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Non-Orthogonal Superimposed Pilot for Goodput Enhancement in Digital Semantic communication

IEEE Transactions on Cognitive communications and Networking 2025年

作者： Zou, Jian Xiao, Jian Shen, Han Xie, Wenwu Meng, Fanyang Yang, Liang Liang, Yongsheng Shenzhen University College of Applied Technology Shenzhen518060 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Harbin Institute of Technology School of Electronics and Information Engineering Shenzhen518055 China Pengcheng Laboratory Research Center of Networks and Communications Shenzhen518060 China Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Jishou University School of Communication and Electronic Engineering Jishou416000 China Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China

A novel digital semantic communication (D-SemCom) framework is proposed to enhance the adaptability and goodput of multi-user-multiple-input multiple-output (MU-MIMO) orthogonal frequency division multiplexing (OFDM) systems by integrating the non-orthogonal superposition pilot (NOSIP) scheme. However, this introduces challenges, such as inter-user interference, which limit the performance of traditional signal processing methods. To address these challenges, we propose SANet, which realizes the nonlinear mapping of received signals to log-likelihood ratios. SANet leverages a common subspace and a multi-head self-attention mechanism to effectively suppress interference and optimize signal decoding performance. Additionally, SANet addresses the coupling between the semantic encoder-decoder and the channel environment, reducing the dependency on channel variations and enabling the system to adapt more flexibly to different channel and data scenarios. We also introduce an adaptive data augmentation strategy that dynamically adjusts the data representation based on channel conditions, further enhancing the reliability and performance of D-SemCom. Numerical results show that: 1) SANet improves the goodput of D-SemCom by 7.01% compared to traditional approaches;2) the goodput of D-SemCom is improved by 37.15% with the NOSIP scheme compared to the orthogonal pilot scheme;and 3) the effectiveness of the adaptive data augmentation strategy in enhancing D-SemCom performance is validated. © 2025 IEEE.

关键词： Frequency division multiplexing

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Finite-size analysis of continuous variable source-independent quantum random number generation

arXiv

引用

arXiv 2020年

作者： Zhang, Junyu Zhang, Yichen Zheng, Ziyong Chen, Ziyang Xu, Bingjie Yu, Song State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China

We study the impact of finite-size effect on continuous variable source-independent quantum random number generation. The central-limit theorem and maximum likelihood estimation theorem are used to derive the formula which could output the statistical fluctuations and determine upper bound of parameters of practical quantum random number generation. With these results, we can see the check data length and confidence probability has intense relevance to the final randomness, which can be adjusted according to the demand in implementation. Besides, other key parameters, such as sampling range size and sampling resolution, have also been considered in detail. It is found that the distribution of quantified output related with sampling range size has significant effects on the loss of final randomness due to finite-size effect. The overall results indicate that the finite-size effect should be taken into consideration for implementing the continuous variable source-independent quantum random number generation in practical. Copyright © 2020, The Authors. All rights reserved.

关键词： Random number generation

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Design of Irregular QC-LDPC Code Based Multi-Level Coded Modulation Scheme for High Speed Optical communication Systems

引用

China communications 2019年第5期16卷 106-120页

作者： Liqian Wang Dongdong Wang Yongjing Ni Xue Chen Midou Cui Fu Yang Beijing University of Posts and Telecommunications Beijing 100876China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050081China Science and Technology on Communication Networks Laboratory Shijiazhuang 050081China College of Information Science and Engineering Yanshan UniversityQinhuangdao 066000China College of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang 050000China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066000China

In this paper, we focus on the design of irregular QC-LDPC code based multi-level coded modulation(MLCM) scheme by jointly optimizing the component code rate and the degree distribution of the irregular QC-LDPC component code. Firstly, the sub-channel capacities of MLCM systems is analyzed and discussed, based on which the optimal component code rate can be obtained. Secondly, an extrinsic information transfer chart based two-stage searching algorithm is proposed to find the good irregular QC-LDPC code ensembles with optimal component code rates for their corresponding sub-channels. Finally, by constructing the irregular QC-LDPC component codes from the designed ensembles with the aim of possibly enlarging the girth and reducing the number of the shortest cycles, the designed irregular QC-LDPC code based 16QAM and 64QAM MLCM systems can achieve 0.4 dB and 1.2 dB net coding gain, respectively, compared with the recently proposed regular QC-LDPC code based 16QAM and 64QAM MLCM systems.

