Andrew Singer - IEEE Xplore Author Profile

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Author details

Andrew Singer

Also published under: A. C. Singer, Andrew C. Singer, A. Singer

Publications

233

Citations

4,498

Publications by Year

19652023

Co-Authors:

Jennifer R. AmosToros ArikanJ. AshbrookA. AshikhminHyeon-Min Bae

Show All Co-Authors (139)

Andrew Singer

Also published under: A. C. Singer, Andrew C. Singer, A. Singer

Affiliation

Dept. of ECE, UIUC

Publication Topics

Microphone Array,
Symbol Rate,
Transfer Function,
Underwater Acoustic,
Acoustic Channel,
Acoustic Communication,
Acoustic Signals,
Adaptive Filter,
Additive Noise,
Bit Error Rate,
Channel Estimation,
Cramer-Rao Lower Bound

Biography

Andrew C. Singer (Fellow, IEEE) received the S.B., S.M., and Ph.D. degrees, all in electrical engineering and computer science, from the Massachusetts Institute of Technology, Cambridge, MA, USA. He is currently the Fox Family Professor of Electrical and Computer Engineering and the Associate Dean for Innovation and Entrepreneurship with the University of Illinois at Urbana-Champaign, Urbana, IL, USA. His research interests include signal processing and communication systems.(Based on document published on 1 September 2020).

Publications

233

Citations

4,498

Publications by Year

19652023

Co-Authors:

Jennifer R. Amos
Toros Arikan
J. Ashbrook
A. Ashikhmin
Hyeon-Min Bae

Show All Co-Authors (139)

Author's Published Works

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Showing 1-25 of 292 results

Journals (167)

Conferences (113)

Early Access Articles (7)

Magazines (5)

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Peng Cheng Laboratory, Shenzhen, China(20)
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Results

A Theory for Semantic Channel Coding With Many-to-one Source

Shuai Ma;Chuanhui Zhang;Huayan Qi;Hang Li;Yue Bi;Guangming Shi;Naofal Al-Dhahir

IEEE Transactions on Cognitive Communications and Networking

Year: 2025 | Early Access Article |

As one of the potential key technologies of 6G, semantic communication is still in its infancy and there are many open problems, such as semantic entropy definition and semantic channel coding theory. To address these challenges, we investigate semantic information measures and semantic channel coding theorem. Specifically, we propose a semantic entropy definition as the uncertainty in the semanti...Show More

A Theory for Semantic Channel Coding With Many-to-one Source

Shuai Ma;Chuanhui Zhang;Huayan Qi;Hang Li;Yue Bi;Guangming Shi;Naofal Al-Dhahir

IEEE Transactions on Cognitive Communications and Networking

Year: 2025 | Early Access Article |

Visual Environment-Interactive Planning for Embodied Complex-Question Answering

Ning Lan;Baoshan Ou;Xuemei Xie;Guangming Shi

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Early Access Article |

This study focuses on Embodied Complex-Question Answering task, which means the embodied robot need to understand human questions with intricate structures and abstract semantics. The core of this task lies in making appropriate plans based on the perception of the visual environment. Existing methods often generate plans in a once-for-all manner, i.e., one-step planning. Such approach rely on lar...Show More

Visual Environment-Interactive Planning for Embodied Complex-Question Answering

Ning Lan;Baoshan Ou;Xuemei Xie;Guangming Shi

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Early Access Article |

Joint Spatial and Frequency Domain Learning for Lightweight Spectral Image Demosaicing

Fangfang Wu;Tao Huang;Junwei Xu;Xun Cao;Weisheng Dong;Le Dong;Guangming Shi

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

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Conventional spectral image demosaicing algorithms rely on pixels’ spatial or spectral correlations for reconstruction. Due to the missing data in the multispectral filter array (MSFA), the estimation of spatial or spectral correlations is inaccurate, leading to poor reconstruction results, and these algorithms are time-consuming. Deep learning-based spectral image demosaicing methods directly lea...Show More

Joint Spatial and Frequency Domain Learning for Lightweight Spectral Image Demosaicing

Fangfang Wu;Tao Huang;Junwei Xu;Xun Cao;Weisheng Dong;Le Dong;Guangming Shi

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

Shuai Ma;Zhiye Sun;Bin Shen;Youlong Wu;Hang Li;Guangming Shi;Shiyin Li;Naofal Al-Dhahir

IEEE Internet of Things Journal

Year: 2025 | Early Access Article |

In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in...Show More

Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

Shuai Ma;Zhiye Sun;Bin Shen;Youlong Wu;Hang Li;Guangming Shi;Shiyin Li;Naofal Al-Dhahir

