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Blind separation of noncooperative paired carrier multiple access signals based on improved quantum-inspired evolutionary algorithm and receding horizon optimization

Blind separation of noncooperative paired carrier multiple a...

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作者： Deng, Qi Zhang, Shanshan Chen, Gang Lu, Huaxiang Lab of Artificial Networks Institute of Semiconductors CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China

The single-channel blind source separation of paired carrier multiple access (PCMA) signal is a key technology in satellite communications. Due to the high-order complexity of existing separation methods and the uncertainty of channel parameters, the processing of noncooperative PCMA signals with long memory remains a great challenge. In this article, the blind separation was solved as the joint channel estimation and sequence detection, and a novel blind separation algorithm based on the improved quantum-inspired evolutionary algorithm (IQEA) and receding horizon optimization was proposed. Considering the practical scenarios, a mixed PCMA signal model with noninteger period sampling was presented in advance. Combined with the proposed signal model, the IQEA was applied to recover the uplink signals from the mixed PCMA signals by its iterative estimation. Meanwhile, to reduce the computational complexity caused by long memory of PCMA signals, the receding horizon optimization was introduced to process the whole signal sequence as a dynamic programming problem. The simulation results verified that the proposed RH-IQEA outperforms existing state-of-the-art separation algorithms in the blind separation of noncooperative PCMA signals, with higher accuracy, lower-order complexity and stronger robustness to parameter estimation errors. © 2021 John Wiley & Sons, Ltd.

关键词： Iterative methods

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When Digital Twin Meets 6G: Concepts, Obstacles, and Research Prospects

arXiv

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arXiv 2024年

作者： Liu, Wenshuai Fu, Yaru Shi, Zheng Wang, Hong The School of Science and Technology Hong Kong Metropolitan University 999077 Hong Kong The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China

The convergence of digital twin technology and the emerging 6G network presents both challenges and numerous research opportunities. This article explores the potential synergies between digital twin and 6G, highlighting the key challenges and proposing fundamental principles for their integration. We discuss the unique requirements and capabilities of digital twin in the context of 6G networks, such as sustainable deployment, real-time synchronization, seamless migration, predictive analytic, and closed-loop control. Furthermore, we identify research opportunities for leveraging digital twin and artificial intelligence to enhance various aspects of 6G, including network optimization, resource allocation, security, and intelligent service provisioning. This article aims to stimulate further research and innovation at the intersection of digital twin and 6G, paving the way for transformative applications and services in the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Deep Reservoir Computing for 100 Gbaud PAM6 IM/DD Transmission Impairment Mitigation

Deep Reservoir Computing for 100 Gbaud PAM6 IM/DD Transmissi...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Han, Mengyao Wang, Muguang Fan, Yuchuan Salgals, Toms Louchet, Hadrien Schatz, Richard Gruen, Markus Pittala, Fabio Krüger, Benjamin Dippon, Thomas Zhang, Lu Yu, Xianbin Spolitis, Sandis Bobrovs, Vjaceslavs Popov, Sergei Pang, Xiaodan Ozolins, Oskars Ministry of Education Beijing Jiaotong University Institute of Lightwave Technology Key Lab of All Optical Network & Advanced Telecommunication Network Beijing100044 China KTH Royal Institute of Technology Department of Applied Physics Stockholm106 91 Sweden RISE Research Institutes of Sweden Kista164 40 Sweden Riga Technical University Institute of Telecommunications Riga1048 Latvia Keysight Technologies GmbH Böblingen Germany Zhejiang University College of Information Science and Electronic Engineering Zhejiang Lab Hangzhou China Riga Technical University Communication Technologies Research Center Riga1048 Latvia

We experimentally evaluate a deep Reservoir Computing (RC)-based post-equalization for 100 Gbaud PAM6 IM/DD transmissions. It achieves ∼1 dB higher sensitivity than DFE, and ∼50% implementation complexity reduction ... 详细信息

