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Power Allocation for Energy Efficiency Optimization in Multi-User mmWave-NOMA System With Hybrid Precoding

ieee ACCESS 2019年 7卷 109083-109093页

作者： Yu, Xiangbin Xu, Fangcheng Yu, Kai Dang, Xiaoyu Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Chinese Acad Sci Key Lab Wireless Sensor Network & Commun Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

Recently, the non-orthogonal multiple access (NOMA) scheme has been applied in millimeter-wave (mmWave) communication system to support more users and further improve the performance. In this paper, an energy-efficient power allocation (PA) scheme is designed for a downlink multi-user mmWave-NOMA system with hybrid precoding (HP), where two typical HP architectures, namely the fully-connected HP architecture (FHPA) and the sub-connected HP architecture (SHPA), are both considered. Firstly, we pair users in term of their channel difference and correlation. Then, analog beamforming schemes are proposed for the system with fully-connected and sub-connected HP architectures, respectively. Based on this, a two-step HP design with the proposed analog beamforming and zero-forcing precoding is presented. With these results, the optimization problem is formulated to maximize the energy efficiency (EE) of the system. This is a non-convex optimization problem, and can be approximately decomposed into independent convex subproblems by applying the fractional programming theory. Using the coordinate descent method, the closed-form solutions of each sub-problem are derived. On these basis, an effective iterative algorithm is proposed to obtain the suboptimal power allocation. Simulation results verify the effectiveness of the proposed PA scheme, it has the same EE performance as the existing scheme with relatively low complexity, and can obtain the EE close to the exhaustive search scheme as well as the chaotic accelerated particle swarm optimization scheme. Moreover, the proposed analog beamforming obviously outperforms the conventional finite resolution analog beamforming, and the system with SHPA has higher EE than that with FHPA.

关键词： Analog beamforming energy efficiency hybrid precoding millimeter-wave communications multi-user pairing non-orthogonal multiple access power allocation

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Flexible network Interface (FNI): A Mission-centric Integration Framework for Next Generation DoD SATCOM networks

Flexible Network Interface (FNI): A Mission-centric Integrat...

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MILCOM, Military Communications conference

作者： Joseph Vanderpoorten James Lyke David Brunnenmeyer Kevin Zhang Don Fronterhouse Joseph Kidder Jeffrey Laing USSF Space Systems Command Air Force Research Labs LinQuest Corporation Supinf Technologies LLC PnP Innovations Inc 5th Layer LLC

Our warfighters rely on flexible, resilient, high performance, and global DoD SATCOM networks to operate effectively in a contested, degraded and operationally-limited (CDO) environment. Today, a multitude of DoD networks exist as stovepipes with disparate networking architectures, cybersecurity requirements, management and control tools. We believe that Internet Protocol convergence becomes insufficient to achieve interoperability in the military mission networking context. As the future battlespace involves multiple domain capabilities and assets that must be assembled within mission relevant timelines, operate in unison, and be adaptive to battlespace, a new model is needed to deliver high performance multi-mission and multi-domain data transport. The Flexible network Interface (FNI) is a robust integration framework that deploys enabling technologies to operationalize heterogeneous networks (DoD and commercial). It delivers consistent end user experience, architecture resiliency, and optimized performance; leveraging heterogeneous host networks and commercial managed services across space, air, ground, and sea. This paper provides an overview of the USSF SSC FNI concept, which is being matured through a series of capability prototyping and demonstration initiatives. We describe the FNI framework's design considerations, and its associated architecture instantiations, key functional elements and interfaces. Several important technology requirements are highlighted, which motivate targeted research and development. Finally, the paper summarizes future work to address open issues across technology, standards, implementations, and deployment.

关键词： Military communication Protocols Requirements management User experience US Department of Defense network interfaces Standards

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Modeling and Identification of an Industrial Hot Water Boiler 43

Modeling and Identification of an Industrial Hot Water Boile...

