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Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

China Communications 2024年第3期21卷 51-65页

作者： Liu Jinru Tian Yongqing Liu Danpu Zhang Zhilong Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijing100876China

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high ***,channel estimation has become very challenging due to the use of massive MIMO antenna ***,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle *** the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational ***,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power ***,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel *** address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this *** proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference *** specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation *** results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.

关键词： channel estimation compressed sensing inter-path interference millimeter wave massive MIMO off-grid parallel interference cancellation

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Joint antenna position and transmit signal optimization for ISAC system with movable antenna array 35

Joint antenna position and transmit signal optimization for ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Xiang, Wan Chen, Yawen Zhang, Xuanrui Lu, Zhaoming Wen, Xiangming Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350362244

This paper studies an integrated sensing and communication (ISAC) system with movable antennas (MAs), where the position of antennas can be flexibly adjusted to reshape the wireless channel. We aim at jointly optimizing the positions of MAs, beamforming of the communication signal, and the covariance of the sensing signal to maximize the overall transmit beampattern gain while ensuring the quality of communication service requirement. Considering the non-convexity of this optimization problem, we propose an efficient algorithm to obtain a high-quality solution by using alternating optimization, semi-definite relaxation (SDR) and Taylors theorem. The simulation results show that the proposed algorithm significantly improves the beampattern gain. © 2024 IEEE.

关键词： Beamforming

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Mobility-Aware AR Computation Tasks Offloading Based on Offline RL over Terahertz system

Mobility-Aware AR Computation Tasks Offloading Based on Offl...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Zhao, Shuyue Jing, Wenpeng Wer, Xiangming Lu, Zhaoming Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350370218

Augmented reality (AR) technology is increasingly applied in the Internet of vehicles (IoV). As it poses significant challenges to the communication and computation capabilities of AR terminal devices, this paper investigates combining terahertz (THz) and mobile edge computing (MEC) integrated with reinforcement learning (RL) to optimize the performance of AR services for the IoV. Though the RL-based MEC offloading has been validated to have excellent performance, it has an intrinsic disadvantage where its training phase needs inappropriate offloading strategies to be tested frequently and poses risks to safe driving. Moreover, although THz communication can provide high transmission data rates, its characteristics that different transmission distances lead to different available bandwidth, pose a unique challenge for the communication resources allocation. Motivated by these, we propose a novel static data-driven AR tasks offloading framework to solve resources allocation problem for the IoV with THz communication. To deal with the challenge that different users have different available THz bandwidth, we propose a distance-aware fair THz bandwidth allocation algorithm. Moreover, the offline RL, instead of online RL, is utilized to train the AR task offloading decision agent, where interaction with the real world is not required, completely safeguarding the system's safety. To address the typical issue of out-of-distribution (ODD) actions encountered in offline RL, we adopt conservative Q-learning (CQL) to train and obtain the optimal AR computation task offloading strategy to enhance users' experience. Simulation results show that the proposed solution outperforms state-of-the-art algorithms in terms of algorithm stability and user reward. © 2023 IEEE.

关键词： augmented reality Mobile edge computing offloading mobility awareness offline reinforcement learning terahertz communication

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keypoint-Restored Inverse Kinematic Solver 9

Keypoint-Restored Inverse Kinematic Solver

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9th International Conference on Computer and Communications, ICCC 2023

作者： Ni, Yongkang Wu, Muqing Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (纸本)9798350317251

Model-based 3D human body reconstruction provides a remarkable perspective for generating realistic full-body mesh in mesh-based 3D pose estimation. However, different strategies for capturing the nonlinear attributes of human model have varying effects on the efficiency and accuracy of recovering 3D human body. In this paper, our objective is to develop a simple and credible 3D human body parameter solver for skinned multi-person linear model (SMPL). To achieve this, we propose a keypoint-restored inverse kinematic solver (KRIKS), composed of inverse kinematics (IK) and iterative optimization, which transforms 3D keypoints to actual human shape and pose with mesh form. Additionally, considering the limitation of IK calculation and the relationship across pose frames, we extend our approach from single-frame analysis to video sequence in seq2frame manner. This integration leverages the temporal coherence of continuous actions, further enhancing the reconstruction performance. We demonstrate quantitative KRIKS baselines on the 3DPW and AMASS datasets, achieving impressive results with 0mm MPBE, 0.18mm PMPJPE, and 11.28mm MPVE on the 3DPW dataset. © 2023 IEEE.

