Statistical Learning for Automated RRM: Application to eUTRAN Mobility

Tiwana, Moazzam Islam; Sayraç, Berna; Altman, Zwi

doi:10.1109/icc.2009.5199489

Cited by 21 publications

(6 citation statements)

References 4 publications

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“…From the evaluation of the results for the SLB algorithm with the reference case, in which all spectrum are allocated, a development for the outage throughput is obtained. From the comparisons with the other algorithms based on SINR, it is seen that the beginning of a latest threshold the SLB algorithm based on SINR and interference allow assigning more spectrum with respect to the SLB algorithm based on SINR, but it leads to a fair allocation [6]. In this paper for the SLB algorithm based on SINR and interference between the two operators are judged to the equal, indeed the similar threshold are measured, but as an growth it would be striking to study what occurs by establishing different thresholds for the dissimilar operators, thus moving from an horizontal to a vertical spectrum sharing paradigm.…”

Section: Discussionmentioning

confidence: 99%

A novel idea of spectrum management (SM) using spectrum load balancing (SLB) algorithm

Islam

Mollah

2014

2014 International Conference on Electrical Engineering and Information &Amp; Communication Technology

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Next Generation Network (NGN) is troubled by the difficulty of growing demand for the spectrum, to resolve the problem, it is possible to find a more efficient way in using the existing spectrum bands and technologies. The radio spectrum can be expressed in three dimensions: frequency, space and time. If any one of these differs between transmitters, interference should not occur. At present, the spectrum sharing (SS) technology is gaining concentration and dissimilar strategies are being studied to allow different operators to share the spectrum in opportunistic way and at the same time gain higher spectrum efficiency. The intention of this paper is to equalize the total distribution of the allocated radio spectrum resources among more interfering devices that work in the same space. Mitigation of the destructive, mutual interference in the wireless network can be achieved with the application of the spectrum load balancing (SLB) algorithm. This enables an equally-smoothed allocation of the spectrum by redistributing, with respect to their individual QoS requisites, and optimized use of the obtainable spectrum. In fact, it permits the detection of the unused spectrum, which was initially licensed, and to release it if it is needed again.

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Section: Discussionmentioning

confidence: 99%

A novel idea of spectrum management (SM) using spectrum load balancing (SLB) algorithm

Islam

Mollah

2014

2014 International Conference on Electrical Engineering and Information &Amp; Communication Technology

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“…From the evaluation of the results for the EAS paradigm with the reference case, in which all spectrums are allocated, a development for the outage throughput is obtained. From the EAS paradigm based on SINR that the beginning of a latest threshold and interference allow assigning more spectrum respect to the EAS paradigm, but it leads to a fair allocation (Tiwana et al, 2009). In this paper for the EAS paradigm between the two operators are judged to the equal, indeed the similar threshold are measured, but as an growth it would be striking to study what occurs by establishing different thresholds for the dissimilar operators, thus moving from an horizontal to a vertical spectrum sharing paradigm.…”

Section: Resultsmentioning

confidence: 99%

A concept of potential future-gen radio spectrum administration seeking easy access spectrum paradigm figured on signal to interference noise ratio and interference thresholds

Kabir

Sobhan

Khan³

et al. 2015

Bangladesh J. Sci. Ind. Res.

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The obscurity of growing demand for the future generation (Future-Gen) spectrum is a concerned issue to resolve the perplexities and to seek for a more proficient manner in accessing the on hand radio spectrum bands and technologies. Frequency, space and time are the three dimensions of the radio spectrum where interference should not be happened if any one of these diverges between transmitters. Nowadays developing attention of the spectrum sharing technology and different strategies are being cultivated to permit more operators to exchange the spectrum in an opportunistic approach and simultaneously grow elevated to proficiency. The authors intentions aiming at this paper the entirely dispensation of the estimated radio spectrum resources among more interfering apparatuses that function in the similar space area are to make equal with the proposed paradigm from the idea of water filling. To alleviate the troublesome, using the application of the easy access spectrum (EAS) algorithm can easily be accomplished with the reciprocal intervention. Efficient use of the achieved spectrum and equal-smoothed allocation by redispensation in view of their particular QoS requisites are agile by this EAS paradigm. It is really allowed to identify the unused spectrum, which was primarily licensed, and to release it if is needed again.

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“…Toril et al [5] supplied a novel multiple regression model for GSM networks to estimate the number of location updates by idle users. Tiwana et al [6] used a regression analysis as statistical learning approach to propose a new model for handover. J. T. Chien [7] proposed a novel Linear Regression based on Bayesian Predictive Classification (LRBPC) for robust speech recognition.…”

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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Statistical Learning for Automated RRM: Application to eUTRAN Mobility

Cited by 21 publications

References 4 publications

A novel idea of spectrum management (SM) using spectrum load balancing (SLB) algorithm

A novel idea of spectrum management (SM) using spectrum load balancing (SLB) algorithm

A concept of potential future-gen radio spectrum administration seeking easy access spectrum paradigm figured on signal to interference noise ratio and interference thresholds

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

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