Article
Computer Science, Information Systems
Seung-Hwan Kim, Jae-Woo Kim, Williams-Paul Nwadiugwu, Dong-Seong Kim
Summary: In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. The input size is extended and then reduced to improve classification accuracy and reduce computation complexity. The simulation results demonstrate higher classification accuracy compared to conventional models across various signal-to-noise ratio (SNR) ranges.
Article
Chemistry, Analytical
Dong Wang, Meiyan Lin, Xiaoxu Zhang, Yonghui Huang, Yan Zhu
Summary: In this study, a CNN-transformer graph neural network (CTGNet) is proposed for modulation classification, which aims to uncover complex representations in signal data by transforming them into graph structures. Extensive experiments demonstrate that our method outperforms advanced deep learning techniques and achieves the highest recognition accuracy, highlighting the significant advantage of CTGNet in capturing key features in signal data and providing an effective solution for modulation classification tasks.
Article
Computer Science, Information Systems
Seung-Hwan Kim, Chang-Bae Moon, Jae-Woo Kim, Dong-Seong Kim
Summary: This study designs a hybrid signal and image-based deep learning model for automatic modulation classification in cognitive radio. The experimental results show that the proposed model significantly outperforms conventional models in terms of prediction accuracy.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2022)
Article
Computer Science, Information Systems
Hany S. Hussein, Mohamed Hassan Essai Ali, Mohammed Ismeil, Mohamed N. Shaaban, Mona Lotfy Mohamed, Hany A. Atallah
Summary: This study proposes robust CNN-based AMC techniques, which eliminate the need for feature extraction and can achieve high classification accuracy. The developed techniques utilize different classification layers and can automatically learn the features from the transmitted signals during training.
Article
Engineering, Electrical & Electronic
Chenghong Xiao, Shuyuan Yang, Zhixi Feng
Summary: In this article, a novel end-to-end automatic modulation classification (AMC) model called complex-valued depthwise separable convolutional neural network (CDSCNN) is proposed, which adopts complex-valued operation units for tailored feature learning for AMC. With an overall accuracy of 62.63% on the RadioML2016.10a dataset, CDSCNN outperforms its counterparts by 1%-11%. After fine-tuning on the RadioML2016.10b dataset, the overall accuracy reaches 63.15%, demonstrating the robust recognition and generalization capability of CDSCNN. Moreover, CDSCNN exhibits lower model complexity compared to other methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Telecommunications
Jiawei Zhang, Tiantian Wang, Zhixi Feng, Shuyuan Yang
Summary: With the advancement of modern communications technology, automatic modulation classification (AMC) has become increasingly important in complex wireless communication environments. Existing deep learning-based AMC schemes have limitations in utilizing feature maps, whereas the proposed adaptive wavelet network (AWN) overcomes this limitation by introducing adaptive wavelet decomposition and channel attention mechanism. AWN explicitly extracts features from multiple frequency bands and selects optimal frequencies, efficiently integrating signal properties in the frequency domain. Simulation results demonstrate the superiority of the proposed AMC scheme over benchmark schemes in terms of performance and computational complexity.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2023)
Article
Computer Science, Information Systems
Wei-Tao Zhang, Dan Cui, Shun-Tian Lou
Summary: This paper investigates the application of convolutional neural networks in identifying modulation classes for digitally modulated signals and proposes an effective network structure that outperforms traditional algorithms in terms of classification accuracy.
Article
Chemistry, Multidisciplinary
Rui Zhang, Zhendong Yin, Zhilu Wu, Siyang Zhou
Summary: This paper proposes a hybrid parallel network for the Automatic Modulation Classification (AMC) problem, utilizing Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial and temporal features, and applying different attention mechanisms to assign weights for each type of features. The Additive Margin softmax function is adopted for classification to expand inter-class distance and compress intra-class distance simultaneously. Simulation results show remarkable performance on an open access dataset.
APPLIED SCIENCES-BASEL
(2021)
Article
Telecommunications
Satish Kumar, Rajarshi Mahapatra, Anurag Singh
Summary: In this study, a CNN-based AMC scheme was designed and implemented to address the challenges of model size and floating-point weights and activations. The use of low precision and quantized CNN, along with the proposed RU-based AMC scheme and iterative pruning-based training mechanism, improved efficiency and accuracy on hardware.
IEEE COMMUNICATIONS LETTERS
(2022)
Article
Computer Science, Information Systems
Zhongyong Wang, Dongzhe Sun, Kexian Gong, Wei Wang, Peng Sun
Summary: This paper proposes a lightweight convolutional neural network for automatic modulation classification task, with a focus on reducing model complexity by designing depthwise separable convolution residual architecture and using global depthwise convolution for feature reconstruction. Experimental results show significant savings in model parameters and inference time compared to recent works.
