no code implementations • 25 Apr 2023 • Po-chun Hsu, Li-Hsiang Shen, Chun-Hung Liu, Kai-Ten Feng
Terahertz (THz) communication with ultra-wide available spectrum is a promising technique that can achieve the stringent requirement of high data rate in the next-generation wireless networks, yet its severe propagation attenuation significantly hinders its implementation in practice.
no code implementations • 6 Apr 2022 • Jwo-Yuh Wu, Liang-Chi Huang, Wen-Hsuan Li, Chun-Hung Liu, Rung-Hung Gau
Sparse subspace clustering (SSC) using greedy-based neighbor selection, such as orthogonal matching pursuit (OMP), has been known as a popular computationally-efficient alternative to the popular L1-minimization based methods.
no code implementations • 27 Oct 2021 • Chun-Hung Liu, Kai-Ten Feng, Lu Wei, Yu Luo
The STFL model not only represents the realistic intermittent learning behavior from the edge server to the mobile devices due to data delivery outage, but also features a mechanism of compensating loss learning updates in order to mitigate the impacts of intermittent learning.
no code implementations • 13 Oct 2021 • Chun-Hung Liu, Di-Chun Liang, Rung-Hung Gau, Lu Wei
Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption.
no code implementations • 14 Aug 2020 • Chun-Hung Liu, Di-Chun Liang, Po-Chia Chen, Jie-Ru Yang
In a heterogeneous cellular network (HetNet) consisting of $M$ tiers of densely-deployed base stations (BSs), consider that each of the BSs in the HetNet that are associated with multiple users is able to simultaneously schedule and serve two users in a downlink time slot by performing the (power-domain) non-orthogonal multiple access (NOMA) scheme.
no code implementations • 2 Feb 2020 • Jwo-Yuh Wu, Wen-Hsuan Li, Liang-Chi Huang, Yen-Ping Lin, Chun-Hung Liu, Rung-Hung Gau
Sparse subspace clustering (SSC) using greedy-based neighbor selection, such as matching pursuit (MP) and orthogonal matching pursuit (OMP), has been known as a popular computationally-efficient alternative to the conventional L1-minimization based methods.