ISBN: (纸本)9781957171180

关键词： Pulse amplitude modulation

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CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation

arXiv

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arXiv 2023年

作者： Zhang, Zekang Gao, Guangyu Jiao, Jianbo Liu, Chi Harold Wei, Yunchao School of Computer Science Beijing Institute of Technology China School of Computer Science University of Birmingham United Kingdom WEI Lab Institute of Information Science Beijing Jiaotong University China Beijing Key Laboratory of Advanced Information Science and Network China

Class incremental semantic segmentation aims to strike a balance between the model’s stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model’s plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the model’s plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning. Inspired by the Gaussian mixture model that samples from a mixture of Gaussian distributions, CoinSeg emphasizes intra-class diversity with multiple contrastive representation centroids. Specifically, we use mask proposals to identify regions with strong objectness that are likely to be diverse instances/centroids of a category. These mask proposals are then used for contrastive representations to reinforce intra-class diversity. Meanwhile, to avoid bias from intra-class diversity, we also apply category-level pseudo-labels to enhance category-level consistency and inter-category diversity. Additionally, CoinSeg ensures the model’s stability and alleviates forgetting through a specific flexible tuning strategy. We validate CoinSeg on Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and achieve superior results compared to previous state-of-the-art methods, especially in more challenging and realistic long-term scenarios. Code is available at https://***/zkzhang98/CoinSeg. © 2023, CC BY.

关键词： Gaussian distribution

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Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

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2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit networks

arXiv

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arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Robot and its living space:A roadmap for robot development based on the view of living space

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Digital Communications and networks 2021年第4期7卷 505-517页

作者： Xiaofeng Cai Huansheng Ning Sahraoui Dhelim Rongjuan Zhou Tao Zhang Yang Xu Yueliang Wan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China School of Foreign Studies University of Science and Technology Beijing Beijing 100083China Key Lab of Information Network Security Ministry of Public SecurityChina Run Technologies Co. LidBeijing China

Robot’s living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society Robots,as the creation of humans,became an irreplaceable component in human *** the advancement of technologies,robots have become more and more intelligent and have been widely used in many fields,such as disease diagnosis,customer services,healthcare for the older people,and so *** robots made our lives much more convenient than ever before,they also brought many potential risks and challenges in technology,security,and *** better understand the development of robots,we proposed a concept of a robot’s living space and analyzed the role of robots in our *** this paper,we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot *** research in this paper contains three central ***,we interpret the concept of the robot’s living space and the functions of each ***,we analyze and summarize the relative technologies which support robots living well in each ***,we provide advice and improvement measures based on a discussion of potential problems caused by the developments of *** the trend of robots humanization and human-robot society integration,we should seriously consider how to collaborate with intelligent robots to achieve hybrid *** build a harmonious human-robot integrated society,studying the robot’s living space and its relationship with humans is the prerequisite and roadmap.

关键词： Robot's living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society

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SiamIRPN: Siamese Visual Tracking with Improved Region Proposal networks

SiamIRPN: Siamese Visual Tracking with Improved Region Propo...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： Cao, Dun Dai, Renhua Zhao, Tianyu Alqahtani, Fayez Tolba, Amr Sherratt, R. Simon Zhu, Min Changsha University of Science and Technology School of Computer and Communications Engineering Hunan Changsha China Nanjing University of Posts and Telecommunications Ministry of Education Key Lab of Broadcast Wireless Communication and Sensor Network Technology China King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia The University of Reading School of Biomedical Engineering RG6 6AY United Kingdom Zhejiang Shuren University College of Information Science and Technology Zhejiang31000 China