关键词： quasi-cyclic low-density parity check (QC-LDPC) code irregular, extrinsic information transfer(EXIT) chart generalized mutual information(GMI) multi-level coded modulation(MLCM)

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Smith-Purcell radiation from time grating

引用

Newton 2025年第2期1卷

作者： Juan-Feng Zhu Ayan Nussupbekov Yudi Fan Wenjie Zhou Zicheng Song Xuchen Wang Zi-Wen Zhang Chao-Hai Du Xianlong Wei Ping Bai Ching Eng Png Cheng-Wei Qiu Lin Wu Science Mathematics and Technology Singapore University of Technology and Design 8 Somapah Road Singapore 487372 Singapore Institute of High Performance Computing Agency for Science Technology and Research (A∗STAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore Center for Composite Materials and Structures School of Astronautics Harbin Institute of Technology Harbin China Institute of Nanotechnology Karlsruhe Institute of Technology Karlsruhe Germany Optical Communication Systems and Networks School of Electronics Peking University Beijing China Key Laboratory for the Physics and Chemistry of Nanodevices School of Electronics Peking University Beijing China

Smith-Purcell radiation (SPR) arises when an electron moves above a spatial grating, where fixed momentum compensation constrains the emission wavelength. In contrast, temporal SPR ( t -SPR) utilizes a time grating to leverage temporal periodicity for energy compensation, enabling more flexible radiation control. We introduce a generalized dispersion equation for t -SPR that predicts the interplay between radiation frequency, direction, electron velocity, modulation period, and harmonic orders. t -SPR offers several advantages over conventional SPR: robustness ( t -SPR maintains stable emission intensity without rapid decay as the electron moves away from the time grating), versatility ( t -SPR enables precise control via temporal modulation of period, amplitude, and waveform), and amplified emission (temporal modulation introduces additional energy channels, boosting emission). These features make t -SPR highly promising for advanced applications in on-chip light sources and electron accelerators.

关键词： Smith-Purcell radiation time grating photonic time crystal free-electron radiation

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Scientific Workflow Scheduling in Mobile Edge Computing Based on a Discrete Butterfly Optimization Algorithm

Research Square

引用

Research Square 2021年

作者： Lansky, Jan Mohammadi, Mokhtar Mohammed, Adil Hussein Taher Karim, Sarkhel H. Rashidi, Shima Rahmani, Amir Masoud Hosseinzadeh, Mehdi Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Information Technology Lebanese French University Kurdistan Region Erbil Iraq Department of Communication and computer engineering Cihan university-Erbil Kurdistan Region Erbil Iraq Computer Department College of Science University of Halabja Halabja Iraq Sulaimani Polytechnic University Technical College of Informatics Computer Networks Department Sulaymaniyah Iraq Department of Computer Science University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of computer science Khazar University Baku Azerbaijan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran

Mobile Edge Computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of the Internet of things (IoT) networks. However, MECs contain limited resources, and they should be managed effectively to improve resource utilization. Workflow scheduling is a process that tries to map the workflow tasks to the most proper set of computing resources regarding some objectives. For this purpose, this paper presents DBOA, a discrete version of the Butterfly Optimization Algorithm (BOA) that applies the Levy flight to improve its convergence speed and prevent the local optima problem. Then, DBOA is applied for DVFS-based data-intensive workflow scheduling and data placement in MEC environments. This scheme also employs the HEFT algorithm's task prioritization method to find the task execution order in the scientific workflows. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on various well-known scientific workflows with different sizes. The obtained experimental results indicate that this method can outperform other algorithms regarding energy consumption, data access overheads, etc. © 2021, CC BY.

