Exploiting Radio Measurements in Wireless Mobile Networks with Advanced Signal Processing

Alaya-Feki, Afef Ben Hadj; Moulines, Éric; Villebrun, E.

doi:10.1109/icwmc.2007.41

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Choi et al [3] provided a cross-layer handover scheme based on a simple linear regression model to predict future signal level which reduces the total handover latency. Alaya-Feki et al [4] used a statistical analysis based on regression method allowing the amelioration of Radio Resources Management and they also propose amelioration in the handover procedure. To improve their networks utilization, operators use often statistics methodology to predict the handover.…”

Section: Related Workmentioning

confidence: 99%

Statistical learning and multiple linear regression model for network selection using MIH

Rahil

Mbarek

Atieh

et al. 2014

The Third International Conference on E-Technologies and Networks for Development (ICeND2014)

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A key requirement to provide seamless mobility and guaranteeing Quality of Service in heterogeneous environment is to select the best destination network during handover. In this paper, we propose a new schema for network selection based on Multiple Linear Regression Model (MLRM). A thorough investigation, on a huge live data collected from GPRS/UMTS networks led to identify the Key Performance Indicators (KPIs) that play the most important role in the handover process. These KPIs are: Received Signal Code Power (RSCP), received energy per chip (Ec/No) and Available Bandwidth (ABW) of the destination network. To extract a handover model from collected data, we study the correlation among values of identified KPIs parameters, before, during and after handover, thanks to a statistical learning approach, using the predictive analytics software SPSS. For model assessment, Pearson Correlation Coefficient and determination coefficient Rsquared (R 2 ) are used. Media Independent Handover (MIH) IEEE 802.21 standard is used in this work to retrieve the lower layer information of available networks and announce the handover needs (handover initiation). The proposed model will help to select the most appropriate network between many existing ones in the vicinity of the mobile node.

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Section: Related Workmentioning

confidence: 99%

Statistical learning and multiple linear regression model for network selection using MIH

Rahil

Mbarek

Atieh

et al. 2014

The Third International Conference on E-Technologies and Networks for Development (ICeND2014)

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“…Spectrum occupancy measurements (radio measurements) have been performed over the years to determine the level of spectrum utilization and in extension utilize the data to develop models for spectrum hole prediction [4][5][6]. Radio measurements have become more efficient due to improvements measurement technologies coupled with recent innovations in data mining, efficient exploitation methods have been developed that make it possible to extract vital information to enhance the viability of new concepts such as Cognitive Radio (CR) [7].…”

Section: Introductionmentioning

confidence: 99%

On the Construction of Radio Environment Map for Cognitive Radio Applications: An Overview

Najashi¹

2019

International Journal of Advanced Research in Computer and Comm

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Demand for data services from a few Kilobytes to several Gigabytes over the last 25 years has led to the development of several mobile and wireless standards. Unfortunately, the spectrum required for these services to operate is becoming scarce not because of shortage but because of under-utilization as several spectrum occupancy measurements have shown over the years. The solution then has to be a dynamic spectrum access approach where licensed and unlicensed users could share the spectrum without causing interference to the licensed user. This concept known as Cognitive Radio (CR) was envisioned by Mitola promises to solve this problem. Radio Environment Map (REM) is one of the central tools at the heart of CR as it constructs a comprehensive map of the CR network storing information on physical network, policies, regulation, licensed user profile and activity. In this research, a general overview on REM construction is presented. From the architecture to the techniques applied in constructing REMs. The performance of propagation models employed in this process is also presented. Finally the quality metrics used to determine the accuracy of the constructed REMs is given

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Spatial and spectral radio resource enhancement for urban spectrum reuse

Kamakaris

Kivanc

Tureli

2008

2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications

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Exploiting Radio Measurements in Wireless Mobile Networks with Advanced Signal Processing

Cited by 4 publications

References 5 publications

Statistical learning and multiple linear regression model for network selection using MIH

Statistical learning and multiple linear regression model for network selection using MIH

On the Construction of Radio Environment Map for Cognitive Radio Applications: An Overview

Spatial and spectral radio resource enhancement for urban spectrum reuse

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