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43rd International conference on Telecommunications and Signal Processing (TSP)

作者： Tarasevich, Maksimilian Tepljakov, Aleksei Petlenkov, Eduard Vansovits, Vitali Tallinn Univ Technol Dept Comp Syst Tallinn Estonia

ISBN: (纸本)9781728163765

One of the most important open issues in industrial automation is a nonlinear dynamical system identification. In the following paper, the grey and black box approaches are implemented for hot water boiler modeling and identification. First-principles approach is applied to the hot water boiler modeling, and symbolic regression is performed for identification of the same industrial process. Then, the implementation of both methods is described. The resulting models are validated and compared on the basis of performance metrics. Finally, the encountered problems in the realization of the methods are reviewed.

关键词： genetic programming hot water boiler industrial process nonlinear model identification symbolic regression

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Neural network architectures for Electricity Consumption Forecasting 12

Neural Network Architectures for Electricity Consumption For...

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ieee Int Congr on Cybermat / 12th ieee Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th ieee Int Conf on Green Computing and Communications (GreenCom) / 12th ieee Int Conf on Internet of Things (iThings) / 5th ieee Int Conf on Smart Data

作者： Heghedus, Cristina Segarra, Santiago Chakravorty, Antorweep Rong, Chunming Univ Stavanger Dept Comp Sci & Elect Engn Stavanger Norway Rice Univ Dept Elect & Comp Engn POB 1892 Houston TX 77251 USA

ISBN: (纸本)9781728129808

Advancing Data Science has become a central goal in the era of Big Data, Cloud Computing, and the Internet of Things. Data science includes a multitude of methods, algorithms, and systems that are used to extract hidden patterns and knowledge from data. This paper is centered around machine learning (ML) models, and their implementation and applicability in the field of the energy informatics. Four neural networks (NNs) are proposed for computational experiments on electricity consumption forecasting. The NNs considered are back propagation neural network (BPNN), fully recurrent neural network (fRNN), long short-term memory network (LSTM), and gated recurrent unit (GRU). The four models are implemented using PyTorch, an open-source ML framework. By comparing their predictive performance, we find that LSTM and GRU attain lower errors than the other two models. More precisely, GRU presents better performance for the daily forecast whereas LSTM outperforms the rest for the monthly forecast. Insights about PyTorch are also provided, as it represents a recent addition to state of the art technologies, in terms of implementation.

关键词： PyTorch TensorFlow Parallel training GPU programming Electricity consumption

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Memory Performance and Bottlenecks in Multicore and GPU architectures 27

Memory Performance and Bottlenecks in Multicore and GPU Arch...

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27th Euromicro International conference on Parallel, Distributed and network-Based Processing (PDP)

作者： Serpa, Matheus S. Moreira, Francis B. Navaux, Philippe O. A. Cruz, Eduardo H. M. Diener, Matthias Griebler, Dalvan Fernandes, Luiz Gustavo Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Fed Inst Parana IFPR Paranavai Brazil Univ Illinois Urbana IL USA Pontifical Catholic Univ Rio Grande Sul PUCRS Sch Technol Porto Alegre RS Brazil

ISBN: (纸本)9781728116440

Nowadays, there are several different architectures available not only for the industry, but also for normal consumers. Traditional multicore processors, GPUs, accelerators such as the Sunway SW26010, or even energy efficiency-driven processors such as the ARM family, present very different architectural characteristics. This wide range of characteristics presents a challenge for the developers of applications. Developers must deal with different instruction sets, memory hierarchies, or even different programming paradigms when programming for these architectures. Therefore, the same application can perform well when executing on one architecture, but poorly on another architecture. To optimize an application, it is important to have a deep understanding of how it behaves on different architectures. The related work in this area mostly focuses on a limited analysis encompassing execution time and energy. In this paper, we perform a detailed investigation on the impact of the memory subsystem of different architectures, which is one of the most important aspects to be considered. For this study, we performed experiments in the Broadwell CPU and Pascal GPU, using applications from the Rodinia benchmark suite. In this way, we were able to understand why an application performs well on one architecture and poorly on others.