关键词： Optimization

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Exploiting deep learning for secure transmission in an underlay cognitive radio network

arXiv

引用

arXiv 2021年

作者： Zhang, Miao Cumanan, Kanapathippillai Thiyagalingam, Jeyarajan Tang, Yanqun Wang, Wei Ding, Zhiguo Dobre, Octavia A. School of Information Science and Engineering Chongqing Jiaotong University Chongqing China Department of Electronic Engineering University of York YorkYO10 5DD United Kingdom Scientific Computing Department of Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus Ditcot United Kingdom School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen510006 China School of Information Science and Technology Nantong University Nantong China Nantong Research Institute for Advanced Communication Technologies Nantong China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom Department of Electrical and Computer Engineering Memorial University St. John’sNLA1B 3X5 Canada

This paper investigates a machine learning-based power allocation design for secure transmission in a cognitive radio (CR) network. In particular, a neural network (NN)based approach is proposed to maximize the secrecy rate of the secondary receiver under the constraints of total transmit power of secondary transmitter, and the interference leakage to the primary receiver, within which three different regularization schemes are developed. The key advantage of the proposed algorithm over conventional approaches is the capability to solve the power allocation problem with both perfect and imperfect channel state information. In a conventional setting, two completely different optimization frameworks have to be designed, namely the robust and non-robust designs. Furthermore, conventional algorithms are often based on iterative techniques, and hence, they require a considerable number of iterations, rendering them less suitable in future wireless networks where there are very stringent delay constraints. To meet the unprecedented requirements of future ultra-reliable low-latency networks, we propose an NN-based approach that can determine the power allocation in a CR network with significantly reduced computational time and complexity. As this trained NN only requires a small number of linear operations to yield the required power allocations, the approach can also be extended to different delay sensitive applications and services in future wireless networks. When evaluate the proposed method versus conventional approaches, using a suitable test set, the proposed approach can achieve more than 94% of the secrecy rate performance with less than 1% computation time and more than 93% satisfaction of interference leakage constraints. These results are obtained with significant reduction in computational time, which we believe that it is suitable for future real-time wireless applications. © 2021, CC0.

关键词： Cognitive radio

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Smart Sensing for Container Trucks

Smart Sensing for Container Trucks

引用

International Conference on Big Data Computing and communications (BIGCOM)

作者： Chengyuan Xu Bin Zhao Rongwei Yang Bingyi Liu Jianping Wang Jinfan Wang Department of Computer Science City University of Hong Kong Kowloon Hong Kong SAR Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States Research Center for Network Communication Peng Cheng Laboratory Shenzhen China School of Computer Science and Technology Wuhan University of Technology Wuhan China Institute of Future Networks Southern University of Science and Technology Shenzhen China

ISBN: (数字)9781728182759

ISBN: (纸本)9781728182766

Container logistics is a typical logistics model with complex business processes. In recent years, operation optimization in logistics is identified as significant in cutting labor costs and boosting productivity. To implement operation optimization in container logistics, the accurate load measuring and operational situation information are the prerequisites. However, the current labor-driven measurement and situation recognition suffer from inevitable delays, errors, and expenses. To tackle these challenges, the authors propose a smart sensing system that intelligently achieves the container load measurement and operational situation recognition. In this paper, the authors present a novel approach for load measurement and the design of an edge-computing-powered controller. The kNN (k-Nearest Neigh-bor) has been introduced to develop an Operational Situation Recognition (OSR) model. The authors realize and implement the smart sensing system in the production environment. Rigorous analysis and prototype evaluations demonstrate the effectiveness of the proposed system.

关键词： Containers Logistics Sensors Load modeling Machine learning Atmospheric measurements

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TOAN: Target-Oriented Alignment Network for fine-grained image categorization with few labeled samples

arXiv

引用

arXiv 2020年

作者： Huang, Huaxi Zhang, Junjie Yu, Litao Zhang, Jian Wu, Qiang Xu, Chang The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute of Advanced Communication and Data Science Shanghai University Shanghai200444 China The School of Computer Science University of Sydney SydneyNSW2006 Australia

In this paper, we study the fine-grained categorization problem under the few-shot setting, i.e., each fine-grained class only contains a few labeled examples, termed Fine-Grained Few-Shot classification (FGFS). The core predicament in FGFS is the high intra-class variance yet low inter-class fluctuations in the dataset. In traditional fine-grained classification, the high intra-class variance can be somewhat relieved by conducting the supervised training on the abundant labeled samples. However, with few labeled examples, it is hard for the FGFS model to learn a robust class representation with the significantly higher intra-class variance. Moreover, the inter- and intra-class variance are closely related. The significant intra-class variance in FGFS often aggravates the low inter-class variance issue. To address the above challenges, we propose a Target-Oriented Alignment Network (TOAN) to tackle the FGFS problem from both intra- and inter-class perspective. To reduce the intra-class variance, we propose a target-oriented matching mechanism to reformulate the spatial features of each support image to match the query ones in the embedding space. To enhance the inter-class discrimination, we devise discriminative fine-grained features by integrating local compositional concept representations with the global second-order pooling. We conducted extensive experiments on four public datasets for fine-grained categorization, and the results show the proposed TOAN obtains the state-of-the-art. © 2020, CC BY-NC-SA.

