Weijie Yuan
Verified @sustech.edu.cn
Department of Electronic and Electrical Engineering
Southern University of Science and Technology
Scopus Publications
Scopus Publications
Haifeng Wen, Weijie Yuan, Chau Yuen, and Yonghui Li
Institute of Electrical and Electronics Engineers (IEEE)
Yunxin Li, Fan Liu, Zhen Du, Weijie Yuan, Qingjiang Shi, and Christos Masouros
Institute of Electrical and Electronics Engineers (IEEE)
The emergence of the fifth-generation (5G) New Radio (NR) technology has provided unprecedented opportunities for vehicle-to-everything (V2X) networks, enabling enhanced quality of services. However, high-mobility V2X networks require frequent handovers and acquiring accurate channel state information (CSI) necessitates the utilization of pilot signals, leading to increased overhead and reduced communication throughput. To address this challenge, integrated sensing and communications (ISAC) techniques have been employed at the base station (gNB) within vehicle-to-infrastructure (V2I) networks, aiming to minimize overhead and improve spectral efficiency. In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery. These new frame structures employ 75% fewer pilots and reduce reference signals by 43.24%, capitalizing on the sensing capability of ISAC signals. Through extensive link-level simulations, we demonstrate that our proposed approach enables faster beam establishment during initial access, higher throughput and more precise beam tracking in connected mode with reduced overhead, and expedited detection and recovery from beam failures. Furthermore, the numerical results obtained from our simulations showcase enhanced spectrum efficiency, improved communication performance and minimal overhead, validating the effectiveness of the proposed ISAC-based techniques in NR V2I networks.
Muhammad Umar Farooq Qaisar, Weijie Yuan, Paolo Bellavista, Fan Liu, Guangjie Han, Rabiu Sale Zakariyya, and Adeel Ahmed
Institute of Electrical and Electronics Engineers (IEEE)
Lyuxiao Yang, Nan Wu, Yifeng Xiong, Weijie Yuan, Bin Li, Yonghui Li, and Arumugam Nallanathan
Institute of Electrical and Electronics Engineers (IEEE)
—Fingerprint-based indoor localization holds great potential for the Internet of Things. Despite numerous studies focusing on its algorithmic and practical aspects, a notable gap exists in theoretical performance analysis in this domain. This paper aims to bridge this gap by deriving several lower bounds and approximations of mean square error (MSE) for fingerprint-based localization. These analyses offer different complexity and accuracy trade-offs. We derive the equivalent Fisher information matrix and its decomposed form based on a wireless propagation model, thus obtaining the Cram ´ er-Rao bound (CRB). By approximating the Fisher information provided by constraint knowledge, we develop a constraint-aware CRB. To more accurately characterize nonlinear transformation and constraint information, we introduce the Ziv-Zakai bound (ZZB) and modify it for adapt deterministic parameters. The Gauss–Legendre quadrature method and the trust-region reflective algorithm are employed to make the calculation of ZZB tractable. We introduce a tighter extrapolated ZZB by fitting the quadrature function outside the well-defined domain based on the Q-function. For the constrained maximum likelihood estimator, an approximate MSE expression, which can characterize map constraints, is also developed. The simulation and experimental results validate the effectiveness of the proposed bounds and approximate MSE.
Dongkai Zhou, Jing Guo, Siqiang Wang, Zhong Zheng, Zesong Fei, Weijie Yuan, and Xinyi Wang
Institute of Electrical and Electronics Engineers (IEEE)
Junsheng Mu, Yuanhao Cui, Wenjiang Ouyang, Zhaohui Yang, Weijie Yuan, and Xiaojun Jing
Institute of Electrical and Electronics Engineers (IEEE)
Zhongqing Wu, Xuehua Li, Yuanxin Cai, and Weijie Yuan
Institute of Electrical and Electronics Engineers (IEEE)
Shihang Lu, Fan Liu, Yunxin Li, Kecheng Zhang, Hongjia Huang, Jiaqi Zou, Xinyu Li, Yuxiang Dong, Fuwang Dong, Jia Zhu,et al.
