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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Ma, Xingjie Yin, Changchuan Liu, Xuanlin Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728186351

In this paper, we consider an unmanned aerial vehicle (UAV)-based mobile edge computing (MEC) system for Internet of Things devices (IoTDs), in which the mobile UAV equipped with computing resources provides service for all IoTDs in the system and each IoTD also has a certain amount of computing power. Each IoTD has a computing task to be completed and the computing task will be offloaded to the UAV or implemented locally. After the UAV completes the service for any IoTD, it proceeds to the next IoTD, until that all IoTDs in the system have been served. The UAV can collect all information related to all IoTDs before taking off, such as the data size of the computing task and the coordinate of the locations of all IoTDs. Our goal is to minimize the total delay of the system (i.e., flying delay, transmission delay, local computing delay and UAV-aided edge computing delay) through joint optimization of the flying trajectory of the UAV and the ratio of the offloading tasks. Aiming at this problem, a machine learning framework based on Q-learning algorithm is proposed so as to minimize the total delay of the system. Simulation results show that the proposed algorithm can achieve up to 15.7% and 38.0% gain compared to the random algorithm with dynamic selection of task offloading mode and the Q-learning algorithm with fixed selection of task offloading mode. © 2020 IEEE.

关键词： Internet of things

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A lightweight fully convolutional network for cardiac MRI segmentation

A lightweight fully convolutional network for cardiac MRI se...

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2020 International Conference on Computer Information and Big Data Applications, CIBDA 2020

作者： Yang, Wenjie Li, Shufang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijng China

ISBN: (纸本)9781728198378

Cardiac magnetic resonance imaging is a commonly used method to assess the function and structure of the cardiovascular system. Separating the heart structure from Cardiac MRI is an important step in calculating the cardiac function index. The automatic segmentation algorithm can significantly reduce the workload of the doctor. Most of the current automatic segmentation algorithm has a large calculation amount and size, has high requirements on the hardware. Based on the U-Net model, this paper proposes a lightweight full convolution model with separable convolution and residual connection. Experiments show that the model reduces the number of parameters while ensuring learning ability and balances speed and accuracy. © 2020 IEEE.

关键词： Convolution

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Sum-Rate Maximization for Active RIS-Aided Downlink RSMA system

Sum-Rate Maximization for Active RIS-Aided Downlink RSMA Sys...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Xinhao Li Tao Wang Haonan Tong Zhaohui Yang Yijie Mao Changchuan Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Information Science and Technology ShanghaiTech University Shanghai China

In this paper, the problem of sum-rate maximization for an active reconfigurable intelligent surface (RIS) assisted downlink rate-splitting multiple access (RSMA) transmission system is studied. In the considered model, the active RIS is deployed to overcome severe power attenuation, which is caused by the cumulative product of RIS incidence path loss and the reflection path loss. Since the active RIS can adjust both the phase and the amplitude of the incident signal simultaneously, the RIS control scheme requires delicate design to improve RSMA communication performance. To address this issue, a sum-rate maximization problem is formulated to jointly optimize the beamforming vectors, rate allocation vector, and RIS precoding matrix. To solve this non-convex sum-rate maximization problem, an iterative algorithm based on fractional programming (FP) and quadratic constraint quadratic programming (QCQP) is proposed. In particular, the proposed algorithm firstly decomposes the original problem into two subproblems, namely, 1) beamforming and rate allocation optimization and 2) active RIS precoding optimization. The corresponding variables of the two subproblems are optimized through sequential convex approximation (SCA) and block coordinate descent (BCD), respectively. Numerical results show that the proposed active RIS-aided RSMA system could increase the sum-rate by up to 45% over the conventional passive RIS-aided RSMA system with the same energy consumption.

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Sum-Rate Maximization for Active RIS-Aided Downlink RSMA system

arXiv

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arXiv 2023年

作者： Li, Xinhao Wang, Tao Tong, Haonan Yang, Zhaohui Mao, Yijie Yin, Changchuan Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Information Science and Technology ShanghaiTech University Shanghai China

n this paper, the problem of sum-rate maximization for an active reconfigurable intelligent surface (RIS) assisted downlink rate-splitting multiple access (RSMA) transmission system is studied. In the considered model, the active RIS is deployed to overcome severe power attenuation, which is caused by the cumulative product of RIS incidence path loss and the reflection path loss. Since the active RIS can adjust both the phase and the amplitude of the incident signal simultaneously, the RIS control scheme requires delicate design to improve RSMA communication performance. To address this issue, a sum-rate maximization problem is formulated to jointly optimize the beamforming vectors, rate allocation vector, and RIS precoding matrix. To solve this non-convex sum-rate maximization problem, an iterative algorithm based on fractional programming (FP) and quadratic constraint quadratic programming (QCQP) is proposed. In particular, the proposed algorithm firstly decomposes the original problem into two subproblems, namely, 1) beamforming and rate allocation optimization and 2) active RIS precoding optimization. The corresponding variables of the two subproblems are optimized through sequential convex approximation (SCA) and block coordinate descent (BCD), respectively. Numerical results show that the proposed active RIS-aided RSMA system could increase the sum-rate by up to 45% over the conventional passive RIS-aided RSMA system with the same energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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A Novel Soft-Output Demapper for DOCSIS 3.1 6

A Novel Soft-Output Demapper for DOCSIS 3.1

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Zheng, Feng Pang, Bowen Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunication Beijing100876 China

ISBN: (纸本)9781728186351

DOCSIS 3.1, the new international standard for high-speed cable television (CATV), uses Low Density Parity Check (LDPC) code for forward error correction (FEC). It is widely known that accurate soft information is critical to the performance of LDPC decoding, but existing bit log-likelihood ratio (LLR) expression only works well under high code rate. In this paper, an improved bit LLR expression is proposed to compensate the difference between the existing LLR expression under low code rate for Gray coded M-ary quadrature amplitude modulation (QAM). In addition to high accuracy, the improved LLR is simple and efficient to manipulate. Besides LDPC, the improved LLR is also applicable to other codes that require soft information. Simulation results show that the improved LLR expression achieves a significant improvement in performance for both LDPC and Turbo decoding. © 2020 IEEE.

