1,589 research outputs found
Traffic Hotspot localization in 3G and 4G wireless networks using OMC metrics
In recent years, there has been an increasing awareness to traffic
localization techniques driven by the emergence of heterogeneous networks
(HetNet) with small cells deployment and the green networks. The localization
of hotspot data traffic with a very high accuracy is indeed of great interest
to know where the small cells should be deployed and how can be managed for
sleep mode concept. In this paper, we propose a new traffic localization
technique based on the combination of different key performance indicators
(KPI) extracted from the operation and maintenance center (OMC). The proposed
localization algorithm is composed with five main steps; each one corresponds
to the determination of traffic weight per area using only one KPI. These KPIs
are Timing Advance (TA), Angle of Arrival (AoA), Neighbor cell level, the load
of each cell and the Harmonic mean throughput (HMT) versus the Arithmetic mean
throughput (AMT). The five KPIs are finally combined by a function taking as
variables the values computed from the five steps. By mixing such KPIs, we show
that it is possible to lessen significantly the errors of localization in a
high precision attaining small cell dimensions.Comment: 7 pages, 7 figures, published in Proc. IEEE International Symposium
on Personal, Indoor and Mobile Radio Communications 2014 (PIMRC); IEEE
International Symposium on Personal, Indoor and Mobile Radio Communications
2014 (PIMRC
Self organising cloud cells: a resource efficient network densification strategy
Network densification is envisioned as the key enabler for 2020 vision that requires cellular systems to grow in capacity by hundreds of times to cope with unprecedented traffic growth trends being witnessed since advent of broadband on the move. However, increased energy consumption and complex mobility management associated with network densifications remain as the two main challenges to be addressed before further network densification can be exploited on a wide scale. In the wake of these challenges, this paper proposes and evaluates a novel dense network deployment strategy for increasing the capacity of future cellular systems without sacrificing energy efficiency and compromising mobility performance. Our deployment architecture consists of smart small cells, called cloud nodes, which provide data coverage to individual users on a demand bases while taking into account the spatial and temporal dynamics of user mobility and traffic. The decision to activate the cloud nodes, such that certain performance objectives at system level are targeted, is carried out by the overlaying macrocell based on a fuzzy-logic framework. We also compare the proposed architecture with conventional macrocell only deployment and pure microcell-based dense deployment in terms of blocking probability, handover probability and energy efficiency and discuss and quantify the trade-offs therein
Smarter grid through collective intelligence: user awareness for enhanced performance
This paper examines the scenario of a university campus, and the impact on energy consumption of the awareness of building managers and users (lecturers, students and administrative staff).Peer ReviewedPostprint (published version
A machine learning management model for QoE enhancement in next-generation wireless ecosystems
Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring a good quality of service (QoS) will be one of the major challenges of next-generation wireless systems on account of a variety of factors that are beyond the control of network and service providers. In this context, ITU-T is working on updating the various Recommendations related to QoS and users\u27 quality of experience (QoE). Considering the ITU-T QoS framework, we propose a methodology to develop a global QoS management model for next-generation wireless ecosystems taking advantage of big data and machine learning. The results from a case study conducted to validate the model in real-world Wi-Fi deployment scenarios are also presented
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