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

Jugulotympanic paragangliomas in southern Finland : a 40-year experience suggests individualized surgical management

Treatment of jugulotympanic paragangliomas (JTPGLs) remains challenging with no clear guidelines for management or follow-up. The aim of this retrospective case-note study was to assess long-term results of operatively and conservatively managed JTPGLs between years 1974-2013. A total of 36 patients with JTPGLs were identified. Clinical characteristics and management outcomes of patients were reviewed. Data were extracted on demographics, symptoms, timing of diagnosis, tumor location and size, embolization, and management, including pre- and post-operative imaging, analysis of operative techniques, and follow-up. Pulsatile tinnitus and hearing loss were the most common presenting symptoms. Thirty-four (94 %) patients were treated with primary surgical therapy and two (6 %) with radiotherapy. The surgical approaches included endaural approach for Fisch Class A tumors and a variety of approaches for Fisch Class B-D tumors with an increasing predilection for function-preserving surgery. Eight (24 %) patients received subtotal resection. Five (15 %) patients had a local recurrence within 10 years after primary surgery. Two (6 %) patients suffered a permanent cranial nerve (CN) deficit after primary surgery. We advocate radical surgery when tumor resection is possible without compromising CNs. Function-preserving surgery with at least a 10-year follow-up for Fisch Class B-D tumors should be considered if CNs are in danger.Peer reviewe

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

Genetic Algorithm Optimized Grid-based RF Fingerprint Positioning in Heterogeneous Small Cell Networks

In this paper we propose a novel optimization algorithm for grid-based RF fingerprinting to improve user equipment (UE) positioning accuracy. For this purpose we have used Multi-objective Genetic Algorithm (MOGA) which enables autonomous calibration of gridcell layout (GCL) for better UE positioning as compared to that of the conventional fingerprinting approach. Performance evaluations were carried out using two different training data-sets consisting of Minimization of Drive Testing measurements obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The robustness of the proposed method has been tested analyzing positioning results from two different areas of interest. Optimization of GCL is performed in two ways: (1) array-wise calibration of the grid-cell units using non-overlapping GCL and (2) creating an overlapping GCL to cover of whole simulation area with different rectangular grid-cell units. Simulation results show that if sufficient amount of training data is available then the proposed method can improve positioning accuracy of 56.74% over the conventional gridbased RF fingerprinting.peerReviewe

Cluster-Based RF Fingerprint Positioning Using LTE and WLAN Outdoor Signals

In this paper we evaluate user-equipment (UE) positioning performance of three cluster-based RF fingerprinting methods using LTE and WLAN signals. Real-life LTE and WLAN data were collected for the evaluation purpose using consumer cellular-mobile handset utilizing ‘Nemo Handy’ drive test software tool. Test results of cluster-based methods were compared to the conventional grid-based RF fingerprinting. The cluster-based methods do not require grid-cell layout and training signature formation as compared to the gridbased method. They utilize LTE cell-ID searching technique to reduce the search space for clustering operation. Thus UE position estimation is done in short time with less computational cost. Among the cluster-based methods Agglomerative Hierarchical Cluster based RF fingerprinting provided best positioning accuracy using a single LTE and six WLAN signal strengths. This method showed an improvement of 42.3 % and 39.8 % in the 68th percentile and 95th percentile of positioning error (PE) over the grid-based RF fingerprinting.peerReviewe

Cluster-based RF fingerprint positioning using LTE and WLAN signal strengths

Wireless Local Area Network (WLAN) positioning has become a popular localization system due to its low-cost installation and widespread availability of WLAN access points. Traditional grid-based radio frequency (RF) fingerprinting (GRFF) suffers from two drawbacks. First it requires costly and non-efficient data collection and updating procedure; secondly the method goes through time-consuming data pre-processing before it outputs user position. This paper proposes Cluster-based RF Fingerprinting (CRFF) to overcome these limitations by using modified Minimization of Drive Tests data which can be autonomously collected by cellular operators from their subscribers. The effect of environmental changes and device variation on positioning accuracy has been carried out. Experimental results show that even under these variations CRFF can improve positioning accuracy by 15.46 and 22.30% in 95 percentile of positioning error as compared to that of GRFF and K-nearest neighbour methods respectively.peerReviewe

An efficient cluster-based outdoor user positioning using LTE and WLAN signal strengths

