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51st IEEE Annual Asilomar Conference on Signals, Systems, and Computers

作者： Chen, Mingzhe Saad, Walid Yin, Changchuan Debbah, Merouane Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China Virginia Tech Bradley Dept Elect & Comp Engn Wireless VT Blacksburg VA 24061 USA Huawei France R&D Math & Algorithm Sci Lab F-92100 Paris France

Providing seamless connectivity for wireless virtual reality (VR) users has emerged as a key challenge for future cloud-enabled cellular networks. In this paper, the problem of wireless VR resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the end-users using downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, so as to better manage uplink and downlink traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both the uplink and downlink. In the downlink, the cloud can transmit 360 degrees contents or specific visible contents (e.g., user field of view) that are extracted from the original 360 degrees contents to the users according to the users' data correlation so as to reduce the backhaul traffic load. In the uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlation-aware resource management problem is formulated as an optimization problem whose goal is to maximize the users' successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, a machine learning algorithm that uses echo state networks (ESNs) with transfer learning is introduced. By smartly transferring information on the SBS's utility, the proposed transfer-based ESN algorithm can quickly cope with changes in the wireless networking environment

关键词： Virtual reality resource allocation echo state networks transfer learning

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Low-Complexity Channel Estimation in OFDM MU-MIMO Next Generation Cellular Networks 29

Low-Complexity Channel Estimation in OFDM MU-MIMO Next Gener...

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29th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Martalo, Marco Opinto, Alessandro Maso, Marco Debbah, Merouane Raheli, Riccardo Univ Parma Dept Engn & Architecture Parma Italy Nokia Bell Labs Nozay France Huawei Paris Res Ctr Math & Algorithm Sci Lab Paris France

ISBN: (纸本)9781538660096

We consider downlink communications between a Base Station (BS) and various mobile stations, equipped with multiple antennas, based on Orthogonal Frequency Division Multiplexing (OFDM). Transmission is compliant with the Long Term Evolution (LTE) standard operating in Frequency Division Duplex (FDD) mode. Since ideal feedback of channel state information to the BS may be cumbersome, we consider two suboptimal channel estimation algorithms, denoted as Resource Block (RB) and Resource Block Group (RBG). Both approaches approximate the channel as constant over multiples of the fundamental LTE block, known as Physical Resource Block (PRB). Our results show that RB and RBG incur a limited performance loss, yet guaranteeing significant saving in the amount of feedback information.

关键词： Orthogonal frequency division multiplexing

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Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

arXiv

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arXiv 2022年

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Switzerland AI Witchlabs Switzerland University of Wuerzburg Germany MediaTek Inc. Taiwan National Yang Ming Chiao Tung University Taiwan Video Algorithm Group Camera Department Xiaomi Inc. China Audio & Video Technology Platform Department ZTE Corp. China China School of Computer Science and Technology Xi'an Jiaotong University China MIGU Video Co. Ltd China BOE Technology Group Co. Ltd. China GenGenAI Korea Republic of North China University of Technology China Institute of Automation Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Sweden Northeastern University China

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of lowbitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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Echo State Transfer Learning for Data Correlation Aware Resource Allocation in Wireless Virtual Reality 51

Echo State Transfer Learning for Data Correlation Aware Reso...

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51st IEEE Asilomar Conference on Signals Systems and Computers

作者： Chen, Mingzhe Saad, Walid Yin, Changchuan Debbah, Merouane Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China Virginia Tech Bradley Dept Elect & Comp Engn Wireless VT Blacksburg VA USA Huawei France R&D Math & Algorithm Sci Lab Paris France