关键词： Quadrature amplitude modulation

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Graph Optimization Based Localization Algorithm by Fusing VLP and VO

Graph Optimization Based Localization Algorithm by Fusing VL...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Minan Guo Bowen Jia Chunyan Feng Zhiyu Zhu Yang Yang Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China China Electronics Standardization Institute Beijing China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Visible light positioning (VLP) has attracted considerable interest due to its high accuracy and low cost. However, VLP typically requires several light emitting diodes (LEDs) for positioning, which may lead to positioning failure in sparse LED layout or parts of the links are blocked. To address this challenge, we propose a novel graph optimization-based localization algorithm by fusing VLP and visual odometry (VO), termed GO-VLO. In the considered model, a few LEDs are deployed on the ceiling and broadcast positioning information, while a receiver equipped with a camera conducts GO-VLO to achieve precise positioning with limited LEDs. GO-VLO consists of a front end to process positioning information and a back end to further optimize the positioning results. In particular, VLP and VO are first used separately at the front end, thus obtaining two respective positioning results. Then, a visible-light visual odometry fusion mechanism is proposed to fuse the positioning results of VLP and VO preliminarily. To strengthen the constraints between frames and prevent location failure when VLP is unavailable, a novel graph optimization framework is further proposed in the back end, which notably takes the covisible feature points of two successive frames into consideration. Experimental results show that our proposed method attains a positioning accuracy of 4.93 cm in two dimension and 6.20 cm in three dimension under dense LED layout.

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Tie-line Power Adjustment Method Based on Proximal Policy Optimization Algorithm 3

Tie-line Power Adjustment Method Based on Proximal Policy Op...

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2020 3rd International Symposium on Power Electronics and Control Engineering, ISPECE 2020

作者： Hou, Jinxiu Yu, Zhihong Zheng, Qingping Xu, Huating Li, Shufang China Electric Power Research Institute Beijing100192 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China College of Electrical Engineering Zhejiang University Hangzhou310027 China

The tie-line power adjustment is an essential part of the power system operation state calculation. Various existing algorithms to solve the tie-line power adjustment problem are mainly implemented by introducing tie-line power equation constraints into conventional power flow calculations. Such methods have low calculation efficiency, not enough automation, and are prone to non-convergence in the power flow calculation. In this paper, the tie-line power adjustment problem is formulated as a Markov decision process, and the proximal policy optimization algorithm is introduced to optimize the decision policy. In order to enhance the effectiveness of the proposed method, a new deep neural network structure suitable for the proximal policy optimization algorithm is designed. The proposed method is verified with the IEEE 39-bus system. © Published under licence by IOP Publishing Ltd.

关键词： Markov processes

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SEMANTIC-ASSISTED IMAGE COMPRESSION

arXiv

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arXiv 2022年

作者： Sun, Qizheng Guo, Caili Yang, Yang Chen, Jiujiu Xue, Xijun Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

Conventional image compression methods typically aim at pixel-level consistency while ignoring the performance of downstream AI tasks. To solve this problem, this paper proposes a Semantic-Assisted Image Compression method (SAIC), which can maintain semantic-level consistency to enable high performance of downstream AI tasks. To this end, we train the compression network using semantic-level loss function. In particular, semantic-level loss is measured using gradient-based semantic weights mechanism (GSW). GSW directly consider downstream AI tasks' perceptual results. Then, this paper proposes a semantic-level distortion evaluation metric to quantify the amount of semantic information retained during the compression process. Experimental results show that the proposed SAIC method can retain more semantic-level information and achieve better performance of downstream AI tasks compared to the traditional deep learning-based method and the advanced perceptual method at the same compression ratio. © 2022, CC BY.

关键词： Image compression

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Energy-Efficient Coded Caching and Resource Allocation for Smart Grid-Supported HetNets 14th

Energy-Efficient Coded Caching and Resource Allocation for S...

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14th EAI International Conference on Communications and networking in China, ChinaCom 2019

作者： Yin, Fangfang Lyu, Junyi Liu, Danpu Zhang, Zhilong Zeng, Minyin Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783030411138

Compared with uncoded caching, coded caching (CC) that exploits accumulated cache size and distributes fractions of a file in different base stations (BSs), can significantly reduce delays and backhaul transmissions. Small base stations (SBSs) with both cache and energy harvesting (EH) ability have attracted extensive attention in recent years. Moreover, renewable energy (RE) also called green energy can be shared between SBSs via the Smart Grid (SG). This paper investigates CC, resource allocation (RA) and energy cooperation (EC) in cache-enabled energy harvesting (EH) heterogeneous networks (HetNets). We formulate the joint optimization problem, aims at minimizing the conventional grid energy consumption while satisfying quality of service (QoS) requirements of users. Simulation results demonstrate the considerable reduction in conventional grid energy consumption compared with other benchmarks. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Resource allocation

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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