Federated learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it well-suited for privacy-sensitive Internet of Things (IoT) environments. However, under heterogeneous [nonindependent and identically distributed (IID)] data distributions, classical FL often suffers...
Anh-Kiet Vo, Thien Huynh-The, Taehong Kim· IEEE Internet of Things Jour...· 0 citations
The paradigm shift toward sustainable spectrum utilization in future wireless architectures requires spectrum sensing methods that balance fine-grained characterization of complex spectral scenes with minimal computational overhead. To this end, we propose OFA-UNet, a cost-efficient model for segmenting various signals...
Thien Huynh-The, Luong Cong Nguyen, Tae-Jun Kim et al.· IEEE Wireless Communications...· 0 citations
Healthcare systems are increasingly data-rich but remain fragmented, reactive, and difficult to coordinate across institutions, devices, and clinical workflows. Healthcare 5.0 addresses these limitations through human-centricity, resilience, and symbiotic human-artificial intelligence (AI) collaboration. This survey po...
T. Gadekallu, Thien Huynh-The, A. Chehri et al.· IEEE Internet of Things Jour...· 0 citations
Simulations show that the effective SINR of OTFS changes 37.8% less per frame than that of orthogonal frequency division multiplexing (OFDM) at 70 m/s under the same numerology, and that the waveform contribution to the usefulness of stale SINR feedback increases with mobility.
T. Van Le, Luong Cong Nguyen, Trong-Dai Hoang et al.· 0 citations
In multiple-input multiple-output systems, the acquisition of high-fidelity channel state information is hindered by the complex interaction between time-frequency fading and limited pilot signal resources. Current deep learning methods often lack physical consistency and require large computational costs. This letter...
Ngoc-Ha Truong, Thanh-Dat Tran, S. N. Truong et al.· IEEE Wireless Communications...· 0 citations
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