论文标题
在6G中共存的RF和Terahertz网络中的用户协会
User Association in Coexisting RF and TeraHertz Networks in 6G
论文作者
论文摘要
虽然第五代（5G）网络已准备好进行部署，但第六代（6G）网络的讨论仍在下道。由于Terahertz（THZ）之类的高频将是6G的中心，因此在本文中，我们提出了两种用户关联（UE）算法，考虑到共存的RF和THZ网络，该算法通过最小化网络流量负载的标准偏差来平衡整个网络的流量负载。我们的算法捕获在RF和THZ频率（例如传输带宽，分子吸收，发射功率等）上观察到的异质性。与典型的无监督聚类算法不同，不同的是，K-Means，K-Medoid等）（例如K-Means，k-Medoid等）搜索了适当的网络，以确定适当的群体，以确定一个标准的算法，以使A型群体相关联，以确定A e Algors，以使AL GRESS与A型相关联，以确定ALGERS，以确定ALGERS的标准，以使ALGERS与A grands相关联。 （STD）可以根据用户的费率约束来最小化。特别是，我们的算法群集U到每个基站（BS），从而可以平衡整个网络的流量负载，即通过最大程度地减少网络流量负载的性病。数值结果表明，所提出的算法在数据速率，流量负载平衡和用户的公平性方面优于经典用户关联算法。
While fifth generation (5G) networks are ready for deployment, discussions over sixth generation (6G) networks are down the road. Since high frequencies like terahertz (THz) will be central to 6G, in this paper, we propose two user association (UE) algorithms considering a coexisting RF and THz network that balances the traffic load across the network by minimizing the standard deviation of the network traffic load. Our algorithms capture the heterogeneity observed at RF and THz frequencies such as transmission bandwidth, molecular absorption, transmit powers, etc. Unlike typical unsupervised clustering algorithms (e.g. k-means, k-medoid, etc.) that search for appropriate cluster centers' locations, our algorithms identify the appropriate UEs to be associated to a certain BS such that the overall network load standard deviation (STD) can be minimized subject to users' rate constraints. In particular, our algorithms cluster UEs to every base station (BS) such that the traffic load across the network can be balanced, i.e., by minimizing the STD of network traffic load. Numerical results show that the proposed algorithms outperform the classical user association algorithms in terms of data rate, traffic load balancing, and user's fairness.