ISBN: (纸本)9798350301113

Though introducing the Region Proposal network (RPN) from object detection enabled Siamese trackers' success, RPN-based trackers still struggle in challenging scenarios. We posit that the reason comes from two major limitations of the introduced RPN, where one is that the external structure of the RPN is simple and straight, the other is that the internal components used in RPN cannot cope with complex scenes. In this paper, we propose an Improved RPN (IRPN) suitable for visual tracking. Externally, we place a Convolutional Block Attention Mechanism (CBAM) to the IRPN, and internally, we adopt the advanced component groups for the IPRN. Using multiple IRPN blocks and deep architecture, we propose a Siamese tracker (SiamIRPN) having a layer-wise structure. Comprehensive experiments and ablation studies on five benchmarks (VOT-2019, OTB100, UAV123, GOT-10k, and NFS) show our proposed SiamIRPN achieves competitive performance. © 2023 IEEE.

关键词： Object detection

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Few-shot Encrypted Malicious Traffic Classification via Hierarchical Semantics and Adaptive Prototype Learning

Few-shot Encrypted Malicious Traffic Classification via Hier...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuan Zhao Chunhe Xia Tianbo Wang Mengyao Liu Yang Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

While encrypted traffic improves security, it is also used by attackers to hide the transmission content to evade detection. Currently, traffic side-channel features combined with Deep Learning (DL) are widely used for malicious traffic classification, but traditional DL-based methods require large training samples and struggle with new threats. Prototypical networks in meta-learning have been effective in few-shot malicious traffic classification. However, existing methods face challenges such as "overlooking hierarchical traffic dependencies" and "bias in class prototype generation". The former means that the existing methods lack the representation design based on the hierarchical structure of traffic, resulting in insufficient feature extraction, and the latter indicates that the existing methods struggle to capture the diverse distribution of traffic features, resulting in unstable classification performance. To address the above problems, this paper proposes a few-shot encrypted malicious traffic classification method based on Hierarchical Semantics and Adaptive Prototype Learning network (HANet). First, network traffic’s fine-grained features are represented in a multi-level matrix, with a hierarchical network structure designed to extract features comprehensively. Then, class prototypes are dynamically generated using a neighborhood partitioning method to balance simple and complex traffic feature distributions, enhancing generalization. Experiments on the CICandMal2017 dataset show that HANet offers significant performance over other few-shot malicious traffic classification methods. HANet has achieved a classification accuracy of more than 80% with only 5, 10, and 15 labeled traffic samples, realizing effective detection of few-shot encrypted malicious traffic.

关键词： Training Metalearning Privacy Semantics Prototypes Telecommunication traffic Feature extraction Loss measurement Cryptography Ransomware

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GERE: Generative Evidence Retrieval for Fact Verification

arXiv

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arXiv 2022年

作者： Chen, Jiangui Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

Fact verification (FV) is a challenging task which aims to verify a claim using multiple evidential sentences from trustworthy corpora, e.g., Wikipedia. Most existing approaches follow a three-step pipeline framework, including document retrieval, sentence retrieval and claim verification. High-quality evidences provided by the first two steps are the foundation of the effective reasoning in the last step. Despite being important, high-quality evidences are rarely studied by existing works for FV, which often adopt the off-the-shelf models to retrieve relevant documents and sentences in an "index-retrieve-then-rank" fashion. This classical approach has clear drawbacks as follows: i) a large document index as well as a complicated search process is required, leading to considerable memory and computational overhead;ii) independent scoring paradigms fail to capture the interactions among documents and sentences in ranking;iii) a fixed number of sentences are selected to form the final evidence set. In this work, we propose GERE, the first system that retrieves evidences in a generative fashion, i.e., generating the document titles as well as evidence sentence identifiers. This enables us to mitigate the aforementioned technical issues since: i) the memory and computational cost is greatly reduced because the document index is eliminated and the heavy ranking process is replaced by a light generative process;ii) the dependency between documents and that between sentences could be captured via sequential generation process;iii) the generative formulation allows us to dynamically select a precise set of relevant evidences for each claim. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines, with both time-efficiency and memory-efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Efficiency

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