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Generalized spectrum analysis of Chirp Cyclostationary signals associate with linear canonical transform

SIGNAL PROCESSING 2024年 218卷

作者： Miao, Hongxia Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

A chirp cyclostationary (CCS) process is a nonstationary stochastic process, whose statistics have been defined and utilized in radar and communication signal processing. However, spectrum analysis of CCS signals, which provides a spectral view to characterize a signal, has not been performed. Classical spectrum analysis is based on the Fourier transform, which is not suitable for CCS signals because of the multiplicative chirp signals in a CCS model. In this study, we propose a generalized spectral representation of chirp cyclostationary signals associated with a linear canonical transform. The proposed representation shows that a CCS signal can be represented by a linear combination of a series of jointly stationary and equal -bandwidth processes, which bridge the nonstationary and stationary processes. Furthermore, the relationships between the second -order statistics of the CCS process and its stationary representations are introduced. Finally, based on the above observations, a linear time -varying Wiener filter was designed for CCS signal denoising and time -varying channel estimation. Finally, the relationships between the nonstationarity of the input and output and timevarying properties of the systems are discussed, which are helpful for determining the nonstationarity of the outputs or the time -varying property of a system.

关键词： Cyclic Wiener filter Linear chirp signal Linear canonical transform Cyclostationary signals Spectrum decomposition

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An Enhanced Dynamic Ray Tracing Architecture for Channel Prediction Based on Multipath Bidirectional Geometry and Field Extrapolation

An Enhanced Dynamic Ray Tracing Architecture for Channel Pre...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Miao, Yinghe Yu, Li Zhang, Yuxiang Xing, Hongbo Zhang, Jianhua Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing100876 China

ISBN: (纸本)9798350351255

With the development of sixth generation (6G) networks toward digitalization and intelligentization of communications, rapid and precise channel prediction is crucial for the network potential release. Interestingly, a dynamic ray tracing (DRT) approach for channel prediction has recently been proposed, which utilizes the results of traditional RT to extrapolate the multipath geometry evolution. However, both the priori environmental data and the regularity in multipath evolution can be further utilized. In this work, an enhanced-dynamic ray tracing (E-DRT) algorithm architecture based on multipath bidirectional extrapolation has been proposed. In terms of accuracy, all available environment information is utilized to predict the birth and death processes of multipath components (MPCs) through bidirectional geometry extrapolation. In terms of efficiency, bidirectional electric field extrapolation is employed based on the evolution regularity of the MPCs' electric field. The results in a Vehicle-to-Vehicle (V2V) scenario show that E-DRT improves the accuracy of the channel prediction from 68.3% to 94.8% while reducing the runtime by 7.2% compared to DRT. © 2024 IEEE.

关键词： Extrapolation

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Compressed Channel Estimation for Near-Field XL-MIMO Using Triple Parametric Decomposition

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IEEE TRANSACTIONS ON VEHICULAR technology 2023年第11期72卷 15040-15045页

作者： Guo, Xufeng Chen, Yuanbin Wang, Ying Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the presence of the conventional angular-domain representation, the energy leakage effect is evident for the near-field extremely large-scale multiple-input-multiple-output (XL-MIMO) channel, which further leads to ambiguous channel power detection, significantly eroding channel estimation performance. To tackle this challenge, this paper proposes a novel Triple Parametric Decomposition (TPD) framework to facilitate compressed channel estimation in the XL-MIMO system. Specifically, we examine the case of a uniform planar array (UPA) with the geometry parameters involving a pair of azimuth-elevation angles and distances in an attempt to achieve independent decomposition by constructing their own covariance matrices. Then, by utilizing the on- and off-grid techniques, we obtain the sparse recovery of angles and distances information in the absence of energy leakage effect at reduced gridding complexity. Simulation results demonstrate that our proposed TPD achieves significant performance improvement over the state-of-the-art methods.

关键词： Antennas Channel estimation Geometry Covariance matrices Indexes Coherence Sparse matrices XL-MIMO near-field channel estimation triple parametric decomposition compressive sensing

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Energy Minimization for Heterogenous Traffic Coexistence with Puncturing in Mobile Edge Computing-Based Industrial Internet of Things

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China Communications 2024年第10期21卷 167-180页

作者： Wang Xue Wang Ying Fei Zixuan Zhao Junwei School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology Beijing 100876China

Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is *** results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.

