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arXiv 2019年

作者： Jiang, Yanxiang Huang, Wenlong Bennis, Mehdi Zheng, Fu-Chun National Mobile Communications Research Laboratory Southeast University Nanjing210096 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai200050 China Centre for Wireless Communications University of Oulu Oulu90014 Finland School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen518055 China

In this paper, we investigate the problem of asynchronous coded caching in fog radio access networks (F-RANs). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents for asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The closed-form expression of the fronthaul load is established for the special case where the number of requests during each time slot is fixed, and the upper and lower bounds of the fronthaul load are given for the general case where the number of requests during each time slot is random. The simulation results show that our proposed scheme can create considerable coded-multicasting opportunities in asynchronous request scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Multicasting

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Analysis of Delay and Energy Efficiency in Fog Radio Access networks with Hybrid Caching

Analysis of Delay and Energy Efficiency in Fog Radio Access ...

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IEEE Globecom Workshops

作者： Chaoyi Wan Yanxiang Jiang Fu-Chun Zheng Pengcheng Zhu Xiqi Gao Xiaohu You National Mobile Communications Research Laboratory Southeast University Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

In this paper, delay and energy efficiency (EE) are investigated in three-tier fog radio access networks (F-RANs) with hybrid caching. By using tools from stochastic geometry, we firstly derive tractable expressions of delay for coded cached, non-partitioned cached and uncached files. Then, we derive tractable expressions of EE by jointly considering power consumed in circuits, transmissions and backhaul links. To balance delay and EE, the corresponding multi-objective optimization problem is formulated to obtain the optimal hybrid caching strategy. Numerical results show the flexibility of the proposed optimal hybrid caching strategy in delay-sensitive and EE-sensitive scenarios, and that fog access point (F-AP) density has a more significant impact on delay-sensitive scenarios than EE-sensitive scenarios.

关键词： Delays Servers Rayleigh channels Radio access networks Geometry Optimization

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Cooperative Edge Caching in Fog Radio Access networks: A Pigeon Inspired Optimization Approach

Cooperative Edge Caching in Fog Radio Access Networks: A Pig...

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IEEE Global communications Conference

作者： Chengyu Xia Yanxiang Jiang Mugen Peng Fu-Chun Zheng Mehdi Bennis Xiaohu You National Mobile Communications Research Laboratory Southeast University Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China Centre for Wireless Communications University of Oulu Oulu Finland

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated to minimize the average download delay. Considering the non-linear and coupled multi-variable nature of the original optimizing problem, we transform it into an equivalent integer linear programming problem with decoupled variables. Then, we decomposed the transformed problem into two subproblems which can be solved separately by each fog access point (F-AP). Considering the non-deterministic polynomial hard (NP-hard) nature of the two decomposed subproblems, we propose an improved pigeon inspired optimization (PIO) based cooperative edge caching scheme, which utilizes Cauchy perturbation and self-adaptive factor to avoid pre-mature convergence and achieve a better search performance, respectively. Our proposed scheme not only allows F-APs to make cache decisions with low computational complexity, but also has very low message passing overhead. Simulation results show that our proposed scheme can greatly decrease the average download delay.

关键词： Delays Integer linear programming Radio access networks Optimization Servers wireless networks Transforms

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Distributed edge caching via reinforcement learning in fog radio access networks

arXiv

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arXiv 2019年

作者： Lu, Liuyang Jiang, Yanxiang Bennis, Mehdi Ding, Zhiguo Zheng, Fu-Chun You, Xiaohu National Mobile Communications Research Laboratory Southeast University Nanjing210096 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Centre for Wireless Communications University of Oulu Oulu90014 Finland School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen518055 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F-RANs. Then, the Q-learning method based on the reinforcement learning (RL) framework is put forth to seek the optimal caching policy in a distributed manner, which enables fog access points (F-APs) to learn and track the potential dynamic process without extra communications cost. Furthermore, we propose a more efficient Q-learning method with value function approximation (Q-VFA-learning) to reduce complexity and accelerate convergence. Simulation results show that the performance of our proposed method is superior to those of the traditional methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Economy-mode Framework for Task Offloading in Fog Computing networks

An Economy-mode Framework for Task Offloading in Fog Computi...

