Abstract
Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naive Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error.
Highlights
Brain strokes are one of the rising health issues and though they might cause significant disabilities to the patient, immediate treatment can effectively increase recovery chances [1]
A cloud-based arrangement is assumed where each OP has their personal dataset constructed from their medical history and daily observations over the course of 200 days, with the requirement to periodically extend the dataset by appending recent observations
The proposed approach assumes a system that is in operation and the outpatient is being assessed by the voting system where multiple classifiers reside
Summary
Brain strokes are one of the rising health issues and though they might cause significant disabilities to the patient, immediate treatment can effectively increase recovery chances [1]. According to statistics from England, Wales and Northern Ireland for 2016-2017, one-third of stroke patients arrived at the hospital unaware of the date and time their symptoms began. Topics that discusses patient monitoring, radio resource allocation, prioritization, fairness, and ensemble-aided disease risk prediction are popular in the literature across several disciplines. Proposing a HetNet optimization framework that incorporates all the above is, to the extent of our knowledge, unique The objective of both approaches presented in this work is to maximize the system’s overall SINR, both of which are governed by a number of power and physical resource block (PRB) assignment constraints. Reporting the cross-validation test scores for all datasets; (iii) extending the work in [7] to study the effects of inter-cell interference in HetNets, where we added a reliability-aware aspect to the PF approach; (iv) testing the fairness among users, and conducting the required sensitivity analysis over 300 instances.
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