关键词： Inverse kinematics

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Super-Resolution Enhanced Video Streaming Based on Cloud-Edge Cooperation for Satellite Backhauls

Super-Resolution Enhanced Video Streaming Based on Cloud-Edg...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Liu, Changhao Jing, Wenpeng Zheng, Ziyuan 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 Beijing China

ISBN: (纸本)9798350370218

Although satellite backhauls facilitate expanding network coverage and providing capacity in unconnected areas, delivering high-quality videos via satellite backhauls is challenging due to high delay, bandwidth limitations and high expenses. Super-resolution (SR) technique has been proposed to integrate into streaming systems to improve Quality of Experience (QoE) in bandwidth-limited environments. However, existing SR-enhanced streaming systems face two key challenges: 1) compatibility problem that legacy video servers and user equipment (UEs) must be modified to integrate SR-related modules, and 2) quality consistency problem that enhanced video quality fluctuates across chunks. Motivated by this, we present SatOL, a novel SR-enhanced video streaming system for satellite-backhauled networks. Specifically, SatOL is designed on a cloud-edge cooperation framework, where a cloud server and an edge server are responsible for SR model training and SR-based video enhancement, respectively. This framework operates as an intermediate hop within end-to-end streaming sessions to perform SR-related processing, avoiding modifications to legacy video servers and UEs. Instead of training video-tailored SR models offline, the cloud server continually trains chunk-tailored SR models during video streaming, which are overfitted to corresponding chunks and provide consistent quality enhancement. To avoid rebuffering caused by time-consuming model training, an online fine-tuning method is proposed to significantly expedite SR model training. To further improve online training efficiency, we propose a decision mechanism that determines whether and how to train chunk-tailored SR models based on the chunks' 1-frame count. Evaluations show that SatOL outperforms baselines, improving QoE and enhanced video quality by 6-285% and 17-25%, respectively, while reducing bandwidth usage by 60-64%. © 2023 IEEE.

关键词： Cloud-Edge cooperation On-line learning Satellite backhaul Super-Resolution Video streaming

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Beam Tracking Based on Multi-Point Radar Sensing and DNN Prediction 8

Beam Tracking Based on Multi-Point Radar Sensing and DNN Pre...

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8th IEEE International Conference on network Intelligence and Digital Content, IC-NIDC 2023

作者： Chen, Kean Liu, Danpu Zhang, Zhilong Beijing Laboratory of Advanced Information Network China Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9798350317923

Millimeter-Wave (mmWave) communication based on massive MIMO and beamforming has become a hotspot owing to its advantage on capacity. However, a rapidly varying mm Wave channel in high mobility scenario will lead to frequent beam switching and high signaling overhead. Integrated Sensing and Communication (ISAC) gives a new idea to the above challenges by using radar sensing to assist beam management. Nevertheless, most of the sensing -assisted beam tracking are limited to the scenario where Line-of-Sight (LOS) paths exist, and fail when a blockage occurs. To address this issue, a beam tracking scheme based on multi-point radar sensing and Deep Neural network (DNN) is proposed in this paper, where the sensing information from multiple radars are aggregated and a DNN is utilized to predict the beam direction by learning the channel nonlinear correlation in a specific environment. The simulation results indicate that the proposed scheme operates effectively in blockage scenario, and surpasses the baselines in the field of spectral efficiency. © 2023 IEEE.

关键词： Deep neural networks

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Multi-Attention Adaptive Graph Transformer network for Data Inference and Prediction in Vehicular Crowdsensing 27

Multi-Attention Adaptive Graph Transformer Network for Data ...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Huang, Shouyu Wang, Luhan Zheng, Rui Huo, Jie Wen, Xiangming Lu, Zhaoming Beijing University of Posts and Telecommunications Beijing Key Laboratory for Network System Architecture and Convergence Beijing China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798331505929

Vehicular crowdsensing has emerged as an important sensing paradigm in the Internet of Things (IoT), which recruits Intelligent Vehicles to collect data. As its variant, sparse MCS collects data from some sensing areas and infer data for other unsensed areas, addressing the challenge of maintaining high-quality sensing at manageable costs. In practical vehicular crowdsensing scenarios, the diverse requirements of urban applications make it crucial not only to infer data for the current period but also to predict the entire sensing map for the future. This predictive capability not only reduces the cost of sensing but also provides critical data support for various urban applications, including intelligent transportation systems. This paper introduces the Multi-Attention Adaptive Graph Transformer network (MAAGTN) aimed at improving the accuracy of data inference and prediction in vehicular crowdsensing. We present a novel embedding method that integrates spatial-temporal information of sensing maps into the model. An adaptive learnable graph module and a multiattention module are designed to dynamically capture complex spatial-temporal correlations among data. Furthermore, a dynamic multi-task learning framework is proposed to mitigate error propagation by adjusting task weights during training. Experimental evaluations on real-world datasets demonstrate the superiority of MAAGTN for data inference and prediction. © 2024 IEEE.