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International Conference on wireless communications and Signal Processing (WCSP)

作者： Beibei Wang Fei Shen Xujie Li Fei Qin Feng Yan Siyuan Zhou College of computer and information Hohai University Nanjing China Shanghai Research Center for Wireless Communications Shanghai China Key Lab for Wireless Sensor network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China The School of Electronic and Electrical Communication Engineering University of Chinese Academy of Sciences Beijing China National Mobile Communications Research Laboratory Southeast University Nanjing China

In the fog network, by offtoading the computing tasks to the fog nodes (FNs) located at the edge of the network, the task processing delay of the terminal nodes (TNs) can be reduced significantly. However, not all the FNs are willing to help TNs for free. Therefore, an economy-mode framework for task offtoading in the fog network is required to motivate the FNs. In this paper, we formulate the task offtoading problem based on Stackelberg game. The TN with computing tasks acts as the leader and its strategy is the optimal unit computing price. Each FN with idle resources acts as the follower and its strategy is the sharable optimal proportion of computing capability. Firstly, the leader predicts the followers' strategies and provides the unit computing price. Next, given the price, the followers will do exactly what the leader wishes, which is the system optimal point. By maximizing the utility functions of both the TN and FNs, a win-win solution is obtained. The numerical simulations indicate the importance of our proposed economy-mode framework for task offtoading.

关键词： Task analysis Delays Games Energy consumption Edge computing Computational modeling wireless communication

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On the secrecy performance of Random VLC networks with imperfect CSI and protected zone

arXiv

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arXiv 2019年

作者： Wang, Jin-Yuan Qiu, Yu Lin, Sheng-Hong Wang, Jun-Bo Lin, Min Liu, Cheng Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing210003 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Vertical Industry Development Department Baidu China Co. Ltd. Shanghai201210 China

This paper investigates the physical-layer security for a random indoor visible light communication (VLC) network with imperfect channel state information (CSI) and a protected zone. The VLC network consists of three nodes, i.e., a transmitter (Alice), a legitimate receiver (Bob), and an eavesdropper (Eve). Alice is fixed in the center of the ceiling, and the emitted signal at Alice satisfies the non-negativity and the dimmable average optical intensity constraint. Bob and Eve are randomly deployed on the receiver plane. By employing the protected zone and considering the imperfect CSI, the stochastic characteristics of the channel gains for both the main and the eavesdropping channels is first analyzed. After that, the closed-form expressions of the average secrecy capacity and the lower bound of secrecy outage probability are derived, respectively. Finally, Monte-Carlo simulations are provided to verify the accuracy of the derived theoretical expressions. Moreover, the impacts of the nominal optical intensity, the dimming target, the protected zone and the imperfect CSI on secrecy performance are discussed, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Visible light communication

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引用

IEEE Global communications Conference

作者： Haojie Feng Yanxiang Jiang Dusit Niyato Fu-Chun Zheng Xiaohu You National Mobile Communications Research Laboratory Southeast University Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Computer Engineering Nanyang Technological University (NTU) Singapore School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) is investigated. In order to make the popularity prediction accurate and adaptive, we propose to learn the corresponding popularity prediction model for every content class from the preprocessed popularity series by training a simplified bidirectional long short-term memory (Bi-LSTM) network, and further use it to help build a content classifier in terms of content popularity trend in the training phase. Then, content popularity can be predicted by the right prediction model with respect to the corresponding content class in the predicting phase. Considering that it is hard to collect enough data about numerous content features through F-APs, we propose to only use the number of requests. Our proposed content popularity prediction policy offers a high prediction accuracy with low computational complexity by transferring the high complexity tasks from the predicting phase to the training phase. Simulation results show that the cache hit rate of our proposed policy approaches the optimal performance.

