Patient-Centric Cellular Networks Optimization Using Big Data Analytics
Patient-Centric Cellular Networks Optimization Using Big Data Analytics
复制标题
使用大数据分析以患者为中心的蜂窝网络优化
作者：
Mohammed S. Hadi;A. Lawey;T. El;J. Elmirghani
大数据分析是优化网络并将其从仅仅是传输数据的盲管转变为能够根据用户需求进行智能调整的认知、自觉和自我优化实体的最先进工具之一。事实上，这可以被视为未来网络的最高优先事项之一。提出了一种以门诊(OP)为中心的长期演进-高级(LTE-A)网络优化系统。从OPS的医疗记录中获得的大数据，以及他们与身体相连的医用物联网传感器的当前读数都被处理和分析，以预测威胁生命的医疗状况的可能性，例如即将发生的中风。此预测用于确保为OP分配最佳LTE-A物理资源块(PRB)，以便以最小的延迟将其关键数据传输到其医疗保健提供商。据我们所知，这是第一次利用大数据分析以有运营意识的方式优化蜂窝网络。使用混合整数线性规划(MILP)和实时启发式算法对PRBS分配进行优化。提出了两种方法：加权和速率最大化(WSRMax)方法和比例公平(PF)方法。这两种方法的平均信噪比分别提高了26.6%和40.5%。WSRMax方法将系统的总SINR提高到比PF方法更高的水平，然而，PF方法报告了更高的OPS SINR，更好的公平性和更低的误差率。
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-connected medical IoT 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.