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20212024
most citedDeep-Unfolding for Next-Generation Transceivers

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SP2024

Feature Allocation for Semantic Communication with Space-Time Importance Awareness

Kequan Zhou, Guangyi Zhang, Yunlong Cai +3

In the realm of semantic communication, the significance of encoded features can vary, while wireless channels are known to exhibit fluctuations across multiple subchannels in diff…

eess.SP20231 cited

Alleviating Distortion Accumulation in Multi-Hop Semantic Communication

Guangyi Zhang, Qiyu Hu, Yunlong Cai +1

Recently, semantic communication has been investigated to boost the performance of end-to-end image transmission systems. However, existing semantic approaches are generally based…

eess.SP2023

One-shot Learning for Channel Estimation in Massive MIMO Systems

Kai Kang, Qiyu Hu, Yunlong Cai +1

In conventional supervised deep learning based channel estimation algorithms, a large number of training samples are required for offline training. However, in practical communicat…

eess.SP20232 cited

Deep-Unfolding for Next-Generation Transceivers

Qiyu Hu, Yunlong Cai, Guangyi Zhang +2

The stringent performance requirements of future wireless networks, such as ultra-high data rates, extremely high reliability and low latency, are spurring worldwide studies on def…

eess.SP2023

FAST: Feature Arrangement for Semantic Transmission

Kequan Zhou, Guangyi Zhang, Yunlong Cai +2

Although existing semantic communication systems have achieved great success, they have not considered that the channel is time-varying wherein deep fading occurs occasionally. Mor…

eess.SP20231 cited

Adaptive CSI Feedback for Deep Learning-Enabled Image Transmission

Guangyi Zhang, Qiyu Hu, Yunlong Cai +1

Recently, deep learning-enabled joint-source channel coding (JSCC) has received increasing attention due to its great success in image transmission. However, most existing JSCC stu…