ISBN: (纸本)9781538618233

In this paper, the problem of data correlation-aware resource management is studied for a network of wireless virtual reality (VR) users communicating over cloud-based small cell networks (SCNs). In the studied model, small base stations (SBSs) with limited computational resources act as VR control centers that collect the tracking information from VR users over the cellular uplink and send them to the VR users over the downlink. In such a setting, VR users may send or request correlated or similar data (panoramic images and tracking data). This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both uplink and downlink. This VR resource allocation problem is formulated as a noncooperative game that allows jointly optimizing the computational and spectrum resources, while being cognizant of the data correlation. To solve this game, a transfer learning algorithm based on the machine learning framework of echo state networks (ESNs) is proposed. Unlike conventional reinforcement learning algorithms that must be executed each time the environment changes, the proposed algorithm can intelligently transfer information on the learned utility, across time, to rapidly adapt to environmental dynamics due to factors such as changes in the users' content or data correlation. Simulation results show that the proposed algorithm achieves up to 16.7% and 18.2% gains in terms of delay compared to the Q-learning with data correlation and Q-learning without data correlation. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays.

关键词： Virtual reality

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Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2017年第6期16卷 3520-3535页

作者： Chen, Mingzhe Saad, Walid Yin, Changchuan Debbah, Merouane Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Network Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China Virginia Polytech Inst & State Univ Elect & Comp Engn Dept Wireless VT Blacksburg VA 24061 USA Univ Paris Saclay Cent Supelec Large Networks & Syst Grp F-91192 Gif Sur Yvette France Huawei France Res & Dev Ctr Math & Algorithm Sci Lab F-91192 Paris France

In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user and determine which content to cache at remote radio heads and the BBUs. This problem is formulated as an optimization problem, which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks (ESNs) with sublinear algorithms is proposed. Using ESNs, the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to capture the maximum amount of user information needed for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared with two baseline algorithms: random caching with clustering and random caching without clustering.

关键词： CRAN mobility caching echo state networks machine learning

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Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2017年第5期35卷 1046-1061页

作者： Chen, Mingzhe Mozaffari, Mohammad Saad, Walid Yin, Changchuan Debbah, Merouane Hong, Choong Seon Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Network Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China Virginia Polytech Inst & State Univ Elect & Comp Engn Dept Wireless VT Blacksburg VA 24061 USA Kyung Hee Univ Dept Comp Sci & Engn Yongin 02447 South Korea Univ Paris Saclay Cent Supelec Large Networks & Syst Grp F-91192 Gif Sur Yvette France Huawei France Res & Dev Math & Algorithm Sci Lab Paris France

In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs.

关键词： CRAN UAV unmanned aerial vehciles drones QoE caching machine learning conceptor-based echo state networks

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Real-time multiple objects tracking on Raspberry-Pi-based smart embedded camera

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JOURNAL OF ELECTRONIC IMAGING 2016年第4期25卷

作者： Dziri, Aziz Duranton, Marc Chapuis, Roland CEA LIST Algorithm & Architecture Codesign Lab F-91191 Gif Sur Yvette France Univ Blaise Pascal Inst PASCAL 24 Ave Landais F-63171 Aubiere France

Multiple-object tracking constitutes a major step in several computer vision applications, such as surveillance, advanced driver assistance systems, and automatic traffic monitoring. Because of the number of cameras used to cover a large area, these applications are constrained by the cost of each node, the power consumption, the robustness of the tracking, the processing time, and the ease of deployment of the system. To meet these challenges, the use of low-power and low-cost embedded vision platforms to achieve reliable tracking becomes essential in networks of cameras. We propose a tracking pipeline that is designed for fixed smart cameras and which can handle occlusions between objects. We show that the proposed pipeline reaches real-time processing on a low-cost embedded smart camera composed of a Raspberry-Pi board and a RaspiCam camera. The tracking quality and the processing speed obtained with the proposed pipeline are evaluated on publicly available datasets and compared to the state-of-the-art methods. (C) 2016 SPIE and IS&T.

关键词： multiobject tracking occlusion handling smart cameras Raspberry-Pi background subtraction Gaussian mixture probability hypothesis density

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Specific Read-only Data Management for Memory System Optimization 24

Specific Read-only Data Management for Memory System Optimiz...

