A SON Solution for Sleeping Cell Detection Using Low-Dimensional Embedding of MDT Measurements

Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering an alarm for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless SC situation is detected via drive tests or through complaints registered by the affected customers. In this paper, we present a novel solution to address this problem that makes use of minimization of drive test (MDT) measurements recently standardized by 3GPP and NGMN. To overcome the processing complexity challenge, the MDT measurements are projected to a low-dimensional space using multidimensional scaling method. Then we apply state of the art k-nearest neighbor and local outlier factor based anomaly detection models together with pre-processed MDT measurements to profile the network behaviour and to detect SC. Our numerical results show that our proposed solution can automate the SC detection process with 93 accuracy

Enabling self organisation for future cellular networks.

The rapid growth in mobile communications due to the exponential demand for wireless access is causing the distribution and maintenance of cellular networks to become more complex, expensive and time consuming. Lately, extensive research and standardisation work has been focused on the novel paradigm of self-organising network (SON). SON is an automated technology that allows the planning, deployment, operation, optimisation and healing of the network to become faster and easier by reducing the human involvement in network operational tasks, while optimising the network coverage, capacity and quality of service. However, these SON autonomous features cannot be achieved with the current drive test coverage assessment approach due to its lack of automaticity which results in huge delays and cost. Minimization of drive test (MDT) has recently been standardized by 3GPP as a key self- organising network (SON) feature. MDT allows coverage to be estimated at the base station using user equipment (UE) measurement reports with the objective to eliminate the need for drive tests. However, most MDT based coverage estimation methods recently proposed in literature assume that UE position is known at the base station with 100% accuracy, an assumption that does not hold in reality. In this work, we develop a novel and accurate analytical model that allows the quantification of error in MDT based autonomous coverage estimation (ACE) as a function of error in UE as well as base station (user deployed cell) positioning. We first consider a circular cell with an omnidirectional antenna and then we use a three-sectored cell and see how the system is going to be affected by the UE and the base station (user deployed cell) geographical location information errors. Our model also allows characterization of error in ACE as function of standard deviation of shadowing in addition to the path-loss

Space station gas compressor technology study program, phase 1

The objectives were to identify the space station waste gases and their characteristics, and to investigate compressor and dryer types, as well as transport and storage requirements with tradeoffs leading to a preliminary system definition

Energy Efficiency in the ICT - Profiling Power Consumption in Desktop Computer Systems

Energy awareness in the ICT has become an important issue. Focusing on software, recent work suggested the existence of a relationship between power consumption, software configuration and usage patterns in computer systems. The aim of this work was collecting and analysing power consumption data of general-purpose computer systems, simulating common usage scenarios, in order to extract a power consumption profile for each scenario. We selected two desktop systems of different generations as test machines. Meanwhile, we developed 11 usage scenarios, and conducted several test runs of them, collecting power consumption data by means of a power meter. Our analysis resulted in an estimation of a power consumption value for each scenario and software application used, obtaining that each single scenario introduced an overhead from 2 to 11 Watts, which corresponds to a percentage increase that can reach up to 20% on recent and more powerful systems. We determined that software and its usage patterns impact consistently on the power consumption of computer systems. Further work will be devoted to evaluate how power consumption is affected by the usage of specific system resource

Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

On the Improvement of Cellular Coverage Maps by Filtering MDT Measurements

In cellular systems, network re-planning aims to update network configuration to cope with permanent changes in the environment. In this task, terminal measurements are often used to calibrate performance models integrated in radio network planning tools. In Release 10 of the 3GPP standard, the Minimization of Drive Test (MDT) feature allows the collection of user position correlated to performance statistics or radio events. In practice, positioning errors severely limit the potential of MDT measurements. In this work, a preliminary analysis of a large MDT dataset taken from a commercial Long-Term Evolution (LTE) network shows for the first time several sources of positioning errors not previously reported in the literature. Then, a heuristic filtering algorithm is proposed to discard samples with inaccurate location data. Method assessment is done by checking the impact of filtering on the coverage map built with a real MDT dataset. Results show that the proposed method significantly improves the accuracy of coverage maps by filtering unreliable measurements.European Union’s Horizon 2020 Research and Innovation Programme under the Project H2020 LOCUS under (Grant 871249