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A Novel Framework for User Positioning and Environment Sensing During Initial Random Access

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1193-1206页

作者： Che, Jingze Zhang, Zhaoyang Zhang, Hao Yang, Zhaohui Liu, Lei Huang, Chongwen Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China

Initial random access is a crucial process in wireless communication networks, which sets up reliable connections between the base station (BS) and multiple active users. In this procedure, useful connection information can be naturally obtained to achieve user positioning, and the channel state information (CSI) of multiple users can be further exploited to realize environment sensing. On the other hand, environment sensing is highly related to user positioning as it requires user-specific CSI and benefits from multi-view observations from different user positions. Therefore, in this paper, we propose a joint initial random access, environment sensing, and user positioning framework. Specifically, oversampled cyclic prefixes (CPs) in orthogonal frequency division multiplexing (OFDM) systems, which contain rich environmental information, can be exploited to achieve enhanced channel estimation. Environment sensing and user positioning are further implemented based on the channel estimation results, and the scatter points are then clustered to reconstruct the environment objects. The simulation results show that the proposed framework can achieve a decimeter-level accuracy and a reconstruction ratio of about 89% for user positioning and environment sensing.

关键词： Sensors Channel estimation Wireless communication OFDM Communication systems Wireless sensor networks Synchronization Standards Simulation Scattering Initial random access user positioning environment sensing

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Frequency-Domain Distributed Multitask Learning Over Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 31619-31632页

作者： Chen, Zefeng Liu, Ying Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China

Distributed multitask learning, which aims to estimate multiple parameter vectors in a distributed and collaborative manner, has been applied to a wide range of Internet of Things (IoT). However, in many practical applications, such as channel impulse responses (CIRs) estimation and adaptive line enhancer (ALE), the parameter vectors of interest can be of large size, resulting in high computational cost. Frequency-domain (FD) learning that utilizes efficient fast Fourier transform (FFT) may be a good candidate to reduce computational complexity. Moreover, benefited from the prewhitening effect caused by the orthogonality property of FFT, the estimation performance can be improved. However, the existing research on FD learning mainly focuses on the single-task scenarios, which is not applicable to multitask scenarios. Considering this, in this article, we first formulate the FD distributed multitask learning (FD-DMTL) problem, and then propose a FD-DMTL algorithm. Through performing numerical simulations and practical applications to the CIRs estimation and smart grid state estimation, it is observed that the proposed algorithm exhibits lower computational complexity and better estimation performance compared with the existing time-domain distributed algorithms.

关键词： Distributed optimization frequency domain Internet of Things (IoT) multitask learning Distributed optimization frequency domain Internet of Things (IoT) multitask learning

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Ordinal Regression for Direction-Related Anomaly Detection

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第5期35卷 6821-6834页

作者： Tu, Jiankai Liu, Huan Li, Chunguang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China

Anomaly detection is widely used in many fields to reveal the abnormal process of a system. Typical model-based anomaly detection methods work well in general anomaly detection problems. However, in some application-specific scenarios, the anomalies of interest are "direction-related," that is, only deviation in certain directions of the data space is abnormal. Most existing anomaly detection methods do not work well in these scenarios, especially when there is no abnormal data information during training. Considering that in many real anomaly detection applications such as medical disease detection and industrial device faults diagnosis, the normal data have several ordinal levels, and the anomalies can be regarded as an unseen level distributed roughly along the ordinal direction outside the normal levels. Notice that the ordinal information is inherently "direction-related," and we can use the ordinal information to assist in finding a "direction-related" boundary for the normal data to detect anomalies of interest. A typical type of methods utilizing the ordinal information is ordinal regression. However, to the best of our knowledge, the existing ordinal regression methods are unable to be directly applied to anomaly detection. In this article, to detect the aforementioned "direction-related" anomalies, we propose an ordinal regression algorithm for direction-related anomaly detection (ORAD). Specifically, we first formulate ORAD as an optimization problem. Then, we apply the difference of convex functions (DC) programming to solve the problem to obtain a "direction-related" boundary. After that, we calculate the outlier scores based on the deviation from the boundary. Theoretically, we analyze the ordinal properties and the convergence of ORAD. We carry out experiments on both synthetic data and real datasets to demonstrate the effectiveness of the proposed ORAD.

