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arXiv 2019年

作者： Yang, Zhaohui Chen, Mingzhe Saad, Walid Shikh-Bahaei, Mohammad Centre for Telecommunications Research Department of Informatics Kings College London WC2B 4BG United Kingdom Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

In this paper, the sum-rate maximization problem is studied for wireless networks that use downlink rate splitting multiple access (RSMA). In the considered model, each base station (BS) divides the messages that must be transmitted to its users into a "private" part and a "common" part. Here, the common message is a message that all users want to receive and the private message is a message that is dedicated to only a specific user. The RSMA mechanism enables a BS to adjust the split of common and private messages so as to control the interference by decoding and treating interference as noise and, thus optimizing the data rate of users. To maximize the users' sum-rate, the network can determine the rate allocation for the common message to meet the rate demand, and adjust the transmit power for the private message to reduce the interference. This problem is formulated as an optimization problem whose goal is to maximize the sum-rate of all users. To solve this nonconvex maximization problem, the optimal power used for transmitting the private message to the users is first obtained in closed form for a given rate allocation and common message power. Based on the optimal private message transmission power, the optimal rate allocation is then derived under a fixed common message transmission power. Subsequently, a one-dimensional search algorithm is proposed to obtain the optimal solution of common message transmission power. Simulation results show that the RSMA can achieve up to 15.6% and 21.5% gains in terms of data rate compared to non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA), respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Power control

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An energy aware and load balancing routing protocol for mobile ad hoc networks 17

An energy aware and load balancing routing protocol for mobi...

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2017 International Conference on Telecommunications and Communication Engineering, ICTCE 2017

作者： Xu, Xiaojun Liao, Qing Zhao, Jingling Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450353151

In this paper, we propose an energy aware and load balancing routing protocol (EALB) for mobile ad hoc networks (MANETs). The proposed protocol takes the residual energy, the current load and the subsequent load changes of the node into account to select the best route. In the route discovery phase, energy levels were defined, and the node execute RREQ forwarding judgment according to its energy level, as far as possible to avoid the low residual energy nodes. In the route reply phase, EALB collects all possible routes by the delay reply mechanism, and selects the best route by the corresponding cost function which consists of energy cost and load cost. The simulations are carried out by NS2, the results show that EALB outperforms BBU-AODV in terms of average end-To-end delay, packet delivery rate and network lifetime. © 2017 Association for Computing Machinery.

关键词： Routing protocols

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A low computational complexity algorithm for compressive wideband spectrum sensing

A low computational complexity algorithm for compressive wid...

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作者： Ren, Shiyu Zeng, Zhimin Guo, Caili Sun, Xuekang Su, Kun Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

Compressed sensing (CS)-based wideband spectrum sens- ing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spec- trum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algo- rithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational com- plexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step. © Copyright 2017 The Institute of Electronics, Information and Communication Engineers.

关键词： Computational complexity

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UAV-assisted Cooperative Communications with Power-splitting SWIPT

UAV-assisted Cooperative Communications with Power-splitting...

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International Conference on Communication systems

作者： Sixing Yin Yifei Zhao Lihua Li Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781538678657;9781538678640

In this paper, we focus on a typical cooperative communication system with one pair of source and destination, where a miniaturized unmanned aerial vehicle (UAV) flying from a start location to an end location serves as a mobile relay. The UAV's transmission capability is powered exclusively by radio signal transmitted from the source via power-splitting mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV's power profile, power-splitting ratio profile and trajectory for amplify-and-forward (AF) protocol. The problem is decomposed into two subproblems: profile optimization given trajectory and trajectory optimization given profile. The former one is solved via the dual decomposition with in-depth analysis while the latter one is solved via successive convex optimization. Then the end-to-end cooperative throughput maximization problem is solved by alternately solving the two subproblems. The numerical results show that the proposed optimal solution outperforms two greedy strategies from existing work.

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Federated Deep Learning for Immersive Virtual Reality over Wireless networks

Federated Deep Learning for Immersive Virtual Reality over W...

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IEEE Global Communications Conference

作者： Mingzhe Chen Omid Semiari Walid Saad Xuanlin Liu Changchuan Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Princeton University Princeton NJ USA The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Colorado Colorado Springs CO USA Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Blacksburg VA USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIPs) that can detach the users from their virtual world. To measure the BIPs for wireless VR users, a novel model that jointly considers the VR applications, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, the base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to increase the data rate of VR users, thus, reducing the number of BIPs for each user. Therefore, the mobility and orientation of VR users must be considered when minimizing BIPs, since the body movements of a VR user may result in blockage of its wireless link. The BIP problem is formulated as an optimization problem which jointly considers the predictions of users' mobility patterns, orientations, and their BS association. To predict the orientation and mobility patterns of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' mobility patterns and orientations. Using these predictions, the user association policy that minimizes BIPs is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIPs by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.

