论文标题
以机器学习和6G网络的大数据分析为动力的患者以患者为中心的HETNET
Patient-centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks
论文作者
论文摘要
具有一个认知和自我优化的网络，不仅可以主动适应渠道条件，而且根据其用户需求，可以是未来6G 6G异质网络（HETNETS）的最高优先级之一。在本文中，我们介绍了一种跨学科方法，该方法将电子保健，优先级，大数据分析（BDA）和无线电资源优化的概念链接在多层5G网络中。 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.我们将中风的可能性转换为一种风险因素，该风险因素在混合整数线性编程（MILP）优化模型中的优先级。因此，任务是通过根据其医疗状态的严重程度授予OPS优先级的OPS来最佳分配物理资源块（PRB），同时对OPS进行优先排序。因此，授权OPS以最小化的延迟将其关键数据发送给其医疗保健提供者。为此，提出了两种优化方法，即加权总和最大化（WSRMAX）方法和比例公平（PF）方法。提出的方法分别将OPS的平均信号提高到干扰加噪声（SINR），分别增加了57％和95％。 WSRMAX方法将系统的总SINR提高到高于PF方法的水平，但是，PF方法对OPS产生了更高的SINR，更好的公平性和较低的误差范围。
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 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.
