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A Neural-Based OTFS Channel Estimator

A Neural-Based OTFS Channel Estimator

引用

IEEE Conference on Wireless Communications and networking

作者： Zhenghe Zhu Yawen Chen Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

orthogonal time frequency space (OTFS) systems are considered as the reliable solution for addressing the challenges of high mobility in sixth generation (6G) scenarios. By modulating data across both the delay and Doppler dimensions, OTFS efficiently handles the double spread problems caused by high mobility. Accurate and efficient channel estimation is essential for ensuring reliable data reception in OTFS systems. In this paper, we propose a convolutional neural network (CNN) based channel estimator within the standard OTFS transceiver framework to estimate the transmitted channel from the received OTFS signal. Simulation results demonstrate that our neural channel estimator achieves superior channel reconstruction and outperforms existing neural-based methods. Moreover, we analyze its performance with higher-order modulation and show that it effectively supports such modulation under good channel conditions, improving spectral efficiency and throughput.

关键词： 6G mobile communication Time-frequency analysis Spectral efficiency Simulation Channel estimation Modulation Transceivers Reliability Convolutional neural networks Standards

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Semi-SemNet: A Evolved Servitization network architecture from 5G to Semantic Communication

Semi-SemNet: A Evolved Servitization Network Architecture fr...

引用

IEEE Conference on Wireless Communications and networking

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

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recently, a new communication paradigm, semantic communication, has garnered significant attention due to its superior information transmission efficiency compared to conventional communication. However, current research on semantic communication primarily focuses on the design of end-to-end communication systems from the perspective of goal orientation or information reconstruction, without considering integration with the evolving technological landscape of the 5th generation mobile communication (5G) networks. To address this gap, in this paper, we propose an evolved servitization network architecture, called Semi-SemNet, which provides goal-level semantic service capability for the proposed converged goal-oriented semantic communication. To enable dynamic semantic-aware load balancing, the contexts of network functions (NFs) are decoupled from themselves and managed by a common NF, rendering each NF stateless. Additionally, we present an overview of enhanced NFs and system procedures for semantic communications. To meet different target nodes, such as application servers and user equipment (UE), we introduce a semantic translation mechanism to eliminate performance degradation when there are multiple independent wireless links between the source node and the target node. Finally, we use fire video remote monitoring as a case study. Experimental results show that Semi-SemNet achieves an average 26.67% PSNR and 27.21% MS-SSIM performance improvement in information reconstruction, and saves 40.51% bandwidth resources in goal execution. Moreover, with the stateless mechanism and semantic-aware load balancing decisions, it can reduce semantic data transmission delay by an average of 60.59% compared to the benchmark methods. The all code and original data of this paper are publicly available at https://***/hwniu/sbi_semantic_***.

关键词： Wireless communication Translation 5G mobile communication network architecture Semantic communication Load management Rendering (computer graphics) Noise measurement Servers Remote monitoring

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Joint Optimization of VNF Reusing and Routing in Satellite networks

Joint Optimization of VNF Reusing and Routing in Satellite N...

引用

IEEE Conference on Wireless Communications and networking

作者： Weixin Yan Zhilong Zhang Danpu Liu Tao Luo Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Low-earth orbit (LEO) satellite networks has attracted a lot of attention, since it is able to provide high-quality services worldwide. With the assistance of NFV technology, the flexibility and the quality of service (QoS) of LEO satellite networks can be further improved. However, most existing researches have not considered virtual network functions (VNFs) reusing during the process of service function chain (SFC) placement in NFV-enabled satellite networks. In this paper, we investigate the problem of SFC placement, and propose a joint VNF reusing and routing algorithm. By considering both of the initialization delay and sharing of VNFs, the service deployment delay can be effectively reduced, and the network profit can be largely improved. In our setting, simulation results show that our proposed algorithm outperforms baseline algorithms in profit, with an improvement of at least 23 %.

关键词： Satellites Service function chaining Simulation Low earth orbit satellites Quality of service Routing Orbits Delays Resource management Optimization

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RIS-Enhanced LEO Satellite Communication: Joint Passive Beamforming and Orientation Optimization

RIS-Enhanced LEO Satellite Communication: Joint Passive Beam...