关键词： energy minimization enhanced mobile broadband(eMBB)and ultra-reliable low latency communications(URLLC)coexistence industrial Internet of Things(IIoT) mobile edge computing(MEC) puncturing

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Web3 Technologies: Challenges and Opportunities

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IEEE NETWORK 2024年第3期38卷 187-193页

作者： Liu, Weikang Cao, Bin Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China

Different from "read" based Web1 and "read-write" based Web2, "read-write-own" based Web3 is proposed as a typical user-centric Internet to open the new generation of the World Wide Web. Web3 is the internet religious fundamentalism for data rights owned by users based on identity, data, network, and service, which requires the support of full-process data protection provided by a zero-trust and zero-touch environment. However, as to the traditional layered architecture like TCP/IP, data protection is an overlaid function, which is far away from the full-process data protection requirements. To this end, in order to build a zero-trust and zero-touch environment, a Web3 architecture is presented in this article as a promising paradigm. Meanwhile, the key principles for Web3 architecture designing are discussed, covering interoperability and interpretability of data, trustworthiness throughout network lifecycle, incentive for collaboration and sharing, and human-centric experience. Moreover, in order to achieve these principles, four aspects of enabling technologies are presented, including semantic, incentive, decentralized, and spatial technologies. Finally, some main challenges and open issues are also identified as a future direction for further work.

关键词： Blockchains Data protection Security Computer architecture Collaboration Artificial intelligence Interoperability Semantic Web User centered design Decentralized applications Web3 Zero-trust Zero-touch Data rights User-centric Decentralization

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Multivariate time-series anomaly detection

Multivariate time-series anomaly detection

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2023 International Conference on Intelligent Systems, Communications, and Computer Networks, ISCCN 2023

作者： Wang, Qifa Shen, Qiwei State Key Laboratory Of Networking And Switching Technology China

ISBN: (纸本)9781510666238

Anomalies are rare items that differ significantly from the majority of the data and raise suspicion. Time series anomaly detection is of great significance in industrial applications, data mining and other fields. In this paper, we propose an autoencoder-based anomaly detection model. In the encoder part, the dependency relationship between different time series is mined through the graph attention network, the time domain data features are extracted through the GRU, and the frequency domain features are extracted through the convolutional neural network. The temporal data is reconstructed using a decoder consisting of a recurrent neural network in the encoder part. The residual between the reconstructed data and the original data is used to further judge anomalies. Data cleaning is performed in the preprocessing part to improve model performance. The effectiveness of our method is demonstrated on three publicly available datasets, and our method is found to outperform four other common anomaly detection methods. © 2023 SPIE. All rights reserved.

关键词： Anomaly detection

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Next-Generation RIS: From Single to Multiple Functions

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IEEE WIRELESS COMMUNICATIONS LETTERS 2023年第12期12卷 1988-1992页

作者： Zheng, Ailing Ni, Wanli Wang, Wen Tian, Hui Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In this letter, we propose a next-generation reconfigurable intelligent surface (RIS) architecture, termed multi-functional RIS (MF-RIS), which enables signal reflection, refraction and amplification simultaneously to overcome the challenges, such as half-space coverage and double-fading attenuation. In order to investigate the performance of MF-RIS, we aim at maximizing the sum-rate in an MF-RIS-aided non-orthogonal multiple access network with discrete phase shifts, where three practical operating protocols, namely energy splitting (ES), mode switching (MS), and time switching (TS), are studied. Then, an efficient iterative algorithm is developed to tackle the formulated non-convex problem, where the transmit beamforming and MF-RIS coefficients are designed jointly by solving a series of semidefinite programming problems. Simulation results show that: 1) The proposed MF-RIS-assisted NOMA network provides 57% and 41% sum-rate gains than single- and dual-functional RISs, respectively;2) The TS protocol outperforms the ES and MS counterparts.

