Optimal Constrained Wireless Emergency Network Antennae Placement

With increasing number of mobile devices, newly introduced smart devices, and the Internet of things (IoT) sensors, the current microwave frequency spectrum is getting rapidly congested. The obvious solution to this frequency spectrum congestion is to use millimeter wave spectrum ranging from 6 GHz to 300 GHz. With the use of millimeter waves, we can enjoy very high communication speeds and very low latency. But, this technology also introduces some challenges that we hardly faced before. The most important one among these challenges is the Line of Sight (LOS) requirement. In the emergent concept of smart cities, the wireless emergency network is set to use millimeter waves. We have worked on the problem of efficiently finding a line of sight for such wireless emergency network antennae in minimal time. We devised two algorithms, Sequential Line of Sight (SLOS) and Tiled Line of Sight (TLOS), both perform better than traditional algorithms in terms of execution time. The tiled line of sight algorithm reduces the time required for a single line of sight query from 200 ms for traditional algorithms to mere 1.7 ms on average

Intra-Cluster Autonomous Coverage Optimization For Dense LTE-A Networks

Self Organizing Networks (SONs) are considered as vital deployments towards upcoming dense cellular networks. From a mobile carrier point of view, continuous coverage optimization is critical for better user perceptions. The majority of SON contributions introduce novel algorithms that optimize specific performance metrics. However, they require extensive processing delays and advanced knowledge of network statistics that may not be available. In this work, a progressive Autonomous Coverage Optimization (ACO) method combined with adaptive cell dimensioning is proposed. The proposed method emphasizes the fact that the effective cell coverage is a variant on actual user distributions. ACO algorithm builds a generic Space-Time virtual coverage map per cell to detect coverage holes in addition to limited or extended coverage conditions. Progressive levels of optimization are followed to timely resolve coverage issues with maintaining optimization stability. Proposed ACO is verified under both simulations and practical deployment in a pilot cluster for a worldwide mobile carrier. Key Performance Indicators show that proposed ACO method significantly enhances system coverage and performance.Comment: conferenc

A novel way to automatically plan cellular networks supported by linear programming and cloud computing

With the increasing number of mobile subscribers worldwide, there is a need for fast and reliable algorithms for planning/optimization of mobile networks, especially because, in order to maintain a network’s quality of service, an operator might need to deploy more equipment. This paper presents a quick and reliable way to automatically plan a set of frequencies in a cellular network, using both cloud technologies and linear programming. We evaluate our pattern in a realistic scenario of a Global System for Mobile communications protocol (GSM) network and compare the results to another already implemented commercial tool. Results show that even though network quality was similar, our algorithm was twelve times faster and used four times less memory. It was also able to frequency plan seventy cells simultaneously in less than three minutes. This mechanism was successfully integrated in the professional tool Metric, and is currently being used for cellular planning. Its extension for application to 3/4/5G networks is under study.info:eu-repo/semantics/publishedVersio

Cloud-Based Implementation of an Automatic Coverage Estimation Methodology for Self-Organising Network

UIDB/EEA/50008/2020One of the main concerns of telecommunications operators is related to network coverage. A weak coverage can lead to a performance decrease, not only in the user experience, when using the operators' services, such as multimedia streaming, but also in the overall Quality of Service. This paper presents a novel cloud-based framework of a semi-empirical propagation model that estimates the coverage in a precise way. The novelty of this model is that it is automatically calibrated by using drive test measurements, terrain morphology, buildings in the area, configurations of the network itself and key performance indicators, automatically extracted from the operator's network. Requirements and use cases are presented as motivations for this methodology. The results achieve an accuracy of about 5 dB, allowing operators to obtain accurate neighbour lists, optimise network planning and automate certain actions on the network by enabling the Self-Organising Network concept. The cloud implementation enables a fast and easy integration with other network management and monitoring tools, such as the Metric platform, optimising operators' resource usage recurring to elastic resources on-demand when needed. This implementation was integrated into the Metric platform, which is currently available to be used by several operators.publishersversionpublishe