IEEE Internet of Things Journal

Year: 2025 | Early Access Article |

Local Uncertainty Energy Transfer for Active Domain Adaptation

Yulin Sun;Guangming Shi;Weisheng Dong;Xin Li;Le Dong;Xuemei Xie

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

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Active Domain Adaptation (ADA) improves knowledge transfer efficiency from the labeled source domain to the unlabeled target domain by selecting a few target sample labels. However, most existing active sampling methods ignore the local uncertainty of neighbors in the target domain, making it easier to pick out anomalous samples that are detrimental to the model. To address this problem, we presen...Show More

Local Uncertainty Energy Transfer for Active Domain Adaptation

Yulin Sun;Guangming Shi;Weisheng Dong;Xin Li;Le Dong;Xuemei Xie

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

Distributed Optimization of Resource-Efficiency for Federated Edge Intelligence in UAV-Enabled IoT Networks

Yingyu Li;Jiangying Rao;Lian Wang;Yong Xiao;Xiaohu Ge;Guangming Shi

IEEE Internet of Things Journal

Year: 2025 | Early Access Article |

UAV-enabled IoT networks have shown promising potential in a range of novel applications and service scenarios such as extending the network coverage, extending the battery lifetime of IoT networks, and also supporting temporary data collecting and processing needs in various emergency situations. This paper studies a federated edge intelligence (FEI) network based on the data collected and upload...Show More

Distributed Optimization of Resource-Efficiency for Federated Edge Intelligence in UAV-Enabled IoT Networks

Yingyu Li;Jiangying Rao;Lian Wang;Yong Xiao;Xiaohu Ge;Guangming Shi

IEEE Internet of Things Journal

Year: 2025 | Early Access Article |

CDS-Net: Contextual Difference Sensitivity Network for Pixel-Wise Road Crack Detection

Qinzhong Tan;Ao Li;Le Dong;Weisheng Dong;Xin Li;Guangming Shi

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Early Access Article |

Road crack detection is a key computer vision task that identifies and locates cracks in road surface images, which usually have an irregular shape and contain only a few pixels in width. Generative and unsupervised methods are popular these years, but generative methods require a lot of training data and computational power while unsupervised methods are not so satisfactory in pixel-level segment...Show More

CDS-Net: Contextual Difference Sensitivity Network for Pixel-Wise Road Crack Detection

Qinzhong Tan;Ao Li;Le Dong;Weisheng Dong;Xin Li;Guangming Shi

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Early Access Article |

Spatial-Spectral Mixing Transformer With Hybrid Image Prior for Multispectral Image Demosaicing

Le Dong;Mengzu Liu;Tengteng Tang;Tao Huang;Jie Lin;Weisheng Dong;Guangming Shi

IEEE Journal of Selected Topics in Signal Processing

Year: 2025 | Volume: 19, Issue: 1 | Journal Article |

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The snapshot multispectral imaging system using a multispectral filter array (MSFA) efficiently captures sample the multispectral image (MSI) information of scenes and obtain spectral mosaic images. To obtain the complete MSI information from these spectral mosaics, effective demosaicing methods are essential. Traditional MSI demosaicing techniques depend on pixel correlation and various hand-craf...Show More

Spatial-Spectral Mixing Transformer With Hybrid Image Prior for Multispectral Image Demosaicing

Le Dong;Mengzu Liu;Tengteng Tang;Tao Huang;Jie Lin;Weisheng Dong;Guangming Shi

IEEE Journal of Selected Topics in Signal Processing

Year: 2025 | Volume: 19, Issue: 1 | Journal Article |

CrossEI: Boosting Motion-Oriented Object Tracking With an Event Camera

Zhiwen Chen;Jinjian Wu;Weisheng Dong;Leida Li;Guangming Shi

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

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With the differential sensitivity and high time resolution, event cameras can record detailed motion clues, which form a complementary advantage with frame-based cameras to enhance the object tracking, especially in challenging dynamic scenes. However, how to better match heterogeneous event-image data and exploit rich complementary cues from them still remains an open issue. In this paper, we ali...Show More

CrossEI: Boosting Motion-Oriented Object Tracking With an Event Camera

Zhiwen Chen;Jinjian Wu;Weisheng Dong;Leida Li;Guangming Shi

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

Scene Prior Constrained Self-Paced Learning for Unsupervised Satellite Video Vehicle Detection

