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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Fusing Bluetooth with Pedestrian Dead Reckoning: A Floor Plan-Assisted Positioning Approach

arXiv

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arXiv 2025年

作者： Pan, Wenxuan Yang, Yang Chen, Mingzhe Wei, Dong Guo, Caili Mao, Shiwen Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering the Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Wireless Engineering Research and Education Center Auburn University AuburnAL36849 United States

Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information and applying it to complex indoor layouts. To fully exploit information from floor plans for positioning, we propose a floor plan-assisted fusion positioning algorithm (FP-BP) using Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). In the considered system, a user holding a smartphone walks through a positioning area with BLE beacons installed on the ceiling, and can locate himself in real time. In particular, FP-BP consists of two phases. In the offline phase, FP-BP programmatically extracts map features from a stylized floor plan based on their binary masks, and constructs a mapping function to identify the corresponding map feature of any given position on the map. In the online phase, FP-BP continuously computes BLE positions and PDR results from BLE signals and smartphone sensors, where a novel grid-based maximum likelihood estimation (GML) algorithm is introduced to enhance BLE positioning. Then, a particle filter is used to fuse them and obtain an initial estimate. Finally, FP-BP performs post-position correction to obtain the final position based on its specific map feature. Experimental results show that FP-BP can achieve a real-time mean positioning accuracy of 1.19 m, representing an improvement of over 28% compared to existing floor plan-fused baseline algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

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Cost-efficient and skew-aware data scheduling for incremental learning in 5G network

arXiv

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arXiv 2020年

作者： Pu, Lingjun Yuan, Xinjing Xu, Xiaohang Chen, Xu Zhou, Pan Xu, Jingdong The College of Computer Science Nankai University Tianjin300071 China The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710126 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Pazhou Lab. Guangzhou510335 China The Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recognized the power of machine learning (ML) and started to explore its potential to facilitate those network functions. Nevertheless, the current ML models for network functions are often derived in an offline manner, which is inefficient due to the excessive overhead for transmitting a huge volume of dataset to remote ML training clouds and failing to provide the incremental learning capability for the continuous model updating. As an alternative solution, we propose Cocktail, an incremental learning framework within a reference 5G network architecture. To achieve cost efficiency while increasing trained model accuracy, an efficient online data scheduling policy is essential. To this end, we formulate an online data scheduling problem to optimize the framework cost while alleviating the data skew issue caused by the capacity heterogeneity of training workers from the long-term perspective. We exploit the stochastic gradient descent to devise an online asymptotically optimal algorithm, including two optimal policies based on novel graph constructions for skew-aware data collection and data training. Small-scale testbed and large-scale simulations validate the superior performance of our proposed framework. © 2020, CC BY.

关键词： 5G mobile communication systems

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Generative neural network based spectrum sharing using linear sum assignment problems

arXiv

引用

arXiv 2019年

作者： Zaky, Ahmed B. Huang, Joshua Zhexue Wu, Kaishun ElHalawany, Basem M. Big data Institute School of Computer Science Shenzhen University China Benha University Egypt Shenzhen University China PCL Research Center of Networks and Communications Peng Cheng Laboratory China

Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large-size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising generative models, for enabling spectrum sharing in underlay device-to-device (D2D) communication by solving linear sum assignment problems (LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders (CVAE). The three proposed architecture are the convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Indirect influence in social networks as an induced percolation phenomenon (vol 119, e2100151119, 2022)

引用

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2022年第24期119卷 e2100151119页

作者： Xie, Jiarong Wang, Xiangrong Feng, Ling Zhao, Jin-Hua Liu, Wenyuan Moreno, Yamir Hu, Yanqing School of Computer Science and Engineering Sun Yat-sen University 510006 Guangzhou China Institute of Future Networks Southern University of Science and Technology 518055 Shenzhen China Peng Cheng Laboratory 518066 Shenzhen China Institute of High Performance Computing A*STAR 138632 Singapore Department of Physics National University of Singapore 117551 Singapore Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University 510006 Guangzhou China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University 510006 Guangzhou China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Institute for Biocomputation and Physics of Complex Systems University of Zaragoza 50018 Zaragoza Spain Department of Theoretical Physics University of Zaragoza 50018 Zaragoza Spain ISI Foundation 10126 Torino Italy Department of Statistics and Data Science College of Science Southern University of Science and Technology 518055 Shenzhen China

Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions.

关键词： percolation indirect interactions social network phase transition behavioral contagion

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Why Are There Six Degrees of Separation in a Social Network?

