Search Results for author:
Found 15 papers, 10 papers with code
Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction
Modulated signals exhibit long temporal dependencies, and extracting global features is crucial in identifying modulation schemes.
Self-Critical Alternate Learning based Semantic Broadcast Communication
In particular, to enable stable optimization via a nondifferentiable semantic metric, we regard sentence similarity as a reward and formulate this learning process as an RL problem.
Data Augmentation of Bridging the Delay Gap for DL-based Massive MIMO CSI Feedback
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) back to the base station (BS).
Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems
Hybrid beamforming is widely recognized as an important technique for millimeter wave (mmWave) multiple input multiple output (MIMO) systems.
Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems
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.
Semantics-Empowered Communication: A Tutorial-cum-Survey
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. g., theories, applications, metrics and implementations) in both academia and industry.
Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback
In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback.
Better Lightweight Network for Free: Codeword Mimic Learning for Massive MIMO CSI feedback
In this paper, a cost free distillation technique named codeword mimic (CM) is proposed to train better feedback networks with the practical lightweight encoder.
Elastic-Link for Binarized Neural Network
For example, we raise the top-1 accuracy of binarized ResNet26 from 57. 9% to 64. 0%.
OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation.
Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System
Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system.
Learning the Superpixel in a Non-iterative and Lifelong Manner
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 contour adherence.
Aggregated Network for Massive MIMO CSI Feedback
In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station.
Binary Neural Network Aided CSI Feedback in Massive MIMO System
In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station to achieve high performance gain.
Multi-resolution CSI Feedback with deep learning in Massive MIMO System
In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS).
