Uplink Control
Uplink control in wireless networks focuses on efficiently managing the transmission of data from user devices to a central server, a critical aspect of many applications like federated learning and the Internet of Things. Current research emphasizes optimizing energy efficiency and latency through techniques such as adaptive quantization, power allocation schemes tailored to specific network architectures (e.g., cell-free massive MIMO), and the application of machine learning (e.g., deep reinforcement learning, neural networks) for tasks like scheduling, channel decoding, and interference management. These advancements are crucial for improving the performance and scalability of wireless systems, particularly in resource-constrained environments and applications demanding high reliability and low latency.
Papers
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design
Linping Qu, Yuyi Mao, Shenghui Song, Chi-Ying Tsui
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
Linping Qu, Shenghui Song, Chi-Ying Tsui