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Physical Review X 2023年第2期13卷 021032-021032页

作者： I. Samoylenko D. Aleja E. Primo K. Alfaro-Bittner E. Vasilyeva K. Kovalenko D. Musatov A. M. Raigorodskii R. Criado M. Romance D. Papo M. Perc B. Barzel S. Boccaletti Moscow Institute of Physics and Technology 9 Institutskiy Pereulok Dolgoprudny Moscow Region 141701 Russia National Research University Higher School of Economics 6 Ulitsa Usacheva Moscow 119048 Russia Universidad Rey Juan Carlos Calle Tulipán s/n 28933 Móstoles Madrid Spain Department of Internal Medicine University of Michigan Ann Arbor Michigan 48109 USA Laboratory of Mathematical Computation on Complex Networks and their Applications Universidad Rey Juan Carlos Calle Tulipán s/n Móstoles 28933 Madrid Spain P.N. Lebedev Physical Institute of the Russian Academy of Sciences 53 Leninsky Prospekt Moscow 119991 Russia Scuola Superiore Meridionale Largo S. Marcellino 10 80138 Napoli NA Italy Russian Academy of National Economy and Public Administration 84 Prospekt Vernadskogo Moscow 119606 Russia Caucasus Mathematical Center at Adyghe State University 208 Pervomayskaya Ulitsa Maykop Adygea 385000 Russia Moscow State University 1 Leninskie Gory Moscow 119991 Russia Institute of Mathematics and Computer Science Buryat State University 5 Ulitsa Ranzhurova Ulan-Ude Buryatia 670000 Russia Data Complex Networks and Cybersecurity Research Institute Universidad Rey Juan Carlos Plaza Manuel Becerra 14 28028 Madrid Spain Department of Neuroscience and Rehabilitation University of Ferrara Ferrara Italy Center for Translational Neurophysiology of Speech and Communication Fondazione Istituto Italiano di Tecnologia Ferrara Italy Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 2000 Maribor Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung 404332 Taiwan Complexity Science Hub Vienna Josefstädterstraße 39 1080 Vienna Austria Alma Mater Europaea Slovenska ulica 17 2000 Maribor Slovenia Department of Physics Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul Republic of Korea Department of Mathematics Bar-Ilan University Ramat-Gan 52900 Israel The Gonda Multi

A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. We show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. We show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, our results show how simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.

关键词： Degree distributions Network evolution Real world networks Small-world networks

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The effectiveness of backward contact tracing in networks

arXiv

引用

arXiv 2020年

作者： Kojaku, Sadamori Hébert-Dufresne, Laurent Mones, Enys Lehmann, Sune Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States DTU Compute Technical University of Denmark Kgs. Lyngby 2800 Denmark Center for Social Data Science University of Copenhagen Copenhagen K1353 Denmark Indiana University Network Science Institute Indiana University BloomingtonIN47408 United States

Discovering and isolating infected individuals is a cornerstone of epidemic control1-7. Because many infectious diseases spread through close contacts, contact tracing is a key tool for case discovery and control8-15. However, although contact tracing has been performed widely, the mathematical understanding of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing-tracing to whom disease spreads, "backward" tracing-tracing from whom disease spreads-is profoundly more effective. The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts. Using simulations on both synthetic and high-resolution empirical contact datasets, we show that even at a small probability of detecting infected individuals, strategically executed contact tracing can prevent a significant fraction of further transmissions. We also show that-in terms of the number of prevented transmissions per isolation-case isolation combined with a small amount of contact tracing is more efficient than case isolation alone. By demonstrating that backward contact tracing is highly effective at discovering super-spreading events, we argue that the potential effectiveness of contact tracing has been underestimated. Therefore, there is a critical need for revisiting current contact tracing strategies so that they leverage all forms of biases. Our results also have important consequences for digital contact tracing because it will be crucial to incorporate the capability for backward and deep tracing while adhering to the privacy-preserving requirements of these new platforms. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Author Correction: Segment Anything for Microscopy

引用

Nature methods 2025年 2025 Jun 10页

作者： Anwai Archit Luca Freckmann Sushmita Nair Nabeel Khalid Paul Hilt Vikas Rajashekar Marei Freitag Carolin Teuber Melanie Spitzner Constanza Tapia Contreras Genevieve Buckley Sebastian von Haaren Sagnik Gupta Marian Grade Matthias Wirth Günter Schneider Andreas Dengel Sheraz Ahmed Constantin Pape Georg-August-University Göttingen Institute of Computer Science Goettingen Germany. German Research Center for Artificial Intelligence Kaiserslautern Germany. RPTU Kaiserslautern-Landau Kaiserslautern Germany. Department of General Visceral and Pediatric Surgery University Medical Center Göttingen Göttingen Germany. Ramaciotti Centre for Cryo-Electron Microscopy Monash University Melbourne Victoria Australia. Georg-August-University Göttingen Campus Institute Data Science Goettingen Germany. CCC-N (Comprehensive Cancer Center Lower Saxony) Göttingen Germany. Department of Hematology Oncology and Cancer Immunology (Campus Benjamin Franklin) Charité Universitätsmedizin Berlin Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Max Delbrück Center Berlin Germany. Clinical Research Unit 5002 KFO5002 University Medical Center Göttingen Germany. Georg-August-University Göttingen Institute of Computer Science Goettingen Germany. constantin.pape@informatik.uni-goettingen.de. Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC) Georg-August-University Göttingen Göttingen Germany. constantin.pape@informatik.uni-goettingen.de.

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A practical guide to machine learning interatomic potentials – Status and future

arXiv

引用

arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

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

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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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