Yuping Liang;Guangming Shi;Jinjian Wu

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Volume: 35, Issue: 3 | Journal Article |

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Recently, deep learning has significantly advanced the satellite object detection. However, the effectiveness of these methods heavily relies on abundant and accurate annotations, which are extremely labor-intensive for satellite videos. Meanwhile, the robustness of traditional difference-based methods is limited by the hand-craft feature from the satellite videos with low-resolution and frame mis...Show More

Scene Prior Constrained Self-Paced Learning for Unsupervised Satellite Video Vehicle Detection

Yuping Liang;Guangming Shi;Jinjian Wu

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Volume: 35, Issue: 3 | Journal Article |

Multi-Modality Multi-Attribute Contrastive Pre-Training for Image Aesthetics Computing

Yipo Huang;Leida Li;Pengfei Chen;Haoning Wu;Weisi Lin;Guangming Shi

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 2 | Journal Article |

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In the Image Aesthetics Computing (IAC) field, most prior methods leveraged the off-the-shelf backbones pre-trained on the large-scale ImageNet database. While these pre-trained backbones have achieved notable success, they often overemphasize object-level semantics and fail to capture the high-level concepts of image aesthetics, which may only achieve suboptimal performances. To tackle this long-...Show More

Multi-Modality Multi-Attribute Contrastive Pre-Training for Image Aesthetics Computing

Yipo Huang;Leida Li;Pengfei Chen;Haoning Wu;Weisi Lin;Guangming Shi

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 2 | Journal Article |

SANSee: A Physical-Layer Semantic-Aware Networking Framework for Distributed Wireless Sensing

Huixiang Zhu;Yong Xiao;Yingyu Li;Guangming Shi;Marwan Krunz

IEEE Transactions on Mobile Computing

Year: 2025 | Volume: 24, Issue: 3 | Journal Article |

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Contactless device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications using ubiquitously available radio frequency (RF) signals. Traditional approaches focus on developing a single global model based on a combined dataset collected from different locations. However, wireless signals are ...Show More

SANSee: A Physical-Layer Semantic-Aware Networking Framework for Distributed Wireless Sensing

Huixiang Zhu;Yong Xiao;Yingyu Li;Guangming Shi;Marwan Krunz

IEEE Transactions on Mobile Computing

Year: 2025 | Volume: 24, Issue: 3 | Journal Article |

Fast Window-Based Event Denoising With Spatiotemporal Correlation Enhancement

Huachen Fang;Jinjian Wu;Qibin Hou;Weisheng Dong;Guangming Shi

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 3 | Journal Article |

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Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, we propose window-based event denoising, which simultaneously deals with a stack of events while existing element-based denoising focuses on one event each time. Besides, we give the theoretical analysis based...Show More

Fast Window-Based Event Denoising With Spatiotemporal Correlation Enhancement

Huachen Fang;Jinjian Wu;Qibin Hou;Weisheng Dong;Guangming Shi

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 3 | Journal Article |

Learning Frame-Event Fusion for Motion Deblurring

Wen Yang;Jinjian Wu;Jupo Ma;Leida Li;Weisheng Dong;Guangming Shi

IEEE Transactions on Image Processing

Year: 2024 | Volume: 33 | Journal Article |

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Motion deblurring is a highly ill-posed problem due to the significant loss of motion information in the blurring process. Complementary informative features from auxiliary sensors such as event cameras can be explored for guiding motion deblurring. The event camera can capture rich motion information asynchronously with microsecond accuracy. In this paper, a novel frame-event fusion framework is ...Show More

Learning Frame-Event Fusion for Motion Deblurring

Wen Yang;Jinjian Wu;Jupo Ma;Leida Li;Weisheng Dong;Guangming Shi

IEEE Transactions on Image Processing

Year: 2024 | Volume: 33 | Journal Article |

Multi-Scale Spatio-Temporal Memory Network for Lightweight Video Denoising

Lu Sun;Fangfang Wu;Wei Ding;Xin Li;Jie Lin;Weisheng Dong;Guangming Shi

IEEE Transactions on Image Processing

Year: 2024 | Volume: 33 | Journal Article |

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Deep learning-based video denoising methods have achieved great performance improvements in recent years. However, the expensive computational cost arising from sophisticated network design has severely limited their applications in real-world scenarios. To address this practical weakness, we propose a multiscale spatio-temporal memory network for fast video denoising, named MSTMN, aiming at strik...Show More

