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arXiv 2025年

作者： He, Boxiang Chen, Zihan Luo, Junshan Liu, Chuanhong Wang, Shilian Wang, Fanggang Quek, Tony Q.S. College of Electronic Science and Technology National University of Defense Technology Changsha410073 China Information Systems Technology and Design Singapore University of Technology and Design Singapore Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Semantic communication, due to its focus on the transmitting meaning rather than the raw bit data, poses unique security challenges compared to the traditional communication systems. In particular, semantic communication systems are vulnerable to the malicious attacks that focus on the semantic layer, with the intention of understanding or distorting the intended meaning of the transmitted privacy data. Diffusion models, a class of generative artificial intelligence (GenAI), are well-suited for ensuring data security to attack. Through iteratively adding and then removing noise, diffusion models can generate meaningful information despite the presence of the unknown noise. This article proposes a diffusion-based framework to enhance the security of semantic transmission for the attacks including eavesdropping and jamming. Specifically, the proposed framework incorporates both the artificial noise and natural channel noise into the forward process of the diffusion models during the semantic transmission, with the reverse process used to remove noise at the legitimate receiver. In the eavesdropping scenarios, the artificial noise is the friendly noise designed to prevent semantic eavesdropping. In the jamming scenarios, the artificial noise is the malicious jamming generated by the jammer, which disrupts the semantic transmission. The case studies show that the proposed diffusion-based framework is promising in securing the semantic transmission. We also consolidate several broad research directions associated with the proposed framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroosmosis

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A novel received signal strength assisted perspective-three-point algorithm for indoor visible light positioning

arXiv

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arXiv 2020年

作者： Bai, Lin Yang, Yang Feng, Chunyan Guo, Caili Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications

In this paper, a received signal strength assisted Perspective-three-Point positioning algorithm (R-P3P) is proposed for visible light positioning (VLP) systems. The basic idea of R-P3P is to joint visual and strength information to estimate the receiver position using 3 LEDs regardless of the LEDs' orientations. R-P3P first utilizes visual information captured by the camera to estimate the incidence angles of visible lights. Then, R-P3P calculates the candidate distances between the LEDs and the receiver based on the law of cosines and Wu-Ritt's zero decomposition method. Based on the incidence angles, the candidate distances and the physical characteristics of the LEDs, R-P3P can select the exact distances from all the candidate distances. Finally, the linear least square (LLS) method is employed to estimate the position of the receiver. Due to the combination of visual and strength information of visible light signals, R-P3P can achieve high accuracy using 3 LEDs regardless of the LEDs' orientations. Simulation results show that R-P3P can achieve positioning accuracy within 10 cm over 70% indoor area with low complexity regardless of LEDs orientations. Copyright © 2020, The Authors. All rights reserved.

关键词： Light emitting diodes

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Dynamic Frame-Slotted Aloha in RFID Using Received Signal Strength Information

Dynamic Frame-Slotted Aloha in RFID Using Received Signal St...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Gan Luan Norman C. Beaulieu School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering and the Beijing Key Laboratory for Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728154428

ISBN: (纸本)9781728154435

Received signal strength information based dynamic frame-slotted Aloha (RSSI-DFSA) RFID is proposed to replace random slot access where idle and collided slots lead to low system efficiency. Tag coordinates are calculated from received signal strength information, and the slot is chosen based on the coordinates. Simulations show that RSSI-DFSA system efficiencies are 14.9%, 2.4%, and 2.1% higher, and the time efficiencies are 8.3%, 2.5%, and 1.8% higher than those of existing algorithms, when the path loss exponent is 4 and the standard deviations of the shadowing are 1, 2, and 3 dB.

关键词： Heuristic algorithms Standards Passive RFID tags Estimation Shadow mapping Conferences Probabilistic logic

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A Design of Decision Engine for 5G Auto-driving Based on Ontology Slice 5

A Design of Decision Engine for 5G Auto-driving Based on Ont...

