| Title | Combined Compressive Sampling Techniques and Features Detection using Kullback Leibler Distance to Manage Handovers |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | HANJRI, Adnane EL, HAYAR, Aawatif, Haqiq, Abdelkrim |
| Conference Name | 2019 IEEE International Smart Cities Conference (ISC2) |
| Keywords | Akaike information criterion, Akaike Weight, Akaike weights, Complexity theory, composability, compressed sensing, compressive sampling, Compressive Sampling algorithm, Compressive Sampling Techniques, Computational modeling, Cyber-physical systems, Detectors, Distribution Analysis Detector, feature extraction, features detection, Handover, Handover technique, Handovers, Kullback Leibler Distance, manage handovers, mobility management (mobile radio), primary signals sparsity, privacy, probability, Probability density function, pubcrawl, received signal probability density function, Resiliency, signal detection, signal sampling, Small Cells |
| Abstract | In this paper, we present a new Handover technique which combines Distribution Analysis Detector and Compressive Sampling Techniques. The proposed approach consists of analysing Received Signal probability density function instead of demodulating and analysing Received Signal itself as in classical handover. In this method we will exploit some mathematical tools like Kullback Leibler Distance, Akaike Information Criterion (AIC) and Akaike weights, in order to decide blindly the best handover and the best Base Station (BS) for each user. The Compressive Sampling algorithm is designed to take advantage from the primary signals sparsity and to keep the linearity and properties of the original signal in order to be able to apply Distribution Analysis Detector on the compressed measurements. |
| DOI | 10.1109/ISC246665.2019.9071754 |
| Citation Key | hanjri_combined_2019 |
Combined Compressive Sampling Techniques and Features Detection using Kullback Leibler Distance to Manage Handovers