关键词： Image classification

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Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation

引用

IEEE Journal on Selected Areas in communications 2025年

作者： Wu, Shangrui Li, Yupeng Wang, Wenhua Guo, Jianxiong Fan, Wentao Liu, Qin Jia, Weijia Yu, Shui Cao, Jiannong Wang, Tian Beijing Normal-Hong Kong Baptist University Guangdong Provincial/Zhuhai Key Laboratory of IRADS Department of Computer Science Zhuhai China Hong Kong Baptist University Hong Kong Hong Kong Hong Kong Baptist University Department of Interactive Media Hong Kong Hong Kong Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an China Hunan University College of Computer Science and Electronic Engineering Changsha China University of Technology Sydney School of Computer Science Sydney Australia The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

The integration of edge computing with deep neural networks (DNNs) is crucial for intelligent industrial cyber-physical systems. Typically, deploying DNNs on heterogeneous edge devices relies on methods like model compression and partitioning. However, these approaches often result in homogeneous models across devices. This homogeneity limits the collective capability of edge computing systems, particularly in terms of generalization to diverse data distributions and adaptation to dynamic industrial environments. In this work, we propose to treat each DNN on an edge device as an independent model, aggregating their capabilities via ensemble learning to enhance generalization and dynamic adaptability. To realize this, we introduce the Adaptive Ensemble Knowledge Distillation Framework (AEKDF), combining cloud-based model training with edge computing based collaborative inference. In the cloud, AEKDF develops an enhanced Born Again Network that generates diverse, lightweight models tailored to specific edge devices through knowledge distillation. This process ensures model diversity which is critical to effective ensemble learning. On the edge, AEKDF employs an adaptive ensemble technique that aggregates prediction logits across devices, enabling rapid adaptation to changing environments and maintaining inference efficiency. Our extensive evaluations conducted on a realistic prototype demonstrate the substantial boost in predictive performance achieved by our AEKDF, showcasing a 4% to 10% accuracy improvement on the CIFAR-100 compared to conventional single-model approaches, while maintaining low latency. © 1983-2012 IEEE.

关键词： Deep neural networks

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Energy efficiency in qos constrained 60 ghz millimeter-wave ultra-dense networks 1

引用

14th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, Qshine 2018

作者： Nguyen, Huy Thanh Murakami, Homare Nguyen, Kien Ishizu, Kentaro Kojima, Fumihide Kim, Jong-Deok Chung, Sang-Hwa Hwang, Won-Joo Department of Information and Communication System Inje University GimhaeGyeongnam621-749 Korea Republic of Wireless Systems Laboratory Wireless Networks Research Center National Institute of Information and Communications Technology Yokosuka239-0847 Japan School of Electrical and Computer Engineering Pusan National University Busan46241 Korea Republic of

ISBN: (数字)9783030144135

ISBN: (纸本)9783030144128

Millimeter-Wave (mmWave) communication in ultra-dense networks (UDNs) has been considered as a promising technology for future wireless communication systems. Exploiting the benefits of mmWave and UDNs, we introduce a new approach for jointly optimizing small-cell base station (SBS)-user (UE) association and power allocation to maximize the system energy efficiency (EE) while guaranteeing the quality of service (QoS) constraints for each UE. The SBS-UE association problem poses a new challenge since it reflects as a complex mixed-integer non-convex problem. On the other hand, the power allocation problem is in non-convexity structure, which is impossible to handle with the association problem concurrently. An alternating descent method is thus introduced to divide the primal optimization problem into two sub-problems and handle one-by-one at each iteration, where the SBS-UE association problem is reformulated using the penalty approach. Then, path-following algorithms are developed to convert non-convex problem into the simple convex quadratic functions at each iteration. Numerical results are provided to demonstrate the convergence and low-complexity of our proposed schemes. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Convex optimization

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