In this paper we propose a novel cluster-based RF fingerprinting method for outdoor user-equipment (UE) positioning using both LTE and WLAN signals. It uses a simple cost effective agglomerative hierarchical clustering with Davies-Bouldin criterion to select the optimal cluster number. The positioning method does not require training signature formation prior to UE position estimation phase. It is capable of reducing the search space for clustering operation by using LTE cell-ID searching criteria. This enables the method to estimate UE positioning in short time with less computational expense. To validate the cluster-based positioning real-time field measurements were collected using readily available cellular mobile handset equipped with Nemo Handy software. Output results of the proposed method were compared with a single grid-cell layout based RF fingerprinting method. Simulation results show that if a single LTE and six WLAN signal strengths are used then the proposed method can improve positioning accuracy of 35% over the grid-based RF fingerprinting.peerReviewe

An efficient grid-based RF fingerprint positioning algorithm for user location estimation in heterogeneous small cell networks

This paper proposes a novel technique to enhance the performance of grid-based Radio Frequency (RF) fingerprint position estimation framework. First enhancement is an introduction of two overlapping grids of training signatures. As the second enhancement, the location of the testing signature is estimated to be a weighted geometric center of a set of nearest grid units whereas in a traditional grid-based RF fingerprinting only the center point of the nearest grid unit is used for determining the user location. By using the weighting-based location estimation, the accuracy of the location estimation can be improved. The performance evaluation of the enhanced RF fingerprinting algorithm was conducted by analyzing the positioning accuracy of the RF fingerprint signatures obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The performance evaluation indicates that if the interpolation is based on two nearest grid units, then a maximum of 18.8% improvement in positioning accuracy can be achieved over the conventional approach.peerReviewe

Platelet-rich plasma versus corticosteroid injection for treatment of trigger finger : study protocol for a prospective randomized triple-blind placebo-controlled trial

BackgroundTrigger finger is a common hand disorder that limits finger range of motion and causes pain and snapping of the affected finger. Trigger finger is caused by an imbalance of the tendon sheath and the flexor tendon. The initial treatment is generally a local corticosteroid injection around the first annular (A1) pulley. However, it is not unusual that surgical release of the A1 pulley is required. Moreover, adverse events after local corticosteroid injection or operative treatment may occur. Platelet-rich plasma (PRP) has been shown to be safe and to reduce symptoms in different tendon pathologies, such as DeQuervain's disease. However, the effects of PRP on trigger finger have not been studied. The aim of this single-center triple-blind randomized controlled trial is to study whether PRP is non-inferior to corticosteroid injection in treating trigger finger. The secondary outcome is to assess the safety and efficacy of PRP in comparison to placebo.MethodsThe trial is designed as a randomized, controlled, patient-, investigator-, and outcome assessor-blinded, single-center, three-armed 1:1:1 non-inferiority trial. The patients with clinical symptoms of trigger finger will be randomly assigned to treatment with PRP, corticosteroid, or normal saline injection. The primary outcome is Patient-Rated Wrist Evaluation and symptom resolution. Secondary outcomes include Quick-Disabilities of the Arm, Shoulder and Hand; pain; grip strength; finger active range of motion; and complications. Appropriate statistical methods will be applied.DiscussionWe present a novel RCT study design on the use of PRP for the treatment of trigger finger compared to corticosteroid and normal saline injection. The results of the trial will indicate if PRP is appropriate for the treatment of trigger finger.Trial registrationClinicalTrials.gov NCT04167098. Registered on November 18, 2019.Peer reviewe

Flexible Backhauling with Massive MIMO for Ultra-Dense Networks

One of the main challenges for wide-scale deployment and timely adoption of ultra-dense networks (UDNs) in future 5G is the backhaul. Typically, mmW technologies for backhaul require line-of-sight conditions while high-capacity wired based solutions need a significant investment in infrastructure. Such limitations pose practical constrains on the scalability of UDNs and increase the deployment cost of dense networks. In this paper, we consider in-band backhaul for UDNs based on massive MIMO systems in sub-6GHz. In particular, we propose a scheme for allowing simultaneous downlink transmissions in backhaul and access network on a single frequency-band that exploits a novel combination of state-of-the-art practical transmit and receive beamforming techniques. A novel frame structure for allowing a co-existence between massive MIMO based backhaul and UDNs is also proposed. Moreover, a solution for in-band uplink transmissions that exploits time-division-duplex (TDD) and spatial multiple-access is also provided. Extensive numerical results using a realistic system-level simulator are given. Results show that the performance of a UDN with the proposed inband backhaul scheme reaches ~58% of the throughput of a similar access network with ideal (e.g., wired) backhaul. Our results also show that the proposed scheme provides an increase in throughput of ~30% compared to a time division duplex (TDD) scheme for in-band backhaul. Further advantages of the proposed massive MIMO based in-band backhaul scheme for UDNs include reusing both the (scarce) spectrum in sub-6GHz and acquired macro-sites, thus providing a seamless transition from LTE to 5G networks.Peer reviewe