Article
Computer Science, Information Systems
Biao Dong, Yuchao Liu, Guan Gui, Xue Fu, Heng Dong, Bamidele Adebisi, Haris Gacanin, Hikmet Sari
Summary: This article presents an efficient lightweight decentralized-learning-based AMC method for edge devices. The proposed method uses a spatiotemporal hybrid deep neural network based on multichannels and multifunction blocks to balance the trade-off between lightweight and classification performance. It also utilizes a cooperative approach for model updates and aggregation to enhance classification accuracy while reducing computational pressure.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Chemistry, Analytical
Fan Wang, Tao Shang, Chenhan Hu, Qing Liu
Summary: Automatic modulation classification (AMC) is crucial in intelligent wireless communication. This paper proposes a lightweight neural network-based AMC framework, which combines complex convolution with residual networks for improved classification performance. Depthwise separable convolution is used for a lightweight design, and a hybrid data augmentation scheme is introduced to compensate for any performance loss. Simulation results demonstrate that this framework reduces the number of parameters by approximately 83.34% and the FLOPs by approximately 83.77% without degrading performance.
Article
Computer Science, Artificial Intelligence
Cuiping Shi, Haiyang Wu, Liguo Wang
Summary: This paper proposes a double branch fusion network of CNN and enhanced graph attention network (CEGAT) based on key sample selection strategy for hyperspectral image classification. By eliminating spectral redundancy, extracting and assigning attention weight to spatial and spectral correlation features, and enhancing the relationship between nodes, the network achieves better classification performance.
Article
Engineering, Electrical & Electronic
Qiaoning Yang, Xiaodong Ji
Summary: This paper proposes an automatic pixel-level crack detection method based on deep transfer learning, which includes crack recognition and semantic segmentation stages, achieving efficient and reliable detection results. Experimental results show that the method can effectively detect pixel-level cracks and perform well in large-scale crack detection tasks.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Sezin Barin, Gur Emre Guraksin
Summary: This study compared different criteria for improving the performance of skin lesion segmentation and proposed a hybrid FCN-based deep learning architecture. The results showed that the proposed architecture outperformed traditional architectures in terms of accuracy, dice coefficient, and Jaccard index.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2022)
Article
Engineering, Electrical & Electronic
Fan Meng, Shengheng Liu, Yongming Huang, Zhaohua Lu
Summary: This paper proposes a learning-aided beam prediction scheme for high-speed railway (HSR) networks in high mobility scenarios. The scheme tackles the challenges of beam alignment and tracking by predicting future beam directions and channel amplitudes. The proposed scheme decomposes the high-dimensional prediction problem into a low-dimensional parameter estimation and a cascaded beamforming operation. Model-based and data-driven modules are incorporated to improve prediction accuracy and robustness.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Wenchao Li, Jiaxin Liu, Shuaichen Li, Quan Chai, Ye Tian, Xuelan He, Xianbin Wang, Yonggui Yuan, Jun Yang, Guoyong Jin, Jianzhong Zhang, Libo Yuan
Summary: This paper presents a novel in-fiber integrated high temperature sensor array with high spatial resolution for online temperature field measurement on a silicon nitride igniter. The sensor array is written by a femtosecond laser in the core of single-mode fiber, and can measure high temperature up to 1000 degrees C with the help of a white light interference demodulation system.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Xuanheng Li, Jiahong Liu, Nan Zhao, Xianbin Wang
Summary: This paper proposes a proactive joint strategy for trajectory and caching in UAV-assisted edge caching to maximize the reduced delay. A data-driven approach based on first and second-order statistics is developed to achieve a distributionally robust (DR) solution, ensuring the reliability and network performance.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
Editorial Material
Automation & Control Systems
Guangjie Han, Adnan M. Abu-Mahfouz, Joel J. P. C. Rodrigues, Xianbin Wang
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Chenyu Wu, Xidong Mu, Yuanwei Liu, Xuemai Gu, Xianbin Wang
Summary: This paper investigates the problem of resource allocation in a STAR-RIS-assisted multi-carrier communication network. The results show that the proposed scheme achieves comparable performance for different multiple access techniques and the STAR-RIS-aided NOMA network outperforms traditional RIS and OMA networks.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Ruikang Zhong, Xiao Liu, Yuanwei Liu, Yue Chen, Xianbin Wang
Summary: This paper proposes an indoor intelligent robot service framework that enables highly reliable communications. By adopting non-orthogonal multiple access (NOMA) technique and Lego modeling method, a radio map is constructed to optimize the mission efficiency and communication reliability of the robots. Simulation results demonstrate the effectiveness of using NOMA techniques and the DT-DPG algorithm.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Fang Fang, Bibo Wu, Shu Fu, Zhiguo Ding, Xianbin Wang
