@article{MDEEF741D,
title = "Unlicensed Band Traffic and Fairness Maximization Approach
Based on Rate-Splitting Multiple Access",
journal = "KIPS Transactions on Computer and Communication Systems",
year = "2023",
issn = "2287-5891",
doi = "https://doi.org/10.3745/KTCCS.2023.12.10.299",
author = "Jeon Zang Woo/Kim Sung Wook",
keywords = "5G Network, Rate Splitting Multiple Access, Unlicensed Band, Game Theory, Bargaining Solution, Reinforcement Learning",
abstract = "As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared,
allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the
performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize
the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose
an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of
NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm
based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the
coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows
better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged
under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network
users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution
of this paper satisfies throughput and fairness at the same time than other algorithms."
}