关键词： Performance evaluation Manycore systems Memory subsystem Cache memory HPC

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API-Misuse Detection Driven by Fine-Grained API-Constraint Knowledge Graph 35

API-Misuse Detection Driven by Fine-Grained API-Constraint K...

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35th ieee/ACM International conference on Automated Software Engineering (ASE)

作者： Ren, Xiaoxue Ye, Xinyuan Xing, Zhenchang Xia, Xin Xu, Xiwei Zhu, Liming Sun, Jianling Zhejiang Univ Hangzhou Peoples R China Australian Natl Univ Canberra ACT Australia Monash Univ Clayton Vic Australia CSIRO Data61 Canberra ACT Australia Ningbo Res Inst Ningbo Peoples R China PengCheng Lab Shenzhen Peoples R China Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9781450367684

API misuses cause significant problem in software development. Existing methods detect API misuses against frequent API usage patterns mined from codebase. They make a naive assumption that API usage that deviates from the most-frequent API usage is a misuse. However, there is a big knowledge gap between API usage patterns and API usage caveats in terms of comprehensiveness, explainability and best practices. In this work, we propose a novel approach that detects API misuses directly against the API caveat knowledge, rather than API usage patterns. We develop open information extraction methods to construct a novel APIconstraint knowledge graph from API reference documentation. This knowledge graph explicitly models two types ofAPI-constraint relations (call-order and condition-checking) and enriches return and throw relations with return conditions and exception triggers. It empowers the detection of three types offrequent API misuses missing calls, missing condition checking and missing exception handling, while existing detectors mostly focus on only missing calls. As a proof-of-concept, we apply our approach to Java SDK API Specification. Our evaluation confirms the high accuracy of the extracted API-constraint relations. Our knowledge-driven API misuse detector achieves 0.60 (68/113) precision and 0.28 (68/239) recall for detecting Java API misuses in the API misuse benchmark MuBench. This performance is significantly higher than that of existing pattern-based API misused detectors. A pilot user study with 12 developers shows that our knowledge-driven API misuse detection is very promising in helping developers avoid API misuses and debug the bugs caused by API misuses.

关键词： Application programming interfaces (API)

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Pvalite CLN: Lightweight Object Detection with Classfication and Localization network 32

Pvalite CLN: Lightweight Object Detection with Classfication...

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32nd ieee International System-on-Chip conference (ieee SOCC)

作者： Jiun-In, Guo Chi-Chi, Tsai Ching-Kan, Tseng

ISBN: (纸本)9781728134826

We developed a 2D convolution object detection network called CLN layer to let a network with a small amount of parameters and have a better detection result as welL Compared to YOLOv2 detector and SSD with lightweight Pvalite network, the proposed Classification and Localization network (CLN) layer has better accuracy and higher recall on the object which is located on the border of image. Additionally, we revise the original open source to make this architecture more platform portable than other architectures. The proposed system is implemented on the embedded platforms with the performance of 640x480@10 fps under nVidia Jetson TX-2 and 480x320@5fps under Renesas R-car H3.

关键词： Object Detection Classification Localization

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AUTOMATIC NEURAL network SEARCH METHOD FOR open SET RECOGNITION 26

AUTOMATIC NEURAL NETWORK SEARCH METHOD FOR OPEN SET RECOGNIT...

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26th ieee International conference on Image Processing (ICIP)

作者： Sun, Li Yu, Xiaoyi Wang, Liuan Sun, Jun Inakoshi, Hiroya Kobayashi, Ken Kobashi, Hiromichi Fujitsu R&D Ctr Beijing Peoples R China Fujitsu Labs Ltd Kawasaki Kanagawa Japan

ISBN: (纸本)9781538662496

Real-world recognition or classification tasks in computer vision are not apparent in controlled environments and often get involved in open set. Previous research work on real-world recognition problem is knowledge- and labor-intensive to pursue good performance for there are numbers of task domains. Auto Machine Learning (AutoML) approaches supply an easier way to apply advanced machine learning technologies, reduce the demand for experienced human experts and improve classification performance on close set. This paper proposes an automatic neural network search method for designing effective convolution neural network (CNN) models for open set recognition (OSR). Feature distribution information is explicitly incorporated into the main objective. So during the search process, the sampled models will enlarge interclass differences and reduce intra-class variations. We design a flexible search space based on classic CNN models to diversify neural architectures and also add some search principles to limit the size of the search space. Experimental results on CIFAR-10 and Dunhuang historical Chinese datasets show that our approach improves performances on both close and open set. Comparing with the other two OSR algorithms, our method also achieves the best performance.