Summary: This paper investigates the energy efficient potential of simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) in a MIMO enabled NOMA system. An algorithm is proposed to optimize the transmit beamforming and phases of the low-cost passive elements on the STAR-RIS for maximizing system energy efficiency. Simulation results demonstrate the superior energy efficiency performance of the proposed algorithm using NOMA technology over OMA scheme and random phase shift scheme.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Ruitao Chen, Xianbin Wang
Summary: Mobile collaborative computing (MCC) is a new platform that effectively improves the quality of mobile service by utilizing idle computational resources in distributed mobile devices through peer-to-peer task offloading. This paper proposes a concept of value of service (VoS) to represent the total value of tasks and devices based on their performance. A situation-aware offloading scheme is proposed to maximize VoS by leveraging changing resource availability conditions. The paper also presents solutions to maximize VoS for binary and partial offloading scenarios.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Yanxin Li, Wei Wang, Mingqian Liu, Nan Zhao, Xu Jiang, Yunfei Chen, Xianbin Wang
Summary: In this article, a UAV-assisted NOMA transmission scheme is proposed to achieve secure downlink transmission via artificial jamming. The tradeoff between jamming power and sum rate is investigated by optimizing power allocation, user scheduling, and UAV trajectory, aiming to balance security and transmission performance.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Thakshanth Uthayakumar, Abubaker Abdelhafiz, Xianbin Wang, Ming Jian
Summary: The nonlinear distortions with memory effects caused by strong crosstalk between multiple power amplifier (PA) branches in multiple-input multiple-output (MIMO) systems pose challenges for behavioral modeling and linearization techniques. In this study, a decomposed cross-correlation based single-input-single-output (CC-SISO) architecture is proposed to estimate and cancel the simultaneous nonlinear and reverse crosstalk in PAs. This architecture significantly reduces the complexity of MIMO digital predistortion (DPD) models and eliminates the need for signal feedback paths, thus simplifying hardware implementation.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Review
Computer Science, Information Systems
Qihui Wu, Wei Wang, Zuguang Li, Bo Zhou, Yang Huang, Xianbin Wang
Summary: The sixth-generation (6G) wireless network aims to provide ubiquitous connectivity and diverse scenarios for various emerging applications. Full spectrum plays a crucial role in achieving the ambitious goal of a Tbps-scale data rate in 6G. This paper reviews the scenario and potential spectrum plan for 6G and introduces SpectrumChain, a blockchain-based dynamic spectrum-sharing (DSS) framework. The unique characteristics of blockchain for DSS and key technologies are presented. Finally, the conclusion and future development trends are discussed.
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Article
Telecommunications
Zuguang Li, Wei Wang, Qihui Wu, Xianbin Wang
Summary: In order to achieve secure and efficient dynamic spectrum sharing with guaranteed revenue and quality of service in future wireless communications, a consortium blockchain based DSS framework is proposed. Regulators supervise the entire DSS process to ensure revenue for each participant. Each MNO on the chain can act as a spectrum provider or requestor based on their demand, and the spectrum resource allocation is recorded on the chain with a smart contract. Optimal spectrum pricing and buying strategies are solved using a multi-leader multi-follower Stackelberg game model, and the equilibrium is determined with a proposed algorithm. A prototype is built using Hyperledger Fabric consortium blockchain, and the average latency is evaluated. Simulations and prototype evaluations confirm the feasibility of blockchain based DSS and reveal that average latency increases with the number of participants, providing valuable insights for real-world applications.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2023)
Proceedings Paper
Engineering, Electrical & Electronic
Thakshanth Uthayakumar, Jie Mei, Xianbin Wang
Summary: This study proposes a novel hybrid multi-dimensional modulation scheme that optimizes radio resource separation and minimizes non-orthogonal interference in multiple domains, achieving maximized communication datarate in beyond 5G and 6G networks.
2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING)
(2022)
Article
Engineering, Electrical & Electronic
M. G. S. Sriyananda, Xianbin Wang, Serguei Primak
Summary: Device and network coordination is crucial for efficient radio resource utilization and meeting QoS requirements in 3D small cell wireless networks. This study proposes a solution that utilizes opportunistic use of distributed radio resources, considering device positions and QoS requirements. The solution is developed using Q-learning and Slotted-ALOHA principles. Scheduling, power control, and access prioritization schemes aided by device and network coordination are discussed. An algorithm based on regret learning is presented for optimal utilization of unlicensed band radio resources. Results show a significant improvement in coordination efficiency compared to conventional methods.
IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY
(2022)
Proceedings Paper
Computer Science, Information Systems
Zewei Jing, Qinghai Yang, Yan Wu, Meng Qin, Kyung Sup Kwak, Xianbin Wang
Summary: In this paper, an adaptive cooperative task offloading algorithm is proposed to maximize the time-averaged energy efficiency for small cell MEC networks enabled by millimeter-wave backhauls. The algorithm makes a good tradeoff between cooperation utility and total energy consumption, and ensures network stability and task admission rate fulfillment.
2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
(2022)