Patient-centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

03/18/2020
by   Mohammed S. Hadi, et al.
0

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 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.

READ FULL TEXT

page 1

page 3

research
11/09/2018

Patient-Centric Cellular Networks Optimization using Big Data Analytics

Big data analytics is one of the state-of-the-art tools to optimize netw...
research
03/14/2019

Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets

In this paper, we introduce machine learning approaches that are used to...
research
04/06/2019

A Novel Big Data Analytics Framework to Predict the Risk of Opioid Use Disorder

Addiction and overdose related to prescription opioids have reached an e...
research
04/16/2019

The Rise of Internet of Things (IoT) in Big Healthcare Data: Review and Open research Issues

Health is one of the sustainable development areas in all of the countri...
research
09/29/2022

IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective

With the wide spread of sensors and smart devices in recent years, the d...
research
01/28/2020

LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations

Exploiting big data knowledge on small devices will pave the way for bui...

Please sign up or login with your details

Forgot password? Click here to reset