Institute of Electrical and Electronics Engineers (IEEE)
Xiang Li and Weijie Yuan
Institute of Electrical and Electronics Engineers (IEEE)
Jinjin Yan, Youbing Zheng, Jinquan Yang, Lyudmila Mihaylova, Weijie Yuan, and Fuqiang Gu
Wiley
AbstractSimultaneous localization and mapping (SLAM) is required in many areas and especially visual‐based SLAM (VSLAM) due to the low cost and strong scene recognition capabilities conventional VSLAM relies primarily on features of scenarios, such as point features, which can make mapping challenging in scenarios with sparse texture. For instance, in environments with limited (low‐even non‐) textures, such as certain indoors, conventional VSLAM may fail due to a lack of sufficient features. To address this issue, this paper proposes a VSLAM system called visual SLAM that can adaptively fuse point‐line‐plane features (PLPF‐VSLAM). As the name implies, it can adaptively employ different fusion strategies on the PLPF for tracking and mapping. In particular, in rich‐textured scenes, it utilizes point features, while in non‐/low‐textured scenarios, it automatically selects the fusion of point, line, and/or plane features. PLPF‐VSLAM is evaluated on two RGB‐D benchmarks, namely the TUM data sets and the ICL_NUIM data sets. The results demonstrate the superiority of PLPF‐VSLAM compared to other commonly used VSLAM systems. When compared to ORB‐SLAM2, PLPFVSLAM achieves an improvement in accuracy of approximately 11.29%. The processing speed of PLPF‐VSLAM outperforms PL(P)‐VSLAM by approximately 21.57%.
Yao Xu, Zhen Du, Weijie Yuan, Shaobo Jia, and Victor C. M. Leung
Institute of Electrical and Electronics Engineers (IEEE)
Jun Wu, Weijie Yuan, and Lajos Hanzo
Institute of Electrical and Electronics Engineers (IEEE)
The real-time unmanned aerial vehicle (UAV) trajectory design of secure integrated sensing and communication (ISAC) is optimized. In particular, the UAV serves both as a downlink transmitter and a radar receiver. The legitimate user (Bob) roams on ground through a series of unknown locations, while the eavesdropper moves following a fixed known trajectory. To maximize the real-time secrecy rate, we propose an extended Kalman filtering (EKF)-based method for tracking and predicting Bob's location at the UAV based on the delay measurements extracted from the sensing echoes. We then formulate a non-convex real-time trajectory design problem and develop an efficient iterative algorithm for finding a near optimal solution. Our numerical results demonstrate that the proposed algorithm is capable of accurately tracking Bob and strikes a compelling legitimate vs. leakage rate trade-off.
Jun Wu, Weijie Yuan, and Lin Bai
Institute of Electrical and Electronics Engineers (IEEE)
Zhongjie Li, Weijie Yuan, Buyi Li, Jun Wu, Changsheng You, and Fanke Meng
Institute of Electrical and Electronics Engineers (IEEE)
Yuanhao Cui, Weijie Yuan, Zhiyue Zhang, Junsheng Mu, and Xinyu Li
Institute of Electrical and Electronics Engineers (IEEE)
The digital twin (DT), which effectively represents the actual real-world physical system or process, has reshaped the classic manufacturing, construction, as well as healthcare industry. As for realizing DT, both sensing and communication functionalities are demanded, which fully builds the connectivity between the physical world and the digital world. We first conducted a survey on the current situation of DT combined with communication and sensing. Inspired from this survey and the current development of communication and sensing, in this paper, we attempt to study the communication annd sensing technologies of physical layer in DT, to reduce the hardware and spectrum overhead. First, we studied the degree of freedom (DoF) problem in general communication and sensing system, and contribute to the DoF definition in the sensing system. Then, in order to improve the spectrum efficiency in DT system, we proposed an iterative optimization framework to address the coexistence of communication and sensing, and some examples are provided. Finally, in order to pursue a better integration gain, we proposed a new waveform design method based on DoF completion. The proposed optimization method can achieve the mean square error (MSE) lower bound. Simulation results demonstrate the effectiveness of various problems in the above scenarios.
