7 papers
From Simulation to Reality: Practical Deep Reinforcement Learning-based Link Adaptation for Cellular Networks
Lizhao You, Nanqing Zhou, Guanglong Pang +3
Link Adaptation (LA) that dynamically adjusts the Modulation and Coding Schemes (MCS) to accommodate time-varying channels is crucial and challenging in cellular networks. Deep rei…
Rich-ARQ: From 1-bit Acknowledgment to Rich Neural Coded Feedback
Enhao Chen, Yulin Shao
This paper reimagines the foundational feedback mechanism in wireless communication, transforming the prevailing 1-bit binary ACK/NACK with a high-dimensional, information-rich vec…
Deep Variable-Length Feedback Codes
Yu Ding, Yulin Shao
Deep learning has enabled significant advances in feedback-based channel coding, yet existing learned schemes remain fundamentally limited: they employ fixed block lengths, suffer…
Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission
Zengzipeng Tang, Yuxuan Sun, Wei Chen +3
Device-edge collaborative inference with Deep Neural Networks (DNNs) faces fundamental trade-offs among accuracy, latency and energy consumption. Current scheduling exhibits two dr…
When Feedback Empowers the Uplink: Integrating Adaptive Coding with Wireless Power Transfer
Zijian Yang, Yulin Shao, Shaodan Ma
Energy consumption and device lifetime are critical concerns for battery-constrained IoT devices. This paper introduces the Feedback-Aided Coding and Energy Transfer (FACET) framew…
Connecting the Unconnectable through Feedback
Yimeng Li, Yulin Shao
Reliable uplink connectivity remains a persistent challenge for IoT devices, particularly those at the cell edge, due to their limited transmit power and single-antenna configurati…