  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.
