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Distributed Learning for Optimal Spectrum Access in Dense Device-to-Device Ad-Hoc Networks

IEEE TRANSACTIONS ON signal processing 2023年 71卷 3149-3163页

作者： Boyarski, Tomer Wang, Wenbo Leshem, Amir Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel Kunming Univ Sci & Technol KUST Fac Mech & Elect Engn Kunming 650500 Peoples R China

In 5G networks, Device-to-Device (D2D) communications aim to provide dense coverage without relying on the cellular network infrastructure. To achieve this goal, the D2D links are expected to be capable of self-organizing and allocating finite, interfering resources with limited inter-link coordination. We consider a dense ad-hoc D2D network and propose a decentralized time-frequency allocation mechanism that achieves sub-linear social regret toward optimal spectrum efficiency. The proposed mechanism is constructed in the framework of multi-agent multi-armed bandits, which employs the carrier-sensing-based distributed auction to learn the optimal allocation of time-frequency blocks with different channel state dynamics from scratch. Our theoretical analysis shows that the proposed fully distributed mechanism achieves a logarithmic regret bound by adopting an epoch-based strategy-learning scheme when the length of the strategy-exploitation window is exponentially growing. We further propose an implementation-friendly protocol featuring a fixed exploitation window, which guarantees a good tradeoff between performance optimality and protocol efficiency. Numerical simulations demonstrate that the proposed protocol achieves higher efficiency than the prevalent reference algorithms in both static and dynamic wireless environments.

关键词： Resource management Device-to-device communication Quality of service Ad hoc networks Time-frequency analysis signal processing algorithms Multiaccess communication Multi-agent multi-armed bandit D2D networks resource allocation distributed network management

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FlexKA: A Flexible Karatsuba Multiplier Hardware Architecture for Variable-Sized Large Integers

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IEEE ACCESS 2023年 11卷 55212-55222页

作者： Kang, Byeongmin Cho, Hyungmin Sungkyunkwan Univ Dept Comp Sci & Engn Suwon 16419 South Korea

The Karatsuba algorithm is an effective way to accelerate large integer multiplications through recursive function calls. However, existing hardware implementations of Karatsuba multipliers are limited to fixed operand sizes. To enable their application in diverse domains, including homomorphic encryption with varying multiplicative depths, it is necessary to support variable operand sizes. In this paper, we propose a novel Karatsuba multiplier design, named FlexKA, which supports variable operand sizes through a state machine that manages the dynamic call states of the operation. We evaluate FlexKA on the Xilinx ZynqMP FPGA and demonstrate that it supports variable operand sizes up to 256K bits, achieving a 9.2x speedup compared to a highly-optimized software library running on a CPU. Our results show that FlexKA is an efficient and effective solution for large integer multiplications with flexible operand sizes in hardware.

关键词： Hardware signal processing algorithms algorithms Software algorithms Memory modules Software Memory management Multiplying circuits field programmable gate arrays

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Confederated Learning: Federated Learning With Decentralized Edge Servers

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 248-263页

作者： Wang, Bin Fang, Jun Li, Hongbin Yuan, Xiaojun Ling, Qing Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510006 Guangdong Peoples R China Pazhou Lab Guangzhou 510300 Guangdong Peoples R China

Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner. Due to stringent communication and bandwidth constraints, such a centralized framework has limited scalability as the number of devices grows. To address this issue, in this paper, we propose a ConFederated Learning (CFL) framework. The proposed CFL consists of multiple servers, in which each server is connected with an individual set of devices as in the conventional FL framework, and decentralized collaboration is leveraged among servers to make full use of the data dispersed throughout the network. We develop a stochastic alternating direction method of multipliers (ADMM) algorithm for CFL. The proposed algorithm employs a random scheduling policy which randomly selects a subset of devices to access their respective servers at each iteration, thus alleviating the need of uploading a huge amount of information from devices to servers. Theoretical analysis is presented to justify the proposed method. Numerical results show that the proposed method can converge to a decent solution significantly faster than gradient-based FL algorithms, thus boasting a substantial advantage in terms of communication efficiency.

关键词： Servers Training Peer-to-peer computing signal processing algorithms Scalability Data models Computational modeling Confederated learning ADMM random scheduling

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Programmable Metasurface Hybrid MIMO Beamforming: Channel Estimation, Data Transmission, and System Implementations at 28 GHz

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IEEE SYSTEMS JOURNAL 2023年第1期17卷 1270-1281页

作者： Li, Yueheng Long, Xueyun de Oliveira, Lucas Giroto Eisenbeis, Joerg Alabd, Mohamad Basim Bettinga, Sven Wan, Xiang Cui, Tie Jun Zwick, Thomas Karlsruhe Inst Technol D-76131 Karlsruhe Germany Southeast Univ Nanjing 12579 Jiangsu Peoples R China

With the development of wireless communication generations, multiple-input and multiple-output (MIMO) architectures have proven to be a solution to the higher required data rates for the increasing number of mobile users. As a matter of fact, better earnings can be gathered from a low-cost system with effective functions, which results in the hybrid beamforming architecture combining digital chains with large-scale antenna arrays. The programmable metasurface (PM) is a promising antenna array concept that realizes reconfigurable beamforming. It is raised to be an attractive antenna array architecture due to its low price and power consumption. However, experimental investigations combining PMs with modern wireless communication systems have not been intensively studied. Its challenges and difficulties of signal processing and system implementation remain uncertainties to be discovered. In this article, a PM hybrid MIMO beamforming system including the aforementioned important topics is presented as follows. First of all, the hybrid beamforming channel estimation algorithm adapted for PM is created by merging analog beam training and digital interleaved orthogonal frequency division multiplexing. Afterward, data transmissions based on the estimated channel state information utilizes variant signal recovery methods to examine the channel estimation accuracy and system feasibilities. To practically analyze the proposