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IEEE International Conference on Communications

作者： Xiang Chen Haiqing Du Jenq-Neng Hwang James A. Ritcey Chung-Nan Lee Department of Electrical Engineering University of Washington Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Department of Computer Science and Engineering National Sun Yat-Sen University

ISBN: (纸本)9781467364300

We propose a forward error correction (FEC) coding rate adaptation scheme which maximizes the quality of experience (QoE), for scalable video coding (SVC) based video transmissions over multi-input multi-output (MIMO) systems. The proposed scheme adaptively selects the best set of spatial channels, number of video layers and their corresponding FEC coding rate according to channel state information (CSI) from the receiver. Unlike previous work, our proposed scheme distributes the FEC encoded bit streams to multiple spatial channels so that additional diversity gains can be obtained. Due to the complexity of the optimization, we decompose the original problem into several sub-problems, which can then be solved by a heuristic algorithm. The optimal solution can be found by choosing the best among all the candidate solutions obtained from the sub-problems. The effectiveness and superb performance of our proposed scheme can be demonstrated by many simulations with different videos and channel conditions.

关键词： FEC QoE SVC MIMO Rate adaptation

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Trajectory Design for Energy Harvesting UAV networks: A Foraging Approach

Trajectory Design for Energy Harvesting UAV Networks: A Fora...

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IEEE Conference on Wireless Communications and networking

作者： Xuanlin Liu Mingzhe Chen Sihua Wang Walid Saad Changchuan Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Princeton University Princeton NJ USA Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at the center of a user group. Meanwhile, to serve ground users and harvest energy, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the number of users that are served by the UAV, and the energy consumption and harvesting. To capture the relationship among these factors, we first model the completion of service and the harvested energy as reward, and the energy consumption during the deployment as cost. Then, the deployment profitability is defined as the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability. The proposed algorithm can find the optimal trajectory for the UAV with a polynomial time complexity. Fundamental analysis shows that the proposed algorithm can achieve the maximal deployment profitability. Simulation results show that the proposed algorithm can effectively reduce the operation time and achieve up to 25.6% gain in terms of the deployment profitability compared to Q-learning algorithm.

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Analysis of Memory Capacity for Deep Echo State networks

arXiv

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arXiv 2019年

作者： Liu, Xuanlin Chen, Mingzhe Yin, Changchuan Saad, Walid Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

—In this paper, the echo state network (ESN) memory capacity, which represents the amount of input data an ESN can store, is analyzed for a new type of deep ESNs. In particular, two deep ESN architectures are studied. First, a parallel deep ESN is proposed in which multiple reservoirs are connected in parallel allowing them to average outputs of multiple ESNs, thus decreasing the prediction error. Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series. This series ESN architecture can capture more features between the input sequence and the output sequence thus improving the overall prediction accuracy. Fundamental analysis shows that the memory capacity of parallel ESNs is equivalent to that of a traditional shallow ESN, while the memory capacity of series ESNs is smaller than that of a traditional shallow ESN. In terms of normalized root mean square error, simulation results show that the parallel deep ESN achieves 38.5% reduction compared to the traditional shallow ESN while the series deep ESN achieves 16.8% reduction. Copyright © 2019, The Authors. All rights reserved.

关键词： network architecture

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A Sparse Superposition Multiple Access Scheme and Performance Analysis for 5G Wireless Communication Systems

A Sparse Superposition Multiple Access Scheme and Performanc...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Kai Zhang Jin Xu Changchuan Yin Weiqiang Tan Chunguo Li Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China School of Computer Science Guangzhou University Guangzhou China National Communications Research Laboratory Southeast University Nanjing China

ISBN: (纸本)9781538670125;9781538670118

Orthogonal multiple access (OMA) is widely employed in traditional cellular wireless communication systems due to a low-complexity receiver is needed. However, multiple access scheme with higher spectral efficiency and the capability of access more users than OMA scheme is preferred for the future wireless communication systems. In this work, we propose a non-orthogonal multiple access scheme called sparse superposition multiple access (SSMA) by superposing more users to a limited number of time-frequency resource for the uplink transmission in the fifth generation (5G) wireless communication systems. Moreover, we design two sparse superposition matrices (SSMs) for 150% and 200% overloaded SSMA scheme. Then, a low complexity belief propagation detection and decoding algorithm based multiple user detection is proposed. Furthermore, we utilize extrinsic information transfer chart to analyze the superiority of the proposed SSMs theoretically and the numerical results show that the proposed SSM with an overload of 150% has a gain of about 1 dB compared to the existing scheme. Finally, simulation results demonstrate that the proposed scheme outperforms the traditional OMA scheme and the existing scheme significantly.

