{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:06:27Z","timestamp":1755839187425,"version":"3.40.5"},"reference-count":71,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T00:00:00Z","timestamp":1629849600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,8,25]]},"abstract":"<jats:p>The future high-speed cellular networks require efficient and high-speed handover mechanisms. However, the traditional cellular handovers are based upon measurements of target cell radio strength which requires frequent measurement gaps. During these measurement windows, data transmission ceases each time, while target bearings are measured causing serious performance degradation. Therefore, prediction-based handover techniques are preferred in order to eliminate frequent measurement windows. Thus, this work proposes an ultrafast and efficient XGBoost-based predictive handover technique for next generation mobile communications. The ML algorithm in general prefers 70\u201330% of training and test data, respectively. However, always obtaining 70% of training samples in mobile communications is challenging because the channel remains correlated within coherence time only. Therefore, collecting training samples beyond coherence time limits performance and adds delay; thus, the proposed work trains the model within coherence time where this fixed data split of 70\u201330% makes the model exceed coherence time. Despite the fact that the proposed model gets starved of required training samples, still there is no loss in predication accuracy. The test results show a maximum delay improvement of up to 596\u2009ms with enhanced performance efficiency of 68.70% depending upon the scenario. The proposed model reduces delay and improves efficiency by having an appropriate training period; thus, the intelligent technique activates faster with improved accuracy and eliminates delay in the algorithm to predict mmWaves\u2019 signal strength in contrast to actually measuring them.<\/jats:p>","DOI":"10.1155\/2021\/2775278","type":"journal-article","created":{"date-parts":[[2021,8,26]],"date-time":"2021-08-26T00:20:07Z","timestamp":1629937207000},"page":"1-19","source":"Crossref","is-referenced-by-count":3,"title":["Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8659-7818","authenticated-orcid":true,"given":"Saad Ijaz","family":"Majid","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan"},{"name":"Department of Electrical Engineering Technology, University of Technology, Nowshera 24100, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-5253","authenticated-orcid":true,"given":"Syed Waqar","family":"Shah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1058-8920","authenticated-orcid":true,"given":"Safdar Nawaz Khan","family":"Marwat","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4458-8606","authenticated-orcid":true,"given":"Abdul","family":"Hafeez","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Jalozai Campus, University of Engineering and Technology Peshawar, Nowshera 24240, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2261-4105","authenticated-orcid":true,"given":"Haider","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Technology, University of Technology, Nowshera 24100, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5887-0920","authenticated-orcid":true,"given":"Naveed","family":"Jan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering Technology, University of Technology, Nowshera 24100, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.1109\/iccw.2018.8403587"},{"doi-asserted-by":"publisher","key":"2","DOI":"10.1007\/s10579-013-9221-8"},{"doi-asserted-by":"publisher","key":"3","DOI":"10.1155\/2021\/4832864"},{"doi-asserted-by":"publisher","key":"4","DOI":"10.1016\/j.asoc.2016.08.001"},{"doi-asserted-by":"publisher","key":"5","DOI":"10.3390\/su12030830"},{"author":"T. 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