关键词： Wireless communication Wireless sensor networks Delays Uplink Downlink Prediction algorithms Machine learning

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Research on K Anonymity Algorithm based on Association Analysis of Data Utility 2

Research on K Anonymity Algorithm based on Association Analy...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Yuanxiunan Gao Tao Luo Jianfeng Li Cong Wang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Software College Beijing University of Posts and Telecommunications

More and more medical data are shared, which leads to disclosure of personal privacy information. Therefore, the construction of medical data privacy preserving publishing model is of great value: not

ISBN: (纸本)9781467389808

关键词： Algorithm design and analysis Correlation coefficient Data privacy Principal component analysis Optimization Correlation Publishing

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A Channel-level Pruning Strategy for Convolutional Layers in CNNs

A Channel-level Pruning Strategy for Convolutional Layers in...

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IEEE International Conference on network Infrastructure and Digital Content (IC-NIDC)

作者： Fangzhou Song Ying Wang Yao Guo Chuang Zhu Jun Liu Mulan Jin Beijing Key Laboratory of Network System Architecture and Convergence China Center for Data Science Beijing China Chaoyang Hospital of Capital Medical University Beijing China

A large number of parameters and computing operations of deep convolutional neural networks(CNNs) make it hinder to apply them to real-world scenarios. In this paper, we propose a channel-level pruning strategy for convolutional layers to reduce the number of parameters and computing operations in CNNs with no accuracy loss. First, we use “Squeeze-and-Excitation” block to extract the activation factors of each sample, which can be used to evaluate the importance of each channel. Second, we compute the overall weight of a specific channel by accumulating its activation factors generated by all training samples. Finally, we prune the redundant channels with low weight and thus yield compact network. On a colorectal pathology dataset, we reduce the number of channels and the convolution layer parameters by a factor of 5× and 21×, without any accuracy loss.

关键词： Convolution Training Pathology Redundancy Computational modeling Convolutional neural networks

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Location-Aware Hypergraph Coloring Based Spectrum Allocation for D2D Communication

Location-Aware Hypergraph Coloring Based Spectrum Allocation...

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International Symposium on Wireless Communication systems (ISWCS)

作者： Zheng Li Shuo Chen Caili Guo Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

Device-to-device (D2D) communication underlaying cellular networks has been recognized as a promising technique to improve spectrum efficiency. However, densely deployed D2D pairs could cause severe interference to cellular user equipments (CUEs) without proper spectrum allocation. Since the CUEs are the primary users of the spectrum band, the quality of service(QoS) of CUEs should be guaranteed. Therefore, the location-aware hypergraph coloring (LAHC) based spectrum allocation algorithm is proposed in uplink D2D underlaying cellular networks. For a particular CUE, the entire cell area is divided into location-aware regions. A set of D2D pairs identified by these regions are limited when sharing spectrum with the CUE to protect the CUE from connection interruption. In addition, we study spectrum allocation using hypergraph coloring method to eliminate cumulative interference from multiple D2D pairs. Simulation indicates that the proposed spectrum allocation algorithm can both guarantee that D2D links will not cause service disruption to CUEs and significantly improve the system capacity.

关键词： Device-to-device communication Interference Resource management Cellular networks Receivers Transmitters Channel allocation

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Polarization-based Cross-tier Interference Alignment in Cognitive Heterogeneous Cellular network

Polarization-based Cross-tier Interference Alignment in Cogn...

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International Symposium on Wireless Communication systems (ISWCS)

作者： Xiaofang Gao Caili Guo Shuo Chen Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

To eliminate the cross-tier interference and optimize the bit-error-rate performance of small cell for downlink underlay cognitive heterogeneous cellular network, a novel polarization-based cross-tier interference alignment scheme is proposed in this paper by exploiting the polarization resource of two-tier network. The transmitting and receiving polarization states of small cell are designed by interference alignment-like behaviors of the minimum total mean squared error optimization model under interference power constraint. An analytical and noncooperative closed form solution is provided for the model. Simulation results show the proposed scheme outperforms the existing schemes in bit-error-rate performance of small cell while keeping the maximum sum rate of overall system. And the tradeoff between bit-error-rate performances of macro cell and small cell is also evaluated.

关键词： Interference Microprocessors Computer architecture Antennas Optimization Wireless communication Transmitters

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Low-Rank and Joint-Sparse Signal Recovery for Spatially and Temporally Correlated Data Using Sparse Bayesian Learning

Low-Rank and Joint-Sparse Signal Recovery for Spatially and ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yanbin Zhang Yangqing Li Changchuan Yin Kesen He Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing 100876 P. R. China

ISBN: (纸本)9781538646595

In order to meet the demands of data-intensive continuous monitoring in wireless body area network, we address a structured sparse signal recovery method to exploit both spatial and temporal correlations in data using compressive sensing (CS). Using a simultaneously low-rank and joint-sparse (L&S) signal model, we employ a Bayesian learning treatment by incorporating an L&S-inducing prior over the data and the appropriate hyperpriors over all hyperparameters, resulting in effective reconstruction of the L&S data. Simulation results suggest that the proposed L&S-bSBL is superior to the state-of-the-art recovery methods in terms of computation burden and runtime cost.

关键词： Wireless Body Area network Bayesian Learning Compressive Sensing Low-rank and Joint-sparse Bayesian learning Compressed sensing body area networks signals

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