An Improved Modelling of User Clustering For Small Cell Deployment In Heterogeneous Cellular Network.

An Improved Modelling of User Clustering For Small Cell Deployment In Heterogeneous Cellular Network. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Improved Modelling of User Clustering For Small Cell Deployment In Heterogeneous Cellular Network. Joyatri Bora, Anwar Hussain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-476123/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Users in practical cellular geographical areas are found to be non-uniformly distributed. Small cell ( SC ) deployments in heterogeneous user distribution in a cellular geographical area help to meet high data rate user demands for multimedia data communications in hot spots. SC s help to offload traffic burden from the macro cell ( MC ) base station, and also cater the data traffic need for the edge users where signal strength from the MC base station ( BS ) is very weak. For deployments of SC s along with the central MC BS (hence called HetNet ) in such spatial heterogeneous user distribution, effective user grouping or clustering algorithm is required for appropriate and satisfactory service coverage. We call it service grouping or clustering of users to be put under a SC for data transmission and reception. It does not disturb the spatial positions of users in clustered non-uniform distribution. Efficient grouping or clustering of users and then deploying a SC at optimal location enhances the performance of the HetNet . It is found that the K-means algorithm used for such grouping of users to position SC s is not efficient. A novel and improved user grouping algorithm is proposed in this paper which performs much better compared to the k-means algorithm. The proposed algorithm of modelling of user clustering results in increase in the number of users under SC s , increase in more offloading of data traffic from MC BS thereby increasing data throughput of MC users. The algorithm also increases in the energy efficiencies of the SC s which is considered as one important performance metric. A doubly stochastic poison process ( DSPP ) also called Cox process is assumed here for simulation of non-uniform user distributions. We consider Rayleigh distributed small scale fading model, large scale fading factor representing shadow fading, and users’ geographical distances from BS s to evaluate users’ data rates. Technical Communication Heterogeneous Clustering K-means Algorithm Doubly Stochastic Poison Process Shadow Fading Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 Jun, 2021 Reviewers invited by journal 30 May, 2021 Editor assigned by journal 29 Apr, 2021 First submitted to journal 28 Apr, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-476123","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":30261789,"identity":"ff33b866-1ed5-4b62-ab5e-a83cc4d94ae9","order_by":0,"name":"Joyatri 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Small cell ( \u003cem\u003eSC \u003c/em\u003e) deployments in heterogeneous user distribution in a cellular geographical area help to meet high data rate user demands for multimedia data communications in hot spots. \u003cem\u003eSC\u003c/em\u003es help to offload traffic burden from the macro cell ( \u003cem\u003eMC \u003c/em\u003e) base station, and also cater the data traffic need for the edge users where signal strength from the MC base station ( \u003cem\u003eBS \u003c/em\u003e) is very weak. For deployments of \u003cem\u003eSC\u003c/em\u003es along with the central \u003cem\u003eMC BS\u003c/em\u003e (hence called HetNet ) in such spatial heterogeneous user distribution, effective user grouping or clustering algorithm is required for appropriate and satisfactory service coverage. We call it service grouping or clustering of users to be put under a \u003cem\u003eSC \u003c/em\u003efor data transmission and reception. It does not disturb the spatial positions of users in clustered non-uniform distribution. Efficient grouping or clustering of users and then deploying a \u003cem\u003eSC \u003c/em\u003eat optimal location enhances the performance of the \u003cem\u003eHetNet \u003c/em\u003e. It is found that the K-means algorithm used for such grouping of users to position \u003cem\u003eSC\u003c/em\u003es is not efficient. A novel and improved user grouping algorithm is proposed in this paper which performs much better compared to the k-means algorithm. The proposed algorithm of modelling of user clustering results in increase in the number of users under \u003cem\u003eSC\u003c/em\u003es , increase in more offloading of data traffic from \u003cem\u003eMC BS\u003c/em\u003e thereby increasing data throughput of \u003cem\u003eMC \u003c/em\u003eusers. The algorithm also increases in the energy efficiencies of the \u003cem\u003eSC\u003c/em\u003es which is considered as one important performance metric. A doubly stochastic poison process ( \u003cem\u003eDSPP \u003c/em\u003e) also called Cox process is assumed here for simulation of non-uniform user distributions. 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