关键词： Anomaly detection difference of convex functions (DC) programming direction-related ordinal regression supervised information

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Achievable Rates of Generalized Linear Systems With Orthogonal/Vector AMP Receiver

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IEEE TRANSACTIONS ON SIGNAL procESSING 2023年 71卷 4116-4133页

作者： Liu, Lei Chi, Yuhao Li, Ying Zhang, Zhaoyang Zhejiang Univ Coll Informat Sci & Elect Engn Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310007 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xidian 710071 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310007 Zhejiang Peoples R China

In signal processing and wireless communications, the generalized linear system (GLS) has been widely used to evaluate the impact of nonlinear preprocessing on receiver performance. Although generalized approximation message passing (GAMP) is a state-of-the-art algorithm for GLS signal recovery, it is only applicable to measurement matrices with independent and identically distributed (IID) elements. To overcome this limitation, the generalized orthogonal/vector approximate message passing (GOAMP/GVAMP) algorithm for unitarily invariant measurement matrices was developed and shown to be replica Bayes optimal in uncoded GLS. However, due to its complicated state evolution (SE), the information-theoretic limit of GOAMP/GVAMP remains an open problem for arbitrary input distributions. In this paper, we provide a rigorous achievable rate analysis of GOAMP/GVAMP in GLS, establishing its information-theoretic limit (i.e., maximum achievable rate). Specifically, GOAMP/GVAMP involves a dual-input-dual-output linear detector (LD) paired with two nonlinear detectors (NLD), making the achievable rate difficult to analyze. To address this issue, we convert the fully unfolded state evolution (SE) of a multi-layer iterative algorithm into an equivalent single-input single-output variational SE (VSE). The achievable rate of GOAMP/GVAMP is calculated using the VSE and the mutual information and minimum mean square error (I-MMSE) lemma. Also, the optimal coding principle for maximizing the achievable rate is provided. Furthermore, the methods presented in the paper can be easily extended to analyze the achievable rate and optimal code design for high-dimensional neural networks using multi-layer AMP-type algorithms. Clipping and quantization, two common nonlinear functions, are used as examples to show the advantages of GOAMP/GVAMP over existing techniques, such as the maximum ratio combining receivers based on the linearized model and the cascading GOAMP/GVAMP with separate LD and N

关键词： Generalized linear system generalized approximate message passing (GAMP) generalized orthogonal/vector AMP achievable rate analysis unitarily invariant arbitrary input distribution

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From Data-Driven Learning to Physics-Inspired Inferring: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第7期23卷 7186-7199页

作者： Xiao, Zhuoran Zhang, Zhaoyang Chen, Zirui Yang, Zhaohui Huang, Chongwen Chen, Xiaoming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310007 Peoples R China

In this paper, we propose an innovative learning-based channel prediction scheme so as to achieve higher prediction accuracy and reduce the requirements of huge amounts and strict sequential format of channel data. Inspired by the idea of the neural ordinary differential equation (Neural ODE), we first prove that the channel prediction problem can be modeled as an ODE problem with a known initial value by analyzing the physical process of electromagnetic wave propagation within a mobile environment. Then, we design a novel physics-inspired spatial channel gradient network (SCGnet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving. With the SCGnet, the static channel at any location served by the base station is accurately inferred through consecutive propagation and integration. Finally, we design an efficient recurrent positioning algorithm based on some prior knowledge of user mobility to obtain the velocity vector and propose an approximate Doppler compensation method to make up the instantaneous angular-delay domain channel. Only discrete historical channel data is needed for the training, whereas only a few fresh channel measurements are needed for the prediction, which ensures the scheme's practicability. Comprehensive evaluations show that the proposed scheme is most efficient in representing, learning, and predicting mobile wireless channels.

