Hadi, MS orcid.org/0000-0003-1422-5254, Lawey, AQ orcid.org/0000-0003-3571-4110, El-Gorashi, TEH et al. (1 more author) (2019) Patient-Centric Cellular Networks Optimization using Big Data Analytics. IEEE Access, 7. pp. 49279-49296. ISSN 2169-3536
Abstract
Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see https://creativecommons.org/licenses/by/3.0/ |
Keywords: | Big Data , Medical services , Cellular networks , Optimization , Interference , Signal to noise ratio , Wireless communication; LTE network optimization , big data analytics , cellular network design , patient-centric network optimization , MILP , naïve Bayesian classifier , resource allocation , OFDMA uplink optimization , resource management |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/H040536/1 EPSRC EP/K016873/1 EPSRC EP/S016570/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Aug 2019 11:07 |
Last Modified: | 25 Jun 2023 21:39 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ACCESS.2019.2910224 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140364 |