关键词： Predictive models Training Market research Computational modeling Machine learning Radio access networks Adaptation models

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A Deep Learning Framework with Spatial-Temporal Attention Mechanism for Cellular Traffic Prediction

A Deep Learning Framework with Spatial-Temporal Attention Me...

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IEEE Globecom Workshops

作者： Yun Gao Xin Wei Liang Zhou Haibing Lv College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center for Communication and Information Technology (NUPT) Nanjing China Key Laboratory of Broadband Wireless Communications and Sensor Network Technology Ministry of Education (NUPT) Nanjing China Wireline Product Planning Dept Wireline Product Operation ZTE Corporation Nanjing China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Currently, traffic prediction in cellular communication has become an important way to relieve traffic congestion, and then guarantee users' quality of experience (QoE) in multimedia service. However, when concerning traffic prediction in the context of big data, traditional deep learning models only have limited memory lengths and thus do not satisfy high prediction accuracy demand. In this paper, we design a new deep learningbased framework, which is built on our proposed novel spatialtemporal attention mechanism, to further increase the prediction accuracy of cellular traffic resource. Specifically, we firstly design a big data hierarchical architecture with four levels to extract meaningful information. Then, considering that cellular traffic resource from base stations characterizes spatial-temporal dependencies, we integrate conventional temporal attention mechanism for target area with that from spatial neighbor areas, proposing a novel spatial-temporal attention mechanism. Finally, we utilize this spatial-temporal attention mechanism based LSTM (STaLSTMs) to predict cellular traffic resource from base stations. Experimental results demonstrate that our proposed framework has better performances in cellular traffic prediction than other competing models. Importantly, in some application scenarios, this framework can also maintain high accuracy but only with 60% data.

关键词： Big Data Machine learning Data models Predictive models Base stations Quality of experience Time series analysis

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network Selection Algorithm Based on Group Decision Making for Heterogeneous wireless networks 9

Network Selection Algorithm Based on Group Decision Making f...

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2017 9th IEEE International Conference on Communication Software and networks (ICCSN 2017)

作者： Ran Luo Su Zhao Qi Zhu College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications National Mobile Communications Research Laboratory Southeast University Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications Key Lab on Wideband Wireless Communications and Sensor network Technology of Ministry of Education Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781509038237;9781509038220

Multimode terminals running multiple services at the same time are increasingly common, in response to this phenomenon, a network selection algorithm based on group decision making for heterogeneous wireless networks is presented in this paper. The algorithm considers each service from the terminal as a decision-maker, and obtains the weight vector of all network attributes for each service by the analytic hierarchy process, then uses the group decision making to synthesize these weight vectors. At the same time, the utility function is used to normalize the network attributes. At last, the network selection is made according to the synthesized weight vector and attribute utility. The simulation results show that the proposed algorithm is far superior to the TOPSIS in satisfying the Qo S requirements of different class of services, and can solve the problem of network selection for a group of services from a multi-service terminal.

关键词： network selection multi-service terminal group decision making Qo S requirement

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Energy Efficiency Optimization Based on Context Awareness in Wearable Computing

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Procedia Computer Science 2018年 129卷 291-297页

作者： Feng Tian Yongan Guo Wei Liu Dapeng Li Zhen Yang Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications) Ministry of Education Nanjing 210003 China National Mobile Communications Research Laboratory Southeast University Nanjing 210096 China

The motion capture system of the wireless wearable device can achieve identification and classification of human motion through various classifiers, but wireless data transmission consumes most of the energy of the sensor node. An adaptive wireless transmission control algorithm based on context awareness is proposed realize energy efficiency optimization based on single classifier and multiple classifiers fusion. Firstly, adaptive wireless transmission control method based on fixed single classifier can effectively improve the overall energy efficiency under the limit of classification accuracy. Secondly, adaptive transmission control method of multi-classifier fusion is proposed with adjusting decision threshold based on various classifiers and their characteristics. The experimental results validate our proposed algorithm.

关键词： Wearable computing energy efficiency adaptive transmission control context awareness multiple classifiers fusion

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