关键词： Neural network search open set search space feature distribution center loss

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AsymNVM: An Efficient Framework for Implementing Persistent Data Structures on Asymmetric NVM Architecture 20

AsymNVM: An Efficient Framework for Implementing Persistent ...

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25th International conference on Architectural Support for programming Languages and Operating Systems (ASPLOS)

作者： Ma, Teng Zhang, Mingxing Chen, Kang Song, Zhuo Wu, Yongwei Qian, Xuehai Tsinghua Univ Beijing Peoples R China Tsinghua Univ Shenzhen Peoples R China Sangfor Shenzhen Peoples R China Alibaba Beijing Peoples R China Univ Southern Calif Los Angeles CA USA

ISBN: (纸本)9781450371025

The byte-addressable non-volatile memory (NVM) is a promising technology since it simultaneously provides DRAM-like performance, disk-like capacity, and persistency. The current NVM deployment with byte-addressability is symmetric, where NVM devices are directly attached to servers. Due to the higher density, NVM provides much larger capacity and should be shared among servers. Unfortunately, in the symmetric setting, the availability of NVM devices is affected by the specific machine it is attached to. High availability can be achieved by replicating data to NVM on a remote machine. However, it requires full replication of data structure in local memory - limiting the size of the working set. This paper rethinks NVM deployment and makes a case for the asymmetric byte-addressable non-volatile memory architecture, which decouples servers from persistent data storage. In the proposed AsymNVM architecture, NVM devices (i.e., back-end nodes) can be shared by multiple servers (i.e., front-end nodes) and provide recoverable persistent data structures. The asymmetric architecture, which follows the industry trend of resource disaggregation, is made possible due to the high-performance network (e.g., RDMA). At the same time, AsymNVM leads to a number of key problems such as, still relatively long network latency, persistency bot-tleneck, and simple interface of the back-end NVM nodes. We build AsymNVM framework based on AsymNVM architecture that implements: 1) high performance persistent data structure update;2) NVM data management;3) concurrency control;and 4) crash-consistency and replication. The key idea to remove persistency bottleneck is the use of operation log that reduces stall time due to RDMA writes and enables efficient batching and caching in front-end nodes. To evaluate performance, we construct eight widely used data structures and two transaction applications based on AsymNVM framework. In a 10-node cluster equipped with real NVM devices, results show that A

关键词： memory architectures persistent memory RDMA

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Estimation of Sound Source Direction of Arrival Map Using Convolutional Neural network and Cross-Correlation in Frequency Bands 6

Estimation of Sound Source Direction of Arrival Map Using Co...

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open conference of Electrical, Electronic and Information Sciences (eStream)

作者： Sakavicius, Saulius Serackis, Arturas Vilnius Gediminas Tech Univ Dept Elect Syst Vilnius Lithuania

ISBN: (纸本)9781728124995

In this paper, we present an evaluation of the usage of a convolutional neural network (CNN) for the estimation of the sound source direction of arrival (DoA) map. Cross-correlations in different frequency bands, calculated for pairs of microphones were used as input features. We propose a technique for generating data for the CNN training, a means of presenting the direction of arrival information for an arbitrary number of sound sources and a viable CNN architecture. In our proposed approach for sound DoA estimation, there is no need for prior knowledge of the number of the active sound sources nor the properties of their signals. We present the results of the evaluation of the two distinct CNN architectures.

关键词： Sound source localization Direction of arrival map Convolutional Neural networks

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