks, and particularly the mathematical means for autonomously finding meaningful patterns of knowledge from huge amounts of data. Although the above mentioned technical areas have been widely discussed in previous literature, it has been done separately and only a few papers discuss how for example, knowledge mining is employed for processing field measurement data in a way that minimizes the drive tests in self-organizing LTE networks. The objective of the thesis is to use well known knowledge mining principles to develop novel self-healing and self-optimization algorithms. These algorithms analyze MDT databases to detect coverage holes, sleeping cells and other geographical areas of anomalous network behavior. The results of the research suggest that by employing knowledge mining in processing the MDT databases, one can acquire knowledge for discriminating between different network problems and detecting anomalous network behavior. For example, downlink coverage optimization is enhanced by classifying RLF reports into coverage, interference and handover problems. Moreover, by incorporating a normalized power headroom report with the MDT reports, better discrimination between uplink coverage problems and the parameterization problems is obtained. Knowledge mining is also used to detect sleeping cells by means of supervised and unsupervised learning. The detection framework is based on a novel approach where diffusion mapping is used to learn about network behavior in its healthy state. The sleeping cells are detected by observing an increase in the number of anomalous reports associated with a certain cell. The association is formed by correlating the geographical location of anomalous reports with the estimated dominance areas of the cells. Moreover, RF fingerprint positioning of the MDT reports is studied and the results suggest that RF fingerprinting can provide a quite detailed location estimation in dense heterogeneous networks. In addition, self-optimization of the mobility state estimation parameters is studied in heterogeneous LTE networks and the results suggest that by gathering MDT measurements and constructing statistical velocity profiles, MSE parameters can be adjusted autonomously, thus resulting in reasonably good classification accuracy. The overall outcome of the thesis is as follows. By automating the classification of the measurement reports between certain problems, network engineers can acquire knowledge about the root causes of the performance degradation in the networks. This saves time and resources and results in a faster decision making process. Due to the faster decision making process the duration of network breaks become shorter and the quality of the network is improved. By taking into account the geographical locations of the anomalous field measurements in the network performance analysis, finer granularity for estimating the location of the problem areas can be achieved. This can further improve the operational decision making that guides the corresponding actions for example, where to start the network optimization. Moreover, by automating the time and resource consuming task of tuning the mobility state estimation parameters, operators can enhance the mobility performance of the high velocity UEs in heterogeneous radio networks in a cost-efficient and backward compatible manner

Locating Small Cells Using Geo-located UE Measurement Reports & RF Fingerprinting

This paper proposes a number of methods to determine potential small cell site locations using geo-located UE measurement reports in order to maximise the traffic offload from the macrocell network onto the small cells. The paper also shows how the information contained within the measurement reports can be used to create “RF fingerprints#x201D; which in turn can be used to discard UE measurement reports with erroneous location information and by doing so increase the effectiveness of the small cell placement algorithm. Simulations are presented which suggest that when addressing traffic hotspots in central London using small cells with coverage radii of 50m and 100m, the gains provided by the placement algorithms using simple RF fingerprinting technique are significant for UE reports with large location errors (>100m RMS error) when compared to techniques not using RF fingerprinting

A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

Spectrum cartography techniques, challenges, opportunities, and applications: A survey

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.publishedVersio

Self-Organizing Networks use cases in commercial deployments

These measurements can be obtained from different sources, but these sources are either expensive or not applicable to any network. To solve this problem, this thesis proposes a method that uses information available in any network so that the calibration of predictive maps is converted into universal without losing accuracy with respect to current methods. Furthermore, the complexity of today's networks makes them prone to failure. To save costs, operators employ network self-healing techniques so that networks are able to self-diagnose and even self-fix when possible. Among the various failures that can occur in mobile communication networks, a common case is the existence of sectors whose radiated signal has been exchanged. This issue appears during the network roll-out when engineers accidentally cross feeders of several antennas. Currently, manual methodology is used to identify this problem. Therefore, this thesis presents an automatic system to detect these cases. Finally, special attention has been paid to the computational efficiency of the algorithms developed in this thesis since they have finally been integrated into commercial tools.Ince their origins, mobile communication networks have undergone major changes imposed by the need for networks to adapt to user demand. To do this, networks have had to increase in complexity. In turn, complexity has made networks increasingly difficult to design and maintain. To mitigate the impact of network complexity, the concept of self-organizing networks (SON) emerged. Self-organized networks aim at reducing the complexity in the design and maintenance of mobile communication networks by automating processes. Thus, three major blocks in the automation of networks are identified: self-configuration, self-optimization and self-healing. This thesis contributes to the state of the art of self-organized networks through the identification and subsequent resolution of a problem in each of the three blocks into which they are divided. With the advent of 5G networks and the speeds they promise to deliver to users, new use cases have emerged. One of these use cases is known as Fixed Wireless Access. In this type of network, the last mile of fiber is replaced by broadband radio access of mobile technologies. Until now, regarding self-configuration, greenfield design methodologies for wireless networks based on mobile communication technologies are based on the premise that users have mobility characteristics. However, in fixed wireless access networks, the antennas of the users are in fixed locations. Therefore, this thesis proposes a novel methodology for finding the optimal locations were to deploy network equipment as well as the configuration of their radio parameters in Fixed Wireless Access networks. Regarding self-optimization of networks, current algorithms make use of signal maps of the cells in the network so that the changes that these maps would experience after modifying any network parameter can be estimated. In order to obtain these maps, operators use predictive models calibrated through real network measurements

