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Performance analysis for multi-Antenna small cell networks with clustered dynamic tdd

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arXiv 2020年

作者： Sun, Hongguang Yang, Howard H. Wang, Xijun Xu, Chao Quek, Tony Q.S. School of Information Engineering Northwest A and F University Yangling Shaanxi Information System Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou Guangdong China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Yangling Shaanxi

Small cell networks with dynamic time-division duplex (D-TDD) have emerged as a potential solution to address the asymmetric traffic demands in 5G wireless networks. By allowing the dynamic adjustment of cell-specific UL/DL configuration, D-TDD flexibly allocates percentage of subframes to UL and DL transmissions to accommodate the traffic within each cell. However, the unaligned transmissions bring in extra interference which degrades the potential gain achieved by DTDD. In this work, we propose an analytical framework to study the performance of multi-Antenna small cell networks with clustered D-TDD, where cell clustering is employed to mitigate the interference from opposite transmission direction in neighboring cells. With tools from stochastic geometry, we derive explicit expressions and tractable tight upper bounds for success probability and network throughput. The proposed analytical framework allows to quantify the effect of key system parameters, such as UL/DL configuration, cluster size, antenna number, and SINR threshold. Our results show the superiority of the clustered D-TDD over the traditional D-TDD, and reveal the fact that there exists an optimal cluster size for DL performance, while UL performance always benefits from a larger cluster. Copyright © 2020, The Authors. All rights reserved.

关键词： Cluster analysis

When to preprocess? Keeping information fresh for computing enable internet of things

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arXiv 2021年

作者： Wang, Xijun Fang, Minghao Xu, Chao Yang, Howard H. Sun, Xinghua Chen, Xiang Quek, Tony Q.S. School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai200050 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China School of Information Engineering Northwest A&F University Shaanxi Yangling China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Shaanxi Yangling China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Shaanxi Yangling China Zhejiang University/University of Illinois at Urbana-Champaign Institute Zhejiang University Haining314400 China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

Age of information (AoI), a notion that measures the information freshness, is an essential performance measure for time-critical applications in internet of things (IoT). With the surge of computing resources at the IoT devices, it is possible to preprocess the information packets that contain the status update before sending them to the destination so as to alleviate the transmission burden. However, the additional time and energy expenditure induced by computing also make the optimal updating a non-trivial problem. In this paper, we consider a time-critical IoT system, where the IoT device is capable of preprocessing the status update before the transmission. Particularly, we aim to jointly design the preprocessing and transmission so that the weighted sum of the average AoI of the destination and the energy consumption of the IoT device is minimized. Due to the heterogeneity in transmission and computation capacities, the durations of distinct actions of the IoT device are nonuniform. Therefore, we formulate the status updating problem as an infinite horizon average cost semi-Markov decision process (SMDP) and then transform it into a discrete-time Markov decision process. We demonstrate that the optimal policy is of threshold type with respect to the AoI. Equipped with this, a structure-aware relative policy iteration algorithm is proposed to obtain the optimal policy of the SMDP. Our analysis shows that preprocessing is more beneficial in regimes of high AoIs, given it can reduce the time required for updates. We further prove the switching structure of the optimal policy in a special scenario, where the status updates are transmitted over a reliable channel, and derive the optimal threshold. Finally, simulation results demonstrate the efficacy of preprocessing and show that the proposed policy outperforms two baseline policies. © 2021, CC BY.

关键词： internet of things

Intelligent Crop Planting Management and Quality Traceability system

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Intelligent Crop Planting Management and Quality Traceabilit...

第二届计算智能、工程与技术国际研讨会

作者： Xiaofen Yang Ming Sun College of Information and Electrical Engineering China Agricultural UniversityKey Laboratory of Modern Precision Agriculture System Integration ResearchMinistry of EducationKey Laboratory of Agricultural Information Acquisition TechnologyMinistry of AgricultureBeijing Engineering and Technology Research Centre for Internet of Things in Agriculture

Aiming at the farmland management problems, soil heavy metal pollutions problems and the agricultural product quality security problems, the system develops a link planting, acquisition, processing, logistics and distribution each link of intelligent crop planting management and achieves quality tracing based centralized service data gateway. This system extracts the information in real time from crop planting to agricultural product sales. It is conducive to the implementation of scientific planting and the sustainable development of agriculture, and it enables consumers to trace the quality of agricultural products. The main work of this research is as follow. The first is the construction of crop planting information management subsystem. It contains the information about field information, seeding, fertilizing, irrigation, weeding, pest controlling, spraying insecticide and harvest. The second is the construction of crop planting environmental monitoring subsystem based on sensor technology. It contains real-time environmental information in the process of crop growth, such as soil temperature and humidity, air temperatures and humidity, light intensity and video information, and it realizes remote monitoring of crops. The third is agricultural product quality traceability subsystem oriented to consumers.