关键词： Multiuser detection Wireless communication Conferences Convergence Maximum likelihood decoding Message passing Receivers

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Rail Transit Event Extraction based on the Fusion of Semantic and Local Dependency Features

Rail Transit Event Extraction based on the Fusion of Semanti...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Qiang Chen Haining Meng Yihao Bao Mingsong Yang Xinyu Tong Qin Zhao Muhammad Bilal Khan Xinhong Hei School of Computer Science and Engineering Xi’an University of Technology Xi’an China School of Computer Science and Engineering Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an University of Technology Xi’an China School of Civil Engineering and Architecture Xi’an University of Technology Xi’an China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Event extraction in rail transit plays a vital role in all stages of design, construction, and final acceptance. Event extraction is a key part of building event logic graph (ELG). However, the difficulty in obtaining semantic features and event local dependencies poses a challenge to accurate event extraction, which hinders the informatization and intelligent development of rail transit projects. To address these challenges, we proposes an event extraction method that fuses semantic and local dependency features. The study initially preprocesses the rail transit design codes dataset and performs sequence labeling. Then, the event trigger words are mined by combining the term frequency-inverse document frequency (TF-IDF) and minimum edit distance (MED) algorithms. Based on the semantic similarity measurement method and the trigger word mining results, the features representation of event semantic information is enhanced by using the Bidirectional Encoder Representations from Transformers (BERT) model and Bi-directional Long Short-Term Memory (BiLSTM) model. At the same time, event arguments are mined and a local dependency graph is generated based on their mining results, and then the enhanced local dependency features are obtained by using the graph convolutional network (GCN). Finally, the event semantic features and local dependency features are fused, and the conditional random field (CRF) is used to predict the labels to obtain the event extraction results. Through the comparison with five baseline methods on the datasets of rail transit design codes, it is found that the Precision, Recall, and F1 score of the proposed method are improved, with an average improvement of 9.604% on Precision, 9.342% on Recall, and 9.730% on F1 score, respectively. The contribution of the sub-modules to the overall performance is further evaluated through ablation experiments, which confirms the effectiveness and feasibility of the proposed method.

关键词： Rails Codes Semantics Bidirectional control Knowledge graphs Feature extraction Transformers Conditional random fields Logic Labeling

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Digital Over-the-Air Federated Learning in Multi-Antenna Systems

arXiv

引用

arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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A detailed experimental performance evaluation on TCP over UBR

A detailed experimental performance evaluation on TCP over U...

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IEEE International Conference on ATM

作者： L. Jaussi M. Lorang J. Nelissen Telecommunications Laboratory Swiss Federal Institute of Technology Lausanne Switzerland Inst. of Communication Networks and Computer Engineering University of Stuttgart Stuttgart Germany Network Architecture Traffic Technology Team Alcatel-Alsthom Corporate Research Center Belgium

The ATM service category UBR is intended for non-real-time applications that do not require guaranteed QoS commitments. With additional, relatively inexpensive control functions such as packet discard schemes, UBR could become a cost-effective alternative for the transmission of data traffic; offering a straightforward and flexible solution as opposed to nrt-VBR and GFR that applies stricter traffic specifications as well as ABR with its sophisticated and complex rate-control protocol. This paper presents the results obtained from a comprehensive set of experiments with TCP over UBR, comprising of measurements taken on different protocol layers. The goal is to experimentally investigate the performance of UBR to carry TCP traffic, to evaluate the performance gain achievable by packet discard schemes and TCP parameter tuning, to study the influence of the TCP implementation, and in a final step, to relate the measurements to simulation results.

关键词： Asynchronous transfer mode Traffic control Switches Protocols Size measurement Laboratories Communication networks computer networks Telecommunication computing computer architecture

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Transmission mode selection for downlink transmission in LTE-A networks

Transmission mode selection for downlink transmission in LTE...

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

作者： Jian Yu Geoffrey Ye Li Changchuan Yin Xiaolong Zhu Yusun Fu Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA Huawei Shanghai Research Institute Shanghai China

In this paper, we investigate mode selection for coordinated multi-point (CoMP) transmission in downlink LTE-A networks, where inter-cluster interference exists. The proposed scheme selects the single-cell (SC) or coordinated beamforming (CB) transmission mode according to interference level to maximize the weighted sum rate with fairness consideration. To formulate the optimization problem, an approximated closed-form expression for the achievable rate is derived by modeling the probability distributions of signal and interference powers as Gamma distributions. Since the original optimization problem is a combinatorial and non-convex one with high complexity, a low-complexity and sub-optimal algorithm is proposed, which first performs coordinated cell selection and then transmission mode selection. Simulation results show that the closed-form approximation of the achievable rate is very tight and the proposed mode selection scheme can improve the average system throughput by 13% and the 10th percentile user throughput by 10% compared with the existing scheme.

关键词： Interference Optimization Approximation methods Closed-form solutions Downlink Signal to noise ratio Land mobile radio

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Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning

arXiv

引用

arXiv 2024年

作者： Huan, Ouwen Yang, Yang Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, a multi-modal data based semisupervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Find Your Online Social Friends from Mobile Internet Traffic

Find Your Online Social Friends from Mobile Internet Traffic

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

作者： Yi Zhang Yuanyuan Qiao Yanting Zhang Nanfei Shu Yizhe Song Jie Yang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Technology Research Institute Aisino Corporation Beijing China School of Electronic Engineering and Computer Science Queen Mary University of London London England

Increasingly more mobile Internet traffic is produced which contains ample personal information related to user mobility and website browsing behavior. Prior research has attempted to recommend friends based on Global Position System (GPS) in location based social networks (LBSN). However, the study of friend recommendation in general social network according to position from the base station is relatively understudied. This paper introduces a novel feature set extracted from mobile Internet traffic according to base station location and Uniform Resource Locator (URL). We train classification models using these features to predict friendship between pairs of Weibo users. Results show that both base station location and URL when acted alone can already effectively reflect friendships even in general social network. We further show that by fusing the two features together, the model obtains even better performance. Finally, we demonstrate that the location and URL features can improve prediction performance than only using the common friends.

关键词： Uniform resource locators Feature extraction Social network services Base stations Internet Global Positioning System Predictive models

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