User Association in Coexisting RF and TeraHertz Networks in 6G

• Published in: Conference proceedings - Canadian Conference on Electrical and Computer Engineering pp. 1 - 5
• Main Authors: Hassan, Noha, Hossan, Md Tanvir, Tabassum, Hina
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30.08.2020
• Subjects: 6G mobile communication; 6G Networks; Clustering algorithms; Interference; Load management; Radio frequency; Signal to noise ratio; Tera Hertz Band; Traffic Load Balancing; Unsupervised Machine Learning; User Association
• ISSN: 2576-7046
• DOI: 10.1109/CCECE47787.2020.9255737

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.