12 citations · 36 across the 13 of their papers we have counts for
13 papers
Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction
Yunpeng Qu, Zhilin Lu, Rui Zeng +2
Automatic Modulation Recognition (AMR) plays a crucial role in wireless communication systems. Deep learning AMR strategies have achieved tremendous success in recent years. Modula…
Data Augmentation of Bridging the Delay Gap for DL-based Massive MIMO CSI Feedback
Hengyu Zhang, Zhilin Lu, Xudong Zhang +1
In massive multiple-input multiple-output (MIMO) systems under the frequency division duplexing (FDD) mode, the user equipment (UE) needs to feed channel state information (CSI) ba…
Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems
Zhilin Lu, Xudong Zhang, Rui Zeng +1
Hybrid beamforming is widely recognized as an important technique for millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Generalized spatial modulation (GSM) i…
Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems
Zhilin Lu, Xudong Zhang, Rui Zeng +1
Channel state information (CSI) feedback is necessary for the frequency division duplexing (FDD) multiple input multiple output (MIMO) systems due to the channel non-reciprocity. W…
Semantics-Empowered Communication: A Tutorial-cum-Survey
Zhilin Lu, Rongpeng Li, Kun Lu +4
Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.…
Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback
Xudong Zhang, Zhilin Lu, Rui Zeng +1
In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following…
Better Lightweight Network for Free: Codeword Mimic Learning for Massive MIMO CSI feedback
Zhilin Lu, Xudong Zhang, Rui Zeng +1
The channel state information (CSI) needs to be fed back from the user equipment (UE) to the base station (BS) in frequency division duplexing (FDD) multiple-input multiple-output…
Elastic-Link for Binarized Neural Network
Jie Hu, Ziheng Wu, Vince Tan +3
Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrai…
Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System
Zhilin Lu, Xudong Zhang, Hongyi He +2
Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs…
Learning the Superpixel in a Non-iterative and Lifelong Manner
Lei Zhu, Qi She, Bin Zhang +4
Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent c…
Aggregated Network for Massive MIMO CSI Feedback
Zhilin Lu, Hongyi He, Zhengyang Duan +2
In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station. The downlink CSI is essential for the m…
Binary Neural Network Aided CSI Feedback in Massive MIMO System
Zhilin Lu, Jintao Wang, Jian Song
In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station to achieve high performance gain. Recently, deep learning…
Multi-resolution CSI Feedback with deep learning in Massive MIMO System
Zhilin Lu, Jintao Wang, Jian Song
In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS). However, the feedback…