关键词： Wireless communication Learning systems Channel estimation Predictive models Area measurement Training Physics MIMO machine learning channel prediction neural ODE physics-inspired learning

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ARIoU: Anchor-free Rotation-decoupling IoU-based optimization for 3D object detection

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NEUROCOMPUTING 2024年 594卷

作者： Wen, Chenyiming Sheng, Hualian Zhao, Ming-Min Zhao, Min-Jian Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China

The rotation-decoupling strategy was developed in outdoor 3D object detection with certain performance improvement. However, its anchor-based architecture limits its further improvement in indoor 3D object detection. In this study, we propose an Anchor-free Rotation-decoupling IoU-based optimization, termed ARIoU, which is specifically devised to indoor 3D object detection. A pivotal aspect of our approach is a customized encoding-decoding scheme that integrates the anchor-free framework with the rotation decoupling strategy to mitigate the negative effect caused by rotation sensitivity. Specifically, the unwanted length/width/rotation prediction randomness induced by labeled rotation ambiguity is alleviated. Furthermore, we plug our ARIoU as the optimization targets in both classification and regression branches to solve the misalignment issue in predicting precision and confidence estimation. Experiments on the SUN RGB-D and S3DIS datasets demonstrate the effectiveness of our approach. Code is available at https://***/wenchened/ARIOU.

关键词： 3D object detection Point cloud Anchor-free detector

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GNN-IR: An Intelligent Routing Method Based on Graph Neural Network in the Underwater Acoustic Sensor Network

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IEEE SENSORS JOURNAL 2024年第13期24卷 21566-21582页

作者： Zhang, Shuyun Chen, Huifang Xie, Lei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China

The underwater acoustic sensor network (UASN) is crucial for monitoring marine environments and detecting underwater targets. Reliable networking, especially routing technique, is essential in the UASN due to the challenges of underwater acoustics. In this article, an intelligent routing method based on graph neural network (GNN), named as the GNN-IR method, is proposed to address complex network topology and adapt to dynamic network environment. The proposed GNN-IR method leverages the GNN's ability to process non-Euclidean data and its strong predictive capability. It integrates the UASN's characteristics by building a model of network state prediction and designing the corresponding functions. Additionally, a prediction validity period mechanism in the GNN-IR method is designed to balance packet delivery ratio (PDR) and network energy efficiency. Simulations show that the GNN-IR method performs well in terms of effective data transmission, long network lifetime, and low additional routing information overhead. Furthermore, the proposed routing method is demonstrated an enhanced adaptability and flexibility in the complex underwater environment.

关键词： Routing Sensors Heuristic algorithms Sensor phenomena and characterization Topology Predictive models Prediction algorithms Graph neural network (GNN) intelligent routing method message passing neural network (MPNN) underwater acoustic sensor network (UASN)

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Soft Actor-Critic-Based Multi-User Multi-TTI MIMO Precoding in Multi-Modal Real-Time Broadband Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 18286-18301页

作者： Huang, Yingzhi Chi, Kaiyi Yang, Qianqian Yang, Zhaohui Zhang, Zhaoyang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China

The next-generation wireless network is envisioned to support real-time broadband communication (RTBC) to provision services for immersive applications. Such applications (e.g., virtual reality, VR) usually need to simultaneously transmit multi-modal (e.g., visual, audio and haptic) data streams that have different traffic characteristics and transmission requirements, within multiple transmission time intervals (TTIs). In this paper, we formulate an optimization problem of multi-user multiple-input multiple-output (MIMO) precoding within multiple TTIs for multi-modal data transmission. As it is hard to find an optimal solution, we first resort to a novel soft actor-critic (SAC)-based learning approach. Specifically, a lightweight reinforcement learning architecture is employed to learn the adaptive priority weight of each user within multiple TTIs by taking into account its remaining multi-modal data amount and dynamic interaction state. The learned priority weights are then input to an iterative weighted minimum mean-square error (WMMSE) algorithm to adjust the precoder matrix and user transmission rates. With a scalable state design, the proposed algorithm can be tailored to different numbers of potential or active users. We also provide another practical solution to the formulated multi-TTI precoding problem, which transforms the problem into a single-TTI optimization problem by adding the quality-of-service (QoS) constraints into the traditional WMMSE problem and then solves it using the alternating direction method of multipliers (ADMM). Simulation results demonstrate the robustness and efficiency of the proposed algorithms, which show that the SAC-based precoding algorithm can achieve a 50.0% increment in system capacity compared to traditional WMMSE and a significant reduction in time complexity compared to the QoS-constrained WMMSE algorithm.