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5th IEEE International Conference on Computer and Communications, ICCC 2019

作者： Zhang, Chenyu Wen, Xiangming Zheng, Wei Lu, Zhaoming Wang, Luhan Beijing Laboratory of Advanced Information Networks China Beijing Key Lab of Network System Architecture and Convergence China School of Information and Communication Engineering Beijing Univ. of Posts and Telecom Beijing100876 China

ISBN: (纸本)9781728147437

With the access to 5G network, auto-driving vehicles can upload their sensors' information to Mobile Edge Computing (MEC) and get driving decision results relaying on the powerful computing ability of MEC. This article presents an architecture of intelligent decision engine running on MEC for 5G auto-driving (IDEA) by adopting the concept of ontology slice. IDEA constructs an ontoligy slice for every vehicle including sensor information, vehicle status, rule bases, etc. and provides logical isolated resources to prevent interference between ontologies. This paper gives out the method of implementing and managing of the ontology slices and method of scheduling reasoner. Besides, to make better and safer decisions, this paper also proposes an algorithm, combining an analytic strategy algorithm (WADD) and a heuristic strategy algorithm (TTB), considering both vehicle's velocity and sensors' refresh rate, to deal with incomplete information before making decesions. With simulation, it is shown that IDEA can provide efficient and reliable auto-driving decision service with low processing time. © 2019 IEEE.

关键词： Ontology

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A Simple Novel Idle Slot Prediction and Avoidance Scheme Using Prediction Bits for DFSA in RFID

A Simple Novel Idle Slot Prediction and Avoidance Scheme Usi...

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IEEE Conference on Wireless Communications and networking

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

Idle slots cause identification inefficiency in Aloha-based RFID algorithms. An idle slot prediction and elimination technique is proposed. The algorithm is dubbed idle predicting dynamic frame-slotted Aloha (IP-DFSA). In IP-DFSA, the reader reads a slot for RN16 as well as the idle prediction bits. Using only a few prediction bits, IP-DFSA can predict and eliminate a significant number of successive idle slots following the current time slot. Whereas previous schemes only achieve 36% efficiency on average, simulation results show that IP-DFSA with 1, 2, 3, and 4 idle slot prediction bits achieves 45%, 52.5%, 56%, and 60% system efficiencies, with 83%, 86%, 88%, and 89% time efficiencies, respectively. The system efficiencies of IP-DFSA range from 9% higher to 24% higher than previous schemes for 1 prediction bit to 4 prediction bits. The number k of idle prediction bits is optimized, and it is revealed that k optimal =4 for tag numbers n≥6.

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Joint system consumption minimization and workload allocation in edge-core network

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The Journal of China Universities of Posts and Telecommunications 2018年第3期25卷 45-54页

作者： Wang Shaochun Lu Zhaoming Wen Xiangming Wang Luhan Ma Lu Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications

The explosive increase of smart devices and mobile traffic results intolerable network latency and degraded service to the end-users. in heavy burden on backhaul and core network, As a complement to core network, edge network contributes to relieving network burden and improving user experience. To investigate the problem of optimizing the total consumption in an edge-core network, the system consumption minimization problem is fromulated, considering the energy consumption and delay. Given that the formulated problem is a mixed nonlinear integer programming （MNIP） , a low-complexity workload allocation algorithm is proposed based on interior-point method. The proposed algorithm has an extremely short running time in practice. Finally, simulation results show that edge network can significantly complement core network with much reduced backhaul energy consumption and delay.

关键词： edge network core network system consumption optimization workload allocation

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Left ventricle contouring in cardiac images based on deep reinforcement learning

TechRxiv

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TechRxiv 2021年

作者： Yin, Sixing Han, Yameng Pan, Judong Wang, Yining Li, Shufang Yu, F. Richard Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China University of California San Francisco San FranciscoCA94117 United States State Key Laboratory of Complex Severe and Rare Diseases Chinese Academy of Medical Sciences Peking Union Medical College Beijing100730 China The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Assessment of the left ventricle segmentation in cardiac magnetic resonance imaging (MRI) is of crucial importance for cardiac disease diagnosis. However, conventional manual segmentation is a tedious task that requires excessive human effort, which makes automated segmentation highly desirable in practice to facilitate the process of clinical diagnosis. In this paper, we propose a novel reinforcement-learning-based framework for left ventricle contouring, which mimics how a cardiologist outlines the left ventricle in a cardiac image. Since such a contour drawing process is simply moving a paintbrush along a specific trajectory, it is thus analogized to a path finding problem. Following the algorithm of proximal policy optimization (PPO), we train a policy network, which makes a stochastic decision on the agent’s movement according to its local observation such that the generated trajectory matches the true contour of the left ventricle as much as possible. Moreover, we design a deep learning model with a customized loss function to generate the agent’s landing spot (or coordinate of its initial position on a cardiac image). The experiment results show that the coordinate of the generated landing spot is sufficiently close to the true contour and the proposed reinforcement-learning-based approach outperforms the existing U-net model even with limited training set. © 2021, CC BY.

