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20212026
most citedBattery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks

126 citations · 147 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Generative Actor-Critic with Soft Bridge Policies

Ke He, Le He, Shunpu Tang +2

Expressive generative policies such as diffusion and flow models are appealing for maximum-entropy (MaxEnt) online reinforcement learning because of their ability to model multimod…

cs.MA2026

AgenticPrecoding: LLM-Empowered Multi-Agent System for Precoding Optimization

Zijiu Yang, Zixiang Zhang, Shunpu Tang +2

Precoding is a key technique for interference management and performance improvement in multi-antenna wireless systems. However, existing precoding methods are typically developed…

cs.IT2023★ 3 cited

Contrastive Learning based Semantic Communication for Wireless Image Transmission

Shunpu Tang, Qianqian Yang, Lisheng Fan +3

Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images o…

cs.LG2021★ 11 cited

Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT

Shunpu Tang, Lunyuan Chen, Ke HeJunjuan Xia +2

In this paper, we investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can collabora…

cs.IT2021★ 7 cited

Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems

Shunpu Tang, Junjuan Xia, Lisheng Fan +3

Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlin…

cs.NI2021★ 126 cited

Battery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks

Shunpu Tang, Wenqi Zhou, Lunyuan Chen +3

In this paper, we study how to optimize the federated edge learning (FEEL) in UAV-enabled Internet of things (IoT) for B5G/6G networks, from a deep reinforcement learning (DRL) app…