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A New User Grouping and Power Allocation Scheme for Downlink Non-orthogonal Multiple Access Systems 3

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A New User Grouping and Power Allocation Scheme for Downlink...

2019 3rd International Conference on Data Mining, Communications and Information Technology, DMCIT 2019

作者： Wang, Yin Li, Xiaohui Wang, Wenwu Zhang, Gongquan Zhang, Hongwei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230039 China

In this paper, we propose a new user grouping and power allocation scheme based on beamforming for downlink non-orthogonal multiple access systems. The proposed user grouping scheme can effectively reduce the interference from the other user and other beams as well, and can effectively improve the weak user rate especially when the SNR is not large. In addition, a power allocation scheme that can maximize the sum capacity while satisfying a certain fairness index is proposed. From the simulation results, the user grouping and power allocation method proposed in this paper can not only improve the overall system throughput performance, but also improve the fairness of weak users. © 2019 Published under licence by IOP Publishing Ltd.

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An enumeration of 1-perfect ternary codes

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arXiv 2021年

作者： Shi, Minjia Krotov, Denis S. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Sobolev Institute of Mathematics Novosibirsk630090 Russia

MSC Codes 94B25We study codes with parameters of the ternary Hamming (n=(3m−1)/2, 3n−m, 3) code, i.e., ternary 1-perfect codes. The rank of the code is defined to be the dimension of its affine span. We characterize ternary 1-perfect codes of rank n − m + 1, count their number, and prove that all such codes can be obtained from each other by a sequence of two-coordinate switchings. We enumerate ternary 1-perfect codes of length 13 obtained by concatenation from codes of lengths 9 and 4;we find that there are 93241327 equivalence classes of such codes. Copyright © 2021, The Authors. All rights reserved.

关键词： Equivalence classes

Multi-Attention Network for 2D Face Alignment in the Wild 14th

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Multi-Attention Network for 2D Face Alignment in the Wild

14th Conference on Image and Graphics Technologies and Applications, IGTA 2019

作者： Liu, Xin Wang, Huabin Cheng, Rui Yan, Xiang Tao, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230031 China

ISBN: (纸本)9789811399169

Most existing algorithms based on Convolutional Neural Networks (CNNs) for face alignment ignore the significance of attention mechanism. In this paper, we propose a Multi-Attention Network (MANet) for robust face alignment. Our attention mechanism includes multi-level feature attention and multi-scale attention. Multi-level feature attention is introduced for the purpose of paying attention to features of different levels, specifically, high-level feature attentions are essential for correlations among neighboring regions whereas low-level feature attentions focus on detailed description for local parts. While multi-scale attention is designed to obtain better representation the features of different scales. The attentions mentioned above are utilized for better feature presentation and information flow, thus our network is guided to emphasize the key information and suppress the less significant information. The experimental results on 300 W and WFLW datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

RCS Simulation and ISAR Imaging of Coated Targets 5

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RCS Simulation and ISAR Imaging of Coated Targets

5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Tan, Xiaolong Fang, Ming Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781538671115

The application of radar absorbing materials on the surface of metal aircraft can effectively reduce the radar cross section (RCS). In this paper, the physical optics (PO) method is used to calculate the RCS of electrically large coated targets. First, we calculate the RCS of the original X29 aircraft and the X29 aircraft coated with a broadband two-layer radar absorbing coating. In addition, the backscattered electric field data of coated and uncoated X29 aircraft are obtained by using the PO method, respectively. Finally, the two-dimensional Inverse Fourier Transform (IFT) of the collected data is taken to form the Inverse Synthetic Aperture Radar (ISAR) image. © 2019 IEEE.

关键词： Radar cross section

Neural multi-objective combinatorial optimization with diversity enhancement 23

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Neural multi-objective combinatorial optimization with diver...

Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Jinbiao Chen Zizhen Zhang Zhiguang Cao Yaoxin Wu Yining Ma Te Ye Jiahai Wang School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

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Visual Language Model based Cross-modal Semantic Communication Systems

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arXiv 2024年

作者： Jiang, Feibo Tang, Chuanguo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years, successfully transcending the Shannon physical capacity limits through innovative semantic transmission concepts. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low semantic density, catastrophic forgetting, and uncertain signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

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arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks

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Intelligent and Converged Networks 2020年第3期1卷 281-294页

作者： Jie Mei Xianbin Wang Kan Zheng Electrical and Computer Engineering Western UniversityLondonON N6A 5B9Canada Intelligent Computing and Communication(IC2)Lab Wireless Signal Processing and Network(WSPN)LabKey Laboratory of Universal Wireless CommunicationMinistry of EducationBeijing University of Posts and Telecommunications(BUPT)Beijing 100876China

Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service(QoS)in the 5th Generation(5G)-beyond and the 6th Generation(6G)***,effective slicing of Radio Access Network(RAN)is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G *** this paper,we propose a self-sustained RAN slicing framework,which integrates the self-management of network resources with multiple granularities,the self-optimization of slicing control performance,and self-learning together to achieve an adaptive control strategy under unforeseen network *** proposed RAN slicing framework is hierarchically structured,which decomposes the RAN slicing control into three levels,i.e.,network-level slicing,next generation NodeB(gNodeB)-level slicing,and packet scheduling level *** the network level,network resources are assigned to each gNodeB at a large timescale with coarse resource *** the gNodeB-level,each gNodeB adjusts the configuration of each slice in the cell at the large *** the packet scheduling level,each gNodeB allocates radio resource allocation among users in each network slice at a small ***,we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing *** the proposed RAN slicing framework,the QoS performance of emerging services is expected to be dramatically enhanced.

关键词： Radio Access Network(RAN) network slicing network resource management intelligent network

Dual-State Personalized Knowledge Tracing with Emotional Incorporation

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arXiv 2024年

作者： Wang, Shanshan Yuan, Fangzheng Wang, keyang Yang, Xun Zhang, Xingyi Wang, Meng Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230601 China Zhejiang Dahua Technology Co. Ltd China Department of Electronic Engineering and Information Science School of information Science and Technology University of Science and Technology of China Hefei230026 China Key Laboratory of intelligent computing and Signal Processing Ministry of Education school of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model's ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: Firstly, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Secondly, we design an Emotional State Tracing Module to monitor students' personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students' response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learningbased Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Students