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24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)

作者： Vaumourin, Gregory Guerre, Alexandre Dombek, Thomas Barthou, Denis CEA LIST Algorithm & Architecture Codesign Lab F-91191 Gif Sur Yvette France Bordeaux Inst Technol LaBRI INRIA Bordeaux Sud Ouest Bordeaux France

ISBN: (纸本)9781467387767

This paper proposes a new way of managing the cache by exploiting the difference of behavior in the memory system between read-only data and read-write data. A division of the existing cache-based memory hierarchy is proposed in order to create a dedicated data path for read-only data. In order to justify this approach, an analysis performed on a set of benchmarks shows that read-only data count for significant part of the working set and are less reused than read-write data. A transparent solution is proposed based on specific compilation support to separate automatically the memory accesses of read-only data at L1-level. This organization exploits the properties of the different sub-workloads in order to increase the overall data locality and data reuse. Simulated in a multicore environment, the evaluation of the new memory organization shows reduction of L1 misses up to 28.5%. Moreover, the messages issued on the interconnection network can be reduced up to 14.7% without any penalty on the performance.

关键词： caches memory read-only data

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SNet. a novel network for manycore architectures 3

SNet. a novel network for manycore architectures

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3rd International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA)

作者： Azar, Celine Chevobbe, Stephane Lhuillier, Yves Diguet, Jean-Philippe Lebanese Univ Fac Sci Beirut Lebanon CEA LIST Algorithm & Architecture Codesign Lab Gif Sur Yvette France CEA LIST Software Secur Lab Gif Sur Yvette France Univ Bretagne Sud LABSTICC Lorient France

ISBN: (纸本)9781467369428

Embedded platforms are expected to integrate thousands of cores in the near future, and one of the major difficulties remains in scaling the interconnection network. We propose a novel Scalable NETwork concept, named SNet, that broadens the NoCs space to include a combined software/hardware dynamic routing methodology. It exploits a distributed, adaptive, non-supervised routing strategy [1], based on the ACO algorithm ( Ant Colony Optimization), to define routing paths among communicating tasks. Data transmission is achieved by a small footprint hardware module, named DMC ( Direct Management of Communications). SNet has the advantage of being highly flexible, and enables to define a wide range of routing topologies to match application requirements. In this paper, we apply the SNet paradigm to a manycore architecture and we evaluate its performances by running a high number of test cases.

关键词： Ant colony optimization

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Assessment of Urban Fabric for Smart Cities

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IEEE ACCESS 2016年 4卷 373-382页

作者： Li, Xin Lv, Zhihan Hijazi, Ihab Hamzi Jiao, Hongzan Li, Li Li, Kuncheng Wuhan Univ Sch Urban Design Wuhan 430072 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing 100864 Peoples R China An Najah Natl Univ Urban Planning Engn Dept Nablus 7 Palestine Southeast Univ Sch Architecture Lab Architectural Algorithm & Applicat Nanjing 210018 Jiangsu Peoples R China

Comprehensive understandings to the built environment especially the urban form is a prerequisite for building a smart city. This paper is based on computational methods to examine the urban fabric of Hankou, China, as a case study. Quantitative and comparative analyses are involved to understand the characteristics on the block scale, where massive demolition and new construction coincide with the existing historical context. Five urban fabric indicators are defined (i.e., density, compactness, fragmentation, variance, and cohesion) to conduct a case study for the 83 selected blocks using geographic information system tools and statistical analysis. Distribution patterns and correlations between these indicators are analyzed. Comparisons are made between typical blocks with high, median, and low fabric densities using comprehensive fabric indicators. The research results indicate that the organic order of the original urban fabric is facing damage, especially from arbitrary demolition, overinfilling, and spontaneous encroachment. Finally, this paper discusses how retaining the urban fabric makes the city a vibrant place to live. The sustainable development of a city should attach great importance to the protection and continuation of local characteristics of integrity and authenticity. The described analytical methods could contribute to the optimization of urban design strategies for future smart cities.

关键词： Smart cities GIS urban fabric Hankou hybridity Chinese urbanization

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