Multi-Scale Spatio-Temporal Memory Network for Lightweight Video Denoising

Lu Sun;Fangfang Wu;Wei Ding;Xin Li;Jie Lin;Weisheng Dong;Guangming Shi

IEEE Transactions on Image Processing

Year: 2024 | Volume: 33 | Journal Article |

Swin Kansformer-Based Semantic Communication Systems for Wireless Image Transmission

Yunhao Wang;Shuai Ma;Dahua Gao;Guangming Shi

2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops)

Year: 2024 | Conference Paper |

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With the continuous advancement of wireless communication technology, numerous applications are gaining increasing popularity, driving the quest for more efficient and innovative semantic communication frameworks. In this paper, we investigate a semantic communication architecture specifically designed for wireless image transmission. We redesign Swin Transformer (SwinT) and propose a new architec...Show More

Swin Kansformer-Based Semantic Communication Systems for Wireless Image Transmission

Yunhao Wang;Shuai Ma;Dahua Gao;Guangming Shi

2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops)

Year: 2024 | Conference Paper |

Intellicise Wireless Networks From Semantic Communications: A Survey, Research Issues, and Challenges

Ping Zhang;Wenjun Xu;Yiming Liu;Xiaoqi Qin;Kai Niu;Shuguang Cui;Guangming Shi;Zhijin Qin;Xiaodong Xu;Fengyu Wang;Yue Meng;Chen Dong;Jincheng Dai;Qianqian Yang;Yaping Sun;Dahua Gao;Hui Gao;Shujun Han;Xiaodan Song

IEEE Communications Surveys & Tutorials

Year: 2024 | Early Access Article |

Information and communication technology (ICT) has been an essential part of modern society. However, the current communication systems are not sufficient to meet the demands of emerging applications. Intellicise (intelligent and concise) wireless networks, with their inherent characteristics of intelligence-endogenous and primitive-concise, have been proposed as a promising research direction. In...Show More

Intellicise Wireless Networks From Semantic Communications: A Survey, Research Issues, and Challenges

Ping Zhang;Wenjun Xu;Yiming Liu;Xiaoqi Qin;Kai Niu;Shuguang Cui;Guangming Shi;Zhijin Qin;Xiaodong Xu;Fengyu Wang;Yue Meng;Chen Dong;Jincheng Dai;Qianqian Yang;Yaping Sun;Dahua Gao;Hui Gao;Shujun Han;Xiaodan Song

IEEE Communications Surveys & Tutorials

Year: 2024 | Early Access Article |

A Comprehensive Review of RGBT Tracking

Haiping Zhang;Di Yuan;Xiu Shu;Zhihui Li;Qiao Liu;Xiaojun Chang;Zhenyu He;Guangming Shi

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Cited by: Papers (4)

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RGBT tracking seeks to leverage both visible and thermal infrared images to enhance the robustness of target tracking. This method makes up for the limitations of single-sensor tracking. The RGB and thermal infrared images complement each other effectively, enabling the tracker to operate seamlessly in complex environments day and night. However, RGBT tracking still faces challenges, such as therm...Show More

A Comprehensive Review of RGBT Tracking

Haiping Zhang;Di Yuan;Xiu Shu;Zhihui Li;Qiao Liu;Xiaojun Chang;Zhenyu He;Guangming Shi

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Human Pose Estimation via Parse Graph of Body Structure

Shibang Liu;Xuemei Xie;Guangming Shi

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 12 | Journal Article |

Cited by: Papers (1)

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When observing a person’s body, humans can extract the structured representation of the body called a parse graph, which includes the hierarchical decompositions from the entire body to parts and primitives and the context relations by horizontal links between the body parts. This ability helps humans better locate body structures at different levels. In order for the model to have this ability fo...Show More

Human Pose Estimation via Parse Graph of Body Structure

Shibang Liu;Xuemei Xie;Guangming Shi

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 12 | Journal Article |

Semantic Feature Division Multiple Access for Multi-User Digital Interference Networks

Shuai Ma;Chuanhui Zhang;Bin Shen;Youlong Wu;Hang Li;Shiyin Li;Guangming Shi;Naofal Al-Dhahir

IEEE Transactions on Wireless Communications

Year: 2024 | Volume: 23, Issue: 10 | Journal Article |

Cited by: Papers (1)

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With the ever-increasing user density and quality of service (QoS) demand, 5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extract...Show More

Semantic Feature Division Multiple Access for Multi-User Digital Interference Networks

Shuai Ma;Chuanhui Zhang;Bin Shen;Youlong Wu;Hang Li;Shiyin Li;Guangming Shi;Naofal Al-Dhahir