关键词： Multi-functional RIS non-orthogonal multiple access beamforming design sum-rate maximization

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Task Allocation Under Geo-Indistinguishability via Group-Based Noise Addition

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IEEE TRANSACTIONS ON BIG DATA 2023年第3期9卷 860-877页

作者： Zhang, Pengfei Cheng, Xiang Su, Sen Wang, Ning Beijing Univ Posts & Telecommun BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Locations are usually necessary for task allocation in spatial crowdsourcing, which may put individual privacy in jeopardy without proper protection. Although existing studies have well explored the problem of location privacy protection in task allocation under geo-indistinguishability, they potentially assume the workers could perform any tasks, which might not be practical in reality. Moreover, they usually adopt planar laplacian mechanism to achieve geo-indistinguishability, which will introduce excessive noise due to its randomness and boundlessness. To this end, we propose a task alloCAtioN approach via grOup-based noisE addition under Geo-I, referred to as CANOE. Its main idea is that each worker uploads the noisy distances between his true location and the obfuscated locations of his preferred tasks instead of uploading his obfuscated location. In particular, to alleviate the total noise when conducting grouping, we put forward an optimized global grouping with adaptive local adjustment method OGAL with convergence guarantee. To collect the noisy distances which are required for subsequent task allocation, we develop a utility-aware obfuscated distance collection method UODC with solid privacy and utility guarantees. We further theoretically analyze the privacy, utility and complexity guarantees of CANOE. Extensive analyses and experiments over two real-world datasets confirm the effectiveness of CANOE.

关键词： Task analysis Resource management Privacy Noise measurement Servers Crowdsourcing Convergence Spatial crowdsourcing task allocation privacy protection geo-indistinguishability

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Wireless Beacon Enabled Hybrid Sparse Channel Estimation for RIS-Aided mmWave Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第5期71卷 3144-3160页

作者： Guo, Xufeng Chen, Yuanbin Wang, Ying Beijing Univ Posts & Telecommun BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The inability of reconfigurable intelligent surfaces (RISs) to signal processing has become a critical bottleneck in terms of the precise capture of cascaded channels. There exists a pair of critical issues: 1) the high-dimensional cascaded channel to be estimated, which always entails substantial pilot overhead typically proportional to RIS elements and 2) the high possibilities of false alarms and unfaithful angle detection during cascaded channel estimates, referred to as the angular distortion, particularly in the case of conventional fully-passive RIS systems. To circumvent these issues, we investigate in this paper a multi-user multiple-input multiple-output (MIMO) system assisted by a wireless beacon (WB) enabled RIS over millimeter wave (mmWave) frequency. Specifically, WB is a functionally independent hardware that attaches parasitically to the RIS using only one radio frequency chain for broadcasting pilots, allowing for the accurate acquisition of partial cascaded channel information, e.g., some of angles of arrival/departure (AoA/AoDs) and complex gains. This efficiently eliminates the angular distortion effect and thus facilitates high precision recovery of sparse cascaded mmWave channels. Then, a hybrid structured sparsity (HSS) model is presented to capture hybrid sparse priors and approximate exact posterior distributions associated with RIS-aided twin-hop channels. We conceive of channel estimation as a compressive sensing problem in contrast to its conventional copy due to the presence of an unknown sensing matrix. To tackle this problem, an expectation-maximization (EM)-based algorithm, i.e., HSS-EM, is developed in order to fully employ the sparse priors encapsulated by the proposed HSS model for a robust and accurate recovery of the cascaded channels. Numerical results validate our analytical claims and demonstrate the significant improvement in terms of the normalized mean square error (NMSE) performance over conventional designs.

关键词： Channel estimation Millimeter wave communication Distortion Radio frequency Wireless communication Sensors Hardware Reconfigurable intelligent surface millimeter wave channel estimation sparse signal recovery compressive sensing

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Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2023年第13期10卷 11942-11943页

作者： Ni, Wanli Zheng, Jingheng Tian, Hui Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Implementing existing federated learning in massive Internet of Things (IoT) networks faces critical challenges, such as imbalanced and statistically heterogeneous data and device diversity. To this end, we propose a semi-federated learning (SemiFL) framework to provide a potential solution for the realization of intelligent IoT. By seamlessly integrating the centralized and federated paradigms, our SemiFL framework shows high scalability in terms of the number of IoT devices even in the presence of computing-limited sensors. Furthermore, compared to traditional learning approaches, the proposed SemiFL can make better use of distributed data and computing resources, due to the collaborative model training between the edge server and local devices. Simulation results show the effectiveness of our SemiFL framework for massive IoT networks. The code can be found at https://***/niwanli/SemiFL_IoT.

关键词： Collaborative intelligence data and device heterogeneity Internet of Things (IoT) semi-federated learning (SemiFL)

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