Xiao Meng, Fan Liu, Christos Masouros, Weijie Yuan, Qixun Zhang, and Zhiyong Feng
Institute of Electrical and Electronics Engineers (IEEE)
In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling.Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain through dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account.Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only based and ISAC kinematic-only based technique in the tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.
Yuanhao Cui, Jiali Nie, Tiankuo Yu, Jiaqi Zou, Weijie Yuan, Zexuan Jing, Junsheng Mu, and Xiaojun Jing
ACM
This paper introduces a sensing-assisted communication method, which relies on the extraction of multi-modal features. Multi-modal data, e.g. vision, radar, lidar, and position are employed as the input data of the proposed beamforming method. The recognition and beamforming accuracy are therefore improved. Initially, the 3D-Conv model is utilized to extract features from the encoded multimodal data. Subsequently, the generative pre-trained transformer (GPT) is employed to grasp correlations across diverse models and fuse their latent features. These fusion features are used to facilitate beam prediction, thereby approximating the optimal beam index for real-world data. Experimental results based on real-world data validate the effectiveness of our approach, achieving an accuracy of 85%, surpassing traditional single-modal schemes by over 25%.
Zhongjie Li, Weijie Yuan, Qinghua Guo, Nan Wu, and Ji Zhang
Institute of Electrical and Electronics Engineers (IEEE)
Orthogonal time frequency space (OTFS) technique, which modulates data symbols in the delay-Doppler (DD) domain, presents a potential solution for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing (UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model (HMM). The empirical state evolution (SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm, we derive the update criterion for the hyperparameters through the expectation-maximization (EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.
Junsheng Mu, Wenjiang Ouyang, Tao Hong, Weijie Yuan, Yuanhao Cui, and Zexuan Jing
Institute of Electrical and Electronics Engineers (IEEE)
Kecheng Zhang, Zhongjie Li, Weijie Yuan, Yunlong Cai, and Feifei Gao
Institute of Electrical and Electronics Engineers (IEEE)
By multiplexing information symbols in the delay-Doppler (DD) domain, orthogonal time frequency space (OTFS) is a promising candidate for future wireless communication in high-mobility scenarios. In addition to the superior communication performance, OTFS is also a natural choice for radar sensing since the primary parameters (range and velocity of targets) in radar signal processing can be inferred directly from the delay and Doppler shifts. Though there are several works on OTFS radar sensing, most of them consider the integer parameter estimation only, while the delay and Doppler shifts are usually fractional in the real world. In this paper, we propose a two-step method to estimate the fractional delay and Doppler shifts. We first perform the two-dimensional (2D) correlation between the received and transmitted DD domain symbols to obtain the integer parts of the parameters. Then a difference-based method is implemented to estimate the fractional parts of delay and Doppler indices. Meanwhile, we implement a target detection method based on a generalized likelihood ratio test since the number of potential targets in the sensing scenario is usually unknown. The simulation results show that the proposed method can obtain the delay and Doppler shifts accurately and get the number of sensing targets with a high detection probability.
Xiaoqi Zhang, Haifeng Wen, Ziyu Yan, Weijie Yuan, Jun Wu, and Zhongjie Li
MDPI AG
A vehicular network embodies a specialized variant of wireless network systems, characterized by its capability to facilitate inter-vehicular communication and connectivity with the encompassing infrastructure. With the rapid development of wireless communication technology, high-speed and reliable communication has become increasingly important in vehicular networks. It has been demonstrated that orthogonal time frequency space (OTFS) modulation proves effective in addressing the challenges posed by high-mobility environments, as it transforms the time-varying channels into the delay-Doppler domain. Motivated by this, in this paper, we focus on the theme of integrated sensing and communication (ISAC)-assisted OTFS receiver design, which aims to perform sensing channel estimation and communication symbol detection. Specifically, the estimation of the sensing channel is accomplished through the utilization of a deep residual denoising network (DRDN), while the communication symbol detection is performed by orthogonal approximate message passing (OAMP) processing. The numerical results demonstrate that the proposed ISAC system exhibits superior performance and robustness compared to traditional methods, with a lower complexity as well. The proposed system has great potential for future applications in wireless communication systems, especially in challenging scenarios with high mobility and interference.