关键词： Intelligent Crop Planting Management and Quality Traceability system

A Survey on Deep Learning in Crop Planting

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A Survey on Deep Learning in Crop Planting

第二届计算智能、工程与技术国际研讨会

In recent years, with the explosive growth of data, deep learning has become one of the hottest research areas in artificial intelligence. Deep learning has been widely used in many fields such as medical field, industry, transportation system, agriculture is no exception. Crop planting is a vital part of agriculture. Here, we review deep learning applications in crop planting. In addition, we discuss the challenges and future trend of deep learning in crop planting. We hope that this review could promote more researchers to apply deep learning methods in crop planting field.

关键词： A Survey on Deep Learning in Crop Planting

AoI and energy consumption oriented dynamic status updating in caching enabled IoT networks

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arXiv 2020年

作者： Xu, Chao Wang, Xijun Yang, Howard H. Sun, Hongguang Quek, Tony Q.S. School of Information Engineering Northwest A&F University Yangling Shaanxi China Key Laboratory for Agricultural Internet of Things Ministry of Agriculture and Rural Affair Yangling China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore

Caching has been regarded as a promising technique to alleviate energy consumption of sensors in internet of things (IoT) networks by responding to users’ requests with the data packets stored in the edge caching node (ECN). For real-time applications in caching enabled IoT networks, it is essential to develop dynamic status update strategies to strike a balance between the information freshness experienced by users and energy consumed by the sensor, which, however, is not well addressed. In this paper, we first depict the evolution of information freshness, in terms of age of information (AoI), at each user. Then, we formulate a dynamic status update optimization problem to minimize the expectation of a long term accumulative cost, which jointly considers the users’ AoI and sensor’s energy consumption. To solve this problem, a Markov Decision Process (MDP) is formulated to cast the status updating procedure, and a model-free reinforcement learning algorithm is proposed, with which the challenge brought by the unknown of the formulated MDP’s dynamics can be addressed. Finally, simulations are conducted to validate the convergence of our proposed algorithm and its effectiveness compared with the zero-wait baseline policy. Copyright © 2020, The Authors. All rights reserved.

关键词： internet of things

Front Cover: International Journal of Intelligent systems, Volume 37 Issue 11 November 2022

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International Journal of Intelligent systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

PISTA-SENSE-ResNet for Parallel MRI Reconstruction

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arXiv 2019年

作者： Lu, Tieyuan Zhang, Xinlin Huang, Yihui Yang, Yonggui Guo, Gang Bao, Lijun Huang, Feng Guo, Di Qu, Xiaobo Xiamen University Xiamen China Department of Radiology The Second Affiliated Hospital of Xiamen Medical College Xiamen China Neusoft Medical System Shanghai China Fujian Provincial University Key Laboratory of Internet of Things Application Technology School of Computer and Information Engineering Xiamen University of Technology Xiamen China

Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time. Although imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstruction images with a fast reconstruction speed remains a challenge. Recently, deep learning approaches have attracted a lot of attention for its encouraging reconstruction results but without a proper interpretability. In this letter, to enable high-quality image reconstruction for the parallel magnetic resonance imaging, we design the network structure from the perspective of sparse iterative reconstruction and enhance it with the residual structure. The experimental results of a public knee dataset show that compared with the optimization-based method and the latest deep learning parallel imaging methods, the proposed network has less error in reconstruction and is more stable under different acceleration factors. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

A convergence proof of projected fast iterative soft-thresholding algorithm for parallel magnetic resonance imaging

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arXiv 2019年

作者： Zhang, Xinlin Lu, Hengfa Guo, Di Bao, Lijun Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Neusoft Medical System Shanghai200241 China

The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the prolonged data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. Algorithms for solving the sparse reconstruction models play an essential role in sparse reconstruction. Being a simple and efficient algorithm for sparse reconstruction, pFISTA has been successfully extended to parallel imaging, however, its convergence criterion is still an open question, confusing users on the setting of the parameter which assures the convergence of the algorithm. In this work, we prove the convergence of the parallel imaging version pFISTA. Specifically, the convergences of two well-known parallel imaging reconstruction models, SENSE and SPIRiT, solved by pFISTA are proved. Experiments on brain images demonstrate the validity of the convergence criterion. The convergence criterion proofed in this work can help users quickly obtain the satisfy parameter that admits faithful results and fast convergence speeds. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI

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arXiv 2019年

作者： Zhang, Xinlin Guo, Di Huang, Yiman Chen, Ying Wang, Liansheng Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen361005 China Neusoft Medical System Shanghai200241 China

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating scans. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods exhibit superiority in providing a high-quality image. However, none of them employ the routinely acquired calibration data for improving image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT was proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem was then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging