异构网络下行多点协作中的机器学习
Machine Learning in Downlink Coordinated Multipoint in Heterogeneous Networks
摘要：
我们提出了一种用于异构第五代新无线电（NR）网络中的下行链路协作多点（DL CoMP）的方法。我们的论文的主要贡献是一个算法，以提高触发DL CoMP使用在线机器学习。我们使用支持向量机（SVM）分类器，以提高用户的下行链路吞吐量在一个现实的频分双工网络环境。我们的仿真结果表明，由于SVM分类器学习到的多个无线电流的通知触发，宏基站和微微基站下行链路吞吐量都有所改善。
英文摘要：
We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The primary contribution of our paper is an algorithm to enhance the trigger of DL CoMP using online machine learning. We use support vector machine (SVM) classifiers to enhance the user downlink throughput in a realistic frequency division duplex network environment. Our simulation results show improvement in both the macro and pico base station downlink throughputs due to the informed triggering of the multiple radio streams as learned by the SVM classifier.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