引用

2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Zheng, Ziyuan Jing, Wenpeng Lu, Zhaoming Wen, Xiangming Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing China

ISBN: (纸本)9781665459754

Low Earth orbit (LEO) satellite communication (SatCom) systems have emerged as a key enabler to provide ubiquitous connectivity in the upcoming generation networks. Yet the conflict between power-limited link budget and long-distance path loss restricts the universal application of LEO SatCom. Fortunately, the reconfigurable intelligent surface (RIS) technique can be an efficient solution to address this issue. In this paper, we incorporate a RIS into the LEO SatCom system to enhance its downlink transmission by introducing a controllable reflection link. A joint RIS passive beamforming and orientation optimization problem is formulated to maximize average throughput at the ground terminal (GT) during the service. The formulated non-convex problem has the unit-modulus constraint on the phase shifters and the piecewise RIS radiation coefficient function related to the predictable satellite trajectory. A two-stage optimization framework is proposed to equivalently solve two sequential subproblems. Specifically, for the passive beamforming subproblem, a closed-form solution is derived at first in compliance with the phase alignment principle. Then, an iterative algorithm is proposed to approximately solve the orientation optimization subproblem. Simulation results show the substantial performance gains on link budget and throughput achieved by the proposed RIS-assisted LEO SatCom scheme. © 2022 IEEE.

关键词： Satellite communication systems

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EasyWiTrack: Fine-Grained Sensing for Plug-and-Play Position Tracking with Wi-Fi

EasyWiTrack: Fine-Grained Sensing for Plug-and-Play Position...

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IEEE Conference on Wireless Communications and networking

作者： Jiawei Jiang Zijun Han Zhaoming Lu Xiangming Wen Gaolong Jiang Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Previous work have verified the feasibility of Wi-Fi - based indoor position tracking. However, these research rely heavily on anchors' position as prior knowledge, which lowers the deployability in practical scenarios. To this end, we propose a novel system named EasyWiTrack to realize a plug-and-play, rel-ative position tracking by establishing a time-domain correlation model to associate the target path, angle information with target motion, and deduce the target's relative position compared to the previous moment for trajectory shape without any anchor position information. During the system implementation, to address the issue of error introduced by asynchronous Wi-Fi transceivers, we adopt bi-directional data acquisition for denoising and achieve millimeter-level target path length estimation. Additionally, to eliminate ambiguity in linear array angle estimation, we employ circle array and apply Multiple Signal Classification algorithm to estimate signal azimuth. Finally through extensive experimentation, we have validated that EasyWiTrack achieves a fine-grained indoor position tracking performance with 1.48cm and 1.73cm average error respectively in LoS and NLoS scenarios with commodity Wi-Fi.

关键词： Target tracking Correlation Trajectory tracking Estimation Transceivers Trajectory Arrays

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Attention Aided Channel Prediction Scheme For Satellite-Terrestrial networks

Attention Aided Channel Prediction Scheme For Satellite-Terr...

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

作者： Chuankai Cui Wenpeng Jing Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Low Earth orbit (LEO) communication satellites are becoming an essential component of the integrated spaceterrestrial network for sixth-generation (6G) networks nowadays. However, due to the time-varying communication environment and long round-trip time (RTT), the downlink channel state information (CSI), which is always obtained by satellites from user equipments feedback, is severely outdated. Furthermore, as the satellite moves, changes in elevation angle result in received CSI with varying correlation characteristics and levels of outdatedness. Motivated by these issues, this paper proposes a deep learning (DL)-based prediction scheme named ATNet, which is designed to make channel prediction in frequency division duplexing (FDD) satellite Communication systems. Specifically, the predictor integrate convolutional neural network (CNN) and attention mechanism with the foundation of long short-term with memory (LSTM) network. This scheme considers the relationship between elevation angle and channel correlation, as well as the degree of CSI outdatedness. Simulation results demonstrate that our ATNet scheme has achieved a $7 \%-17 \%$ improvement in prediction accuracy compared to the baseline schemes.

关键词： Correlation Accuracy Simulation Satellite broadcasting Low earth orbit satellites Space-air-ground integrated networks Frequency conversion Convolutional neural networks Long short term memory Land mobile radio

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WiFineTrack: Enabling Fine-Grained Position Tracking Using Commodity WiFi

WiFineTrack: Enabling Fine-Grained Position Tracking Using C...

引用

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

作者： Gaolong Jiang Zijun Han Zhaoming Lu Xiangming Wen Jiawei Jiang Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

With the development of integrated sensing, communication and computation (ISCC), wireless sensing technology is turning a WiFi device into a special sensor. Specifically, the current research focuses on extending the inherent communication attributes of ubiquitous WiFi signals to achieve indoor position tracking. However, previous works based on geometric positioning like triangulation are limited by the number of antennas and bandwidth, and can only achieve decimeter-level positioning accuracy. To this end, we present WiFineTrack, enabling fine-grained position tracking based on Channel State Information (CSI). Instead of representing the continuous absolute positions directly, WiFineTrack first characterizes the relative motion traces by mapping the target’s movements in relation to its prior position. Then the model correlates the angle-based triangulation with the derived relative motion traces to reconstruct the absolute motion traces. Thus, the model shifts the tracking task from traditional absolute geometry to relative length variation. WiFineTrack designs an elaborate denoising framework for tracking refinement, including phase correcting based on the bidirectional transmission mechanism and accumulated error suppression based on the Extended Kalman Filter (EKF). We implement WiFineTrack using off-the-shelf WiFi devices, and empirical results show that WiFineTrack can achieve an accuracy of 10.45 cm under diverse environment conditions.

关键词： Geometry Wireless communication Wireless sensor networks Target tracking Accuracy Prototypes Turning Sensors Wireless fidelity Standards

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Trajectory Design for Multi-UAV-Enabled Wireless Powered Communication networks: A Multi-Agent DRL Approach

Trajectory Design for Multi-UAV-Enabled Wireless Powered Com...

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IEEE Conference on Wireless Communications and networking

作者： Honghao Qi Muqing Wu Zijian Zhang Min Zhao Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to support ground networks. In UAV-enabled wireless powered communication networks (WPCNs), the UAV provides data collection (DC) and energy transmission (ET) services for ground energy harvesting (EH) IoT devices. In view of the limited working efficiency of a single UAV, we propose a multi-UAV-enabled WPCN system. In order to further improve the efficiency of the system, we jointly optimize the data transmission performance of the network and the average harvested energy of IoT devices by designing the trajectories of the UAVs. The high mobility of UAVs causes the rapid change of channels, which leads to limitations of the centralized management scheme. Therefore, the problem is modeled as a multi-agent deep reinforcement learning (MADRL) problem. The scenario of multiple UAVs working collaboratively is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). We develop a multi-agent deep deterministic policy gradient (MADDPG)-based approach to design UAVs' trajectories by adjusting the flying velocity and direction. Simulation results prove that the proposed algorithm far exceeds the benchmark schemes.

关键词： Wireless communication Performance evaluation Federated learning Simulation Markov decision processes Benchmark testing Autonomous aerial vehicles

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EasyCount: Crowd Counting Based on Easy Deployment Using Commodity Wi-Fi

EasyCount: Crowd Counting Based on Easy Deployment Using Com...

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IEEE Conference on Wireless Communications and networking

作者： Sida Ling Jingbo Zhao Zhaoming Lu Xiangming Wen Zhe Xiao Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Recent years have witnessed the great potential of Wi-Fi-based crowd counting technique due to its popularity and non-invasive. However, the existing schemes with high accuracy require labeled data with different numbers of people for a priori learning, which poses an obstacle to the usability of crowd counting systems. Hence in this paper, we propose EasyCount, an easy learning-free multi-environment crowd counting system that only needs one person's prior data for simple calibration. First, we model the reflection signal of an individual as a Gaussian stochastic process. Then, we propose the ratio of dynamic power to noise power (RDN), which establishes a mapping between the autocorrelation function (ACF) of Channel State Information (CSI) amplitude and the number of people. The proposed RDN metric is proportional to the number of people, thus eliminating the dependence on labeled data with different numbers of people. Finally, we design a calibration scheme to obtain the base RDN, which enables our system to overcome the impact of different environments. Extensive experiments show that EasyCount can be easily deployed in different environments, achieving an average accuracy of 93.9 % for 0–6 people counting tasks in multiple environments with only one pair of transceivers, which is comparable to the existing state-of-the-art works.

关键词： Accuracy Noise Stochastic processes Transceivers Reflection Calibration Sensors

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Human Pose Estimation Based on Multi-branch Convolutional Neural network 2

Human Pose Estimation Based on Multi-branch Convolutional Ne...

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2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Zhang, Haochen Xia, Hailun Zhang, Rui Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9781665442763

With the development of the convolutional neural network, significant progress has been made in human pose estimation. Recent human pose estimation approaches adopt a single branch network, which can only provide limited surveillance information. Aiming at the problem, we propose to adopt a multi-branch network for both 2D human pose estimation and 3D human pose estimation. Leading results on multiple datasets verify the effectiveness of our multi-branch network. © 2022 IEEE.

关键词： Convolutional neural networks

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