关键词： Reinforcement learning

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RLS-DPD Algorithm for Hybrid Precoding architecture in MIMO-OFDM systems

RLS-DPD Algorithm for Hybrid Precoding Architecture in MIMO-...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Tingyu Huang Weijun Hong Tingxiao Cai Huanhuan Lin Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China China Electronics Stand-ardization Institute Beijing China Borsche Technology Limited Corporation Beijing China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

In emerging communication systems, such as 5G 6G, millimeter wave and terahertz communication, OFDM and MIMO are indispensable technologies. However, the cross-modulation among different subchannel would greatly degrade the performance of system due to the non-linearity of the large-scale amplifier array. Hybrid precoding technology is a general candidate for this problem due to its capability for linearization, and the predistortion matrix would be the key factor for its performance. In this paper, a memory polynomial model is adopted for modelling nonlinear power amplifier (PA) and Recursive Least Square based digital predistortion (RLS-DPD) algorithm is proposed for parameter training for hybrid predistortion module. The proposed RLS algorithm extracts the parameters of the digital predistortion module through adaptive learning in the training phase. Adaptive learning refers to the process of using the filter parameters obtained at the previous moment to automatically adjust the parameters at the current moment according to the estimated error, which minimizes the cost function. The simulation results show that the RLS-DPD algorithm can reduce the system bit error rate by 27.778 % , error vector magnitude (EVM) by 67.961 % at 8dB SNR, and enable the nonlinear OFDM signal to be demodulated correctly at the terminal.

关键词： Training Adaptive learning Adaptation models Precoding OFDM Simulation Predistortion

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Exploiting Pose Mask Features for Video Action Recognition 5

Exploiting Pose Mask Features for Video Action Recognition

引用

5th IEEE International Conference on Computer and Communications, ICCC 2019

作者： Miao, Junqing Xia, Hailun Zeng, Zhimin Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture Convergence Beijing Lab. of Adv. Info. Networks Sch. of Info. and Communication Engineering Beijing China

ISBN: (纸本)9781728147437

Existing state-of-the-art approaches in video action recognition mostly adopt RGB images and optical flows as input and neglect the abundant information provided by human poses. In this paper, we present a novel method that highlights the essential area of human body and takes it as complementary input for existing action recognition pipelines. The method, called Pose Mask network (PMN), leverages a 2D pose estimator to extract heatmaps from frames and utilizes them as Pose Masks on the original images. The Pose Masks are robust to the variance of background and focus on key information of human body. Experiments show that our Pose Mask yields a result far exceeding that of using simple pose representations. More importantly, PMN acts as a supplement to other RGB-based approaches. Combining our PMN with Temporal Segment network, we obtain state-of-the-art performance on the HMDB51 and JHMDB datasets. © 2019 IEEE.

关键词： Gesture recognition

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Optimization of user selection and bandwidth allocation for federated learning in VLC/RF systems

arXiv

引用

arXiv 2021年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Poor, H. Vincent Cui, Shuguang Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

Limited radio frequency (RF) resources restrict the number of users that can participate in federated learning (FL) thus affecting FL convergence speed and performance. In this paper, we first introduce visible light communication (VLC) as a supplement to RF in FL and build a hybrid VLC/RF communication system, in which each indoor user can use both VLC and RF to transmit its FL model parameters. Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance. The problem is first separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The final user selection and bandwidth allocation are obtained by iteratively solving these two subproblems. Simulation results show that the proposed FL algorithm that efficiently uses VLC and RF for FL model transmission can improve the prediction accuracy by up to 10% compared with a conventional FL system using only RF. Copyright © 2021, The Authors. All rights reserved.

关键词： Visible light communication

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