Yifeng Xiong, Fan Liu, Yuanhao Cui, Weijie Yuan, Tony Xiao Han, and Giuseppe Caire
Institute of Electrical and Electronics Engineers (IEEE)
Haifeng Wen, Weijie Yuan, Zilong Liu, and Shuangyang Li
Institute of Electrical and Electronics Engineers (IEEE)
Chang Liu, Shuangyang Li, Weijie Yuan, Xuemeng Liu, and Derrick Wing Kwan Ng
Institute of Electrical and Electronics Engineers (IEEE)
This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent reliability performance, pragmatic precoder design is an effective and indispensable solution. However, the design requires accurate instantaneous channel state information at the transmitter (ICSIT) which is not always available in practice. Motivated by this, we adopt a deep learning (DL) approach to exploit implicit features from estimated historical delay-Doppler domain channels (DDCs) to directly predict the precoder to be adopted in the next time frame for minimizing the frame error rate (FER), that can further improve the system reliability without the acquisition of ICSIT. To this end, we first establish a predictive transmission protocol and formulate a general problem for the precoder design where a closed-form theoretical FER expression is derived serving as the objective function to characterize the system reliability. Then, we propose a DL-based predictive precoder design framework which exploits an unsupervised learning mechanism to improve the practicability of the proposed scheme. As a realization of the proposed framework, we design a DDCs-aware convolutional long short-term memory (CLSTM) network for the precoder design, where both the convolutional neural network and LSTM modules are adopted to facilitate the spatial-temporal feature extraction from the estimated historical DDCs to further enhance the precoder performance. Simulation results demonstrate that the proposed scheme facilitates a flexible reliability-latency tradeoff and achieves an excellent FER performance that approaches the lower bound obtained by a genie-aided benchmark requiring perfect ICSI at both the transmitter and receiver.
Zhongjie Li, Weijie Yuan, Zexuan Jing, Junsheng Mu, Nan Wu, and Zhiyun Lin
ACM
In this paper, we study uplink transmission for reconfigurable intelligent surfaces (RIS)-aided orthogonal time frequency space (OTFS) systems to achieve real-time communications and sensing in high-mobility scenarios, which is the urgent requirement for various future networks like Internet of Things (IoT) and Metaverse. To this end, we first propose an efficient and reliable transmission scheme which utilizes the delay-Doppler information in OTFS to facilitate the configuration of RIS. Specifically, the proposed scheme exploits the estimated delay and Doppler shifts of the cascaded channel to sense the user, and the sensing parameters are then used for RIS passive beamforming. It is noteworthy that we estimate the channel state information (CSI) by employing only one OTFS frame and configure the RIS based on the predicted channel parameters, leading to substantially reduced channel training overhead and more real-time RIS configuration. To obtain the essential information for channel information sensing, we then propose a low-complexity algorithm which determines the Doppler and delay shifts of the channel between the user and RIS based on the mapping relationship of the delay-Doppler pairs. With the delay-Doppler information in hand, a user tracking scheme relying on extended Kalman filter (EKF) are then presented to track the user and obtain the spatial angle information. By making use of the channel parameters acquired at the base station (BS), the RIS reflection vector is designed to maximize the achievable rate. The results obtained from the simulation experiments affirm the efficacy of the proposed scheme, thereby confirming its capability to attain efficient communications and sensing under high Doppler channels.