IEEE Transactions on Wireless Communications

Year: 2024 | Volume: 23, Issue: 10 | Journal Article |

Fast Universal Azimuth Signal Modeling for Maneuvering-Platform BFSAR Imaging

Xuanqi Wang;Yachao Li;Xuan Song;Baixiao Chen;Guangming Shi;Yitao Wang;Yuzhou Liu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

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Appropriate modeling and processing of echoes are the foundations for high-precision frequency-domain synthetic aperture radar (SAR) imaging. The complex geometry makes it challenging to accurately characterize and cope with the 2-D spatial variation of Doppler modulation in maneuvering-platform translational-variant bistatic forward-looking SAR (MTV-BFSAR), resulting in that the azimuth processin...Show More

Fast Universal Azimuth Signal Modeling for Maneuvering-Platform BFSAR Imaging

Xuanqi Wang;Yachao Li;Xuan Song;Baixiao Chen;Guangming Shi;Yitao Wang;Yuzhou Liu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Uncertainty Modeling of the Transmission Map for Single Image Dehazing

Bokang Wang;Qian Ning;Fangfang Wu;Xin Li;Weisheng Dong;Guangming Shi

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 11 | Journal Article |

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Despite rapid progress of end-to-end optimization for single-image dehazing, a long-standing open problem is the non-homogenous haze, at the core of the differences between synthetic hazy images and real hazy images. The atmospheric scattering model (ASM) has been widely adopted to model the degradation process of haze images but based on the assumption of homogeneous haze. In realistic scenarios,...Show More

Uncertainty Modeling of the Transmission Map for Single Image Dehazing

Bokang Wang;Qian Ning;Fangfang Wu;Xin Li;Weisheng Dong;Guangming Shi

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 11 | Journal Article |

Mobility-Aware MEC Planning With a GNN-Based Graph Partitioning Framework

Jiayi Liu;Zhongyi Xu;Chen Wang;Xuefang Liu;Xuemei Xie;Guangming Shi

IEEE Transactions on Network and Service Management

Year: 2024 | Volume: 21, Issue: 4 | Journal Article |

Cited by: Papers (1)

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Mobile service continuity is essential important to ensure that user sessions and services will survive user mobility. The 5G enhances its mobility management by providing the flexibility and offering three types of Session and Service Continuity (SSC) modes to address various service continuity requirements. Multi-access edge computing (MEC) is a type of widely adopted network architecture that d...Show More

Mobility-Aware MEC Planning With a GNN-Based Graph Partitioning Framework

Jiayi Liu;Zhongyi Xu;Chen Wang;Xuefang Liu;Xuemei Xie;Guangming Shi

IEEE Transactions on Network and Service Management

Year: 2024 | Volume: 21, Issue: 4 | Journal Article |

Degradation Estimation Recurrent Neural Network With Local and Non-Local Priors for Compressive Spectral Imaging

Yubo Dong;Dahua Gao;Yuyan Li;Guangming Shi;Danhua Liu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (2)

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In the coded aperture snapshot spectral imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3-D hyperspectral images (HSIs) from 2-D measurements. However, some noticeable gaps exist between the imaging model used in DUNs and the real CASSI imaging process, such as the sensing error as well as photon and dark current noise, compromising the ...Show More

Degradation Estimation Recurrent Neural Network With Local and Non-Local Priors for Compressive Spectral Imaging

Yubo Dong;Dahua Gao;Yuyan Li;Guangming Shi;Danhua Liu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Graph Transformer and LSTM Attention for VNF Multi-Step Workload Prediction in SFC

Yu Wu;Jiayi Liu;Chen Wang;Xuemei Xie;Guangming Shi

IEEE Transactions on Network and Service Management

Year: 2024 | Volume: 21, Issue: 4 | Journal Article |

Cited by: Papers (1)

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The knowledge on a Service Function Chain’s (SFC’s) resource requirements is an indispensable prerequisite for proactive resource provisioning and run-time management of the SFC. However, due to the intrinsic dynamics in network environment, accurate resource requirements and workloads prediction for the Virtual Network Functions (VNFs) of a SFC, especially in a large time scale, is a non-trivial ...Show More

Graph Transformer and LSTM Attention for VNF Multi-Step Workload Prediction in SFC

Yu Wu;Jiayi Liu;Chen Wang;Xuemei Xie;Guangming Shi

IEEE Transactions on Network and Service Management

Year: 2024 | Volume: 21, Issue: 4 | Journal Article |

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