关键词： Multiple-input multiple-output (MIMO) multi-TTI precoding soft actor-critic (SAC) soft actor-critic (SAC) multi-TTI precoding multi-modal multi-modal real-time broadbandcommunication (RTBC) multi-modal

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Memory AMP for Generalized MIMO: Coding Principle and information-Theoretic Optimality

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第6期23卷 5769-5785页

作者： Chen, Yufei Liu, Lei Chi, Yuhao Li, Ying Zhang, Zhaoyang Xidian Univ State Key Lab Integrated Serv Networks Xidian 710071 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310007 Zhejiang Peoples R China

To support complex communication scenarios in next-generation wireless communications, this paper focuses on a generalized MIMO (GMIMO) with practical assumptions, such as massive antennas, practical channel coding, arbitrary input distributions, and general right-unitarily-invariant channel matrices (covering Rayleigh fading, certain ill-conditioned and correlated channel matrices). The orthogonal/vector approximate message passing (OAMP/VAMP) receiver has been proved to be information-theoretically optimal in GMIMO, but it is limited to high-complexity linear minimum mean-square error (LMMSE). To solve this problem, a low-complexity memory approximate message passing (MAMP) receiver has recently been shown to be Bayes optimal but limited to uncoded systems. Therefore, how to design a low-complexity and information-theoretically optimal receiver for GMIMO is still an open issue. To address this issue, this paper proposes an information-theoretically optimal MAMP receiver and investigates its achievable rate analysis and optimal coding principle. Specifically, due to the long-memory linear detection, state evolution (SE) for MAMP is intricately multi-dimensional and cannot be used directly to analyze its achievable rate. To avoid this difficulty, a simplified single-input single-output (SISO) variational SE (VSE) for MAMP is developed by leveraging the SE fixed-point consistent property of MAMP and OAMP/VAMP. The achievable rate of MAMP is calculated using the VSE, and the optimal coding principle is established to maximize the achievable rate. On this basis, the information-theoretic optimality of MAMP is proved rigorously. Furthermore, the simplified SE analysis by fixed-point consistency is generalized to any two iterative detection algorithms with the identical SE fixed point. Numerical results show that the finite-length performances of MAMP with practical optimized low-density parity-check (LDPC) codes are 0.5 similar to 2.7 dB away from the associated constra

关键词： Memory approximate message passing (MAMP) generalized MIMO (GMIMO) low complexity capacity optimality coding principle orthogonal/vector approximate message passing (OAMP/VAMP)

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Joint Uplink and Downlink Channel Estimation for mmWave Massive MIMO Systems With Asymmetrical Transceiver Architecture

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第6期13卷 1556-1560页

作者： Liu, Yongcheng Cheng, Pengyuan Li, Min Zhao, Ming-Min Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China

An asymmetrical fully-digital transceiver architecture has recently demonstrated enhanced energy efficiency for millimeter-wave (mmWave) massive MIMO systems. It achieves high beamforming gain by utilizing all transmit antennas and RF chains in downlink transmission, while reducing the number of receive RF chains and antennas for uplink reception to lower hardware costs. However, this asymmetry poses challenges in downlink channel estimation due to the discrepancy in uplink and downlink channel dimensions. To address this issue, we propose a Sparse Bayesian Learning-based Joint Uplink and Downlink Channel Estimation (SBL-JUDCE) approach. SBL-JUDCE directly estimates the downlink channel by padding the uplink training signal vector to account for deactivated receive antennas. Leveraging the sparsity of the mmWave channel, we formulate the downlink channel estimation as a compressive sensing problem using a 0-1 masking matrix. We develop an SBL framework to solve this problem effectively. Simulation results demonstrate the superiority of SBL-JUDCE over a two-step approach, where the uplink channel is estimated first and then used to infer the downlink channel, in terms of channel estimation error and spectral efficiency for downlink transmission with the estimated channel.

关键词： Channel estimation Uplink Downlink Radio frequency Array signal processing Millimeter wave communication Antennas Asymmetrical transceiver channel estimation massive MIMO millimeter-wave communications

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