Mobility management in multi-RAT multiI-band heterogeneous networks

Support for user mobility is the raison d'etre of mobile cellular networks. However, mounting pressure for more capacity is leading to adaption of multi-band multi-RAT ultra-dense network design, particularly with the increased use of mmWave based small cells. While such design for emerging cellular networks is expected to offer manyfold more capacity, it gives rise to a new set of challenges in user mobility management. Among others, frequent handovers (HO) and thus higher impact of poor mobility management on quality of user experience (QoE) as well as link capacity, lack of an intelligent solution to manage dual connectivity (of user with both 4G and 5G cells) activation/deactivation, and mmWave cell discovery are the most critical challenges. In this dissertation, I propose and evaluate a set of solutions to address the aforementioned challenges. The beginning outcome of our investigations into the aforementioned problems is the first ever taxonomy of mobility related 3GPP defined network parameters and Key Performance Indicators (KPIs) followed by a tutorial on 3GPP-based 5G mobility management procedures. The first major contribution of the thesis here is a novel framework to characterize the relationship between the 28 critical mobility-related network parameters and 8 most vital KPIs. A critical hurdle in addressing all mobility related challenges in emerging networks is the complexity of modeling realistic mobility and HO process. Mathematical models are not suitable here as they cannot capture the dynamics as well as the myriad parameters and KPIs involved. Existing simulators also mostly either omit or overly abstract the HO and user mobility, chiefly because the problems caused by poor HO management had relatively less impact on overall performance in legacy networks as they were not multi-RAT multi-band and therefore incurred much smaller number of HOs compared to emerging networks. The second key contribution of this dissertation is development of a first of its kind system level simulator, called SyntheticNET that can help the research community in overcoming the hurdle of realistic mobility and HO process modeling. SyntheticNET is the very first python-based simulator that fully conforms to 3GPP Release 15 5G standard. Compared to the existing simulators, SyntheticNET includes a modular structure, flexible propagation modeling, adaptive numerology, realistic mobility patterns, and detailed HO evaluation criteria. SyntheticNET’s python-based platform allows the effective application of Artificial Intelligence (AI) to various network functionalities. Another key challenge in emerging multi-RAT technologies is the lack of an intelligent solution to manage dual connectivity with 4G as well 5G cell needed by a user to access 5G infrastructure. The 3rd contribution of this thesis is a solution to address this challenge. I present a QoE-aware E-UTRAN New Radio-Dual Connectivity (EN-DC) activation scheme where AI is leveraged to develop a model that can accurately predict radio link failure (RLF) and voice muting using the low-level measurements collected from a real network. The insights from the AI based RLF and mute prediction models are then leveraged to configure sets of 3GPP parameters to maximize EN-DC activation while keeping the QoE-affecting RLF and mute anomalies to minimum. The last contribution of this dissertation is a novel solution to address mmWave cell discovery problem. This problem stems from the highly directional nature of mmWave transmission. The proposed mmWave cell discovery scheme builds upon a joint search method where mmWave cells exploit an overlay coverage layer from macro cells sharing the UE location to the mmWave cell. The proposed scheme is made more practical by investigating and developing solutions for the data sparsity issue in model training. Ability to work with sparse data makes the proposed scheme feasible in realistic scenarios where user density is often not high enough to provide coverage reports from each bin of the coverage area. Simulation results show that the proposed scheme, efficiently activates EN-DC to a nearby mmWave 5G cell and thus substantially reduces the mmWave cell discovery failures compared to the state of the art cell discovery methods

Enabling Energy-Efficient and Backhaul-aware Next Generation Heterogeneous Networks

Heterogeneous networks have been firmly established as the direction in which next-generation cellular networks are evolving. We consider the dense deployment of small cells to provide enhanced capacity, while the macro cells provide wide area coverage. With the development of dual connectivity technology, deploying small cells on dedicated carriers has become an attractive option, with enhanced flexibility for splitting traffic within the network. The power consumption and latency requirements of the backhaul link are also gaining increasing prominence due to these factors. Backhaul link quality itself is expected to play an important role in influencing the deployment costs of next-generation 5G systems.  Energy efficiency as a network design paradigm is also gaining relevance due to the increasing impact cellular networks are having on the global carbon emission footprint. For operators, improving energy efficiency has the added advantage of reducing network operation expenditures. For the end-users, avoiding unnecessary draining of device battery power would improve the user experience.  In this work, we study energy efficient mechanisms for inter-frequency small cell discovery, based on mobility awareness and proximity estimation. Further, we apply generalized small cell discovery concepts in a device-to-device communication environment in order to optimize the energy consumption for device discovery. We also look at energy efficient small cell operations based on traffic characteristics and load constraints-based offloading in relation to the radio access and backhaul power consumption. In addition we study intelligent means of dist-ributing delay-critical functionalities such as Hybrid ARQ, while centralizing the computationally-intense processes in a 5G, cloud-based, centralized radio access network. Numerical evaluations done using a LTE-Advanced heterogeneous network and analytical settings indicate that significant UE and network power consumption reductions could be achieved with the considered enhancements. Using the optimized small cell operation schemes investigated in this work, reductions in network power consumption and consequent improvements in the overall energy efficiency of the network were observed. The performance of the distributed opportunistic HARQ mechanism for a centralized radio access network is compared to the optimal and static retransmission mechanisms, and the evaluated scheme is shown to perform close to the optimal mechanism, while operating with a non-ideal backhaul link

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