关键词： Spatio-temporal data

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Multi-IRS Assisted Multi-UAV Wireless Resource Allocation and Deployment Optimization

Multi-IRS Assisted Multi-UAV Wireless Resource Allocation an...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhang, Lianwen Hu, Zhiqun Lu, Zhaoming Zhang, Yujing Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks China

ISBN: (纸本)9798350345384

Unmanned aerial vehicle (UAV) used as the aerial base station, can provide flexible and reliable data transmission service for designated areas, owing to its advantages of mobility, easy deployment and cost-effectiveness. However, the limited endurance of UAV, caused by its small size and payload limitation, significantly affects the quality of UAV base station (BS) network. In this study, we employ multiple intelligent reflecting surfaces (IRSs) to enhance the performance of multi-UAV BS network through collaborative adjustment of the phase shift matrix of the IRS, where UAVs can benefit from all IRSs in the region, leading to a significant increase in the network capacity. We formulate an optimization problem which aims to maximize the downlink sum-rate of the network by optimizing UAV-user association, transmit beamforming at UAVs, phase shifts of IRSs and UAVs' locations. Considering the non-convexity of the original problem, we use Lagrangian dual transform and quadratic transform (QT) techniques to simplify the problem. Then we proposed an algorithm, named SLOTC, which combines successive refinement method, Lagrange multiplier method, oblique manifold method and Taylor-conjugate gradient method, to solve the simplified problem. Our proposed algorithm significantly outperforms existing benchmark schemes, as demonstrated by numerical results. © 2023 IEEE.

关键词： Cost effectiveness

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A Satellite-Terrestrial Integrated network architecture Based on Stateless Core network 9

A Satellite-Terrestrial Integrated Network Architecture Base...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Huang, Shaoxin Wang, Luhan Wen, Xiangming Liu, Yu Niu, Haiwen Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing100876 China

ISBN: (纸本)9798350317251

Comprehensive coverage is a natural advantage of satellites, and loading the functions of base station on satellites can expand the service range of Satellite-Terrestrial Integrated network(STIN), providing users with extensive and efficient access services. However, the management and control capabilities of satellite networks are inadequate, and this aspect has not been well investigated. The signalling from the control plane needs to be routed over the backhaul links to the Terrestrial Core network(TCN) for processing which increases the service latency. In this paper, we propose a novel integrated core network architecture where satellites simultaneously have access and control capabilities by provisioning next-generation NodeB(gNB) and some core network functions. Considering the constrained satellite resources, we design a lightweight Stateless Satellite Core network (SSCN) to enhance the flexibility and stability of the satellite network control. Meanwhile, based on SSCN, we redesign the registration, session establishment, and handover procedures to support mobility management and further reduce service latency. Simulation results show that the proposed integrated core network architecture improves the stability of mobility management and optimizes the network latency compared to the Non-Terrestrial network(NTN) architecture in that satellites act as radio access networks only. © 2023 IEEE.

关键词： network architecture

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Deep Learning Enabled Video Semantic Transmission Against Multi-Dimensional Noise

Deep Learning Enabled Video Semantic Transmission Against Mu...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Niu, Haiwen Wang, Luhan Lu, Zhaoming Du, Keliang Wen, Xiangming Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

ISBN: (纸本)9798350370218

As a novel communication paradigm to achieve efficient end-to-end communication over wireless channels, deep learning (DL) enabled semantic communication system has attracted increasing attention. However, semantic wireless communication systems are affected by multi-dimensional noise, including physical channel noise and semantic noise caused by unstable sources in the real environment. Unfortunately, the joint consideration of physical channel noise and visual semantic noise in video semantic transmission has not been studied yet in the current literature. In this paper, we propose a novel DL-enabled video semantic communication system called DVSC, which employs channel and spatial attention mechanisms to achieve self-adaptation for different channel conditions, mitigating the impacts of varying degrees of physical channel noise on system performance. Additionally, we introduce a feature refinement module based on an encoder-decoder architecture, and a semantic correction module based on a lightweight U-Net structure, which can address semantic ambiguity caused by visual semantic noise at the transceiver. We also design a loss function that considers both pixel and perceptual quality metrics to jointly optimize the DVSC in the pixel and semantic domains. Our extensive experimental results demonstrate that the DVSC can adapt to various channel conditions and outperform benchmark comparison schemes while spending less time on encoding and decoding. To reproduce our results, we make the source code publicly available at https://***/Coding-Cow/***. © 2023 IEEE.

关键词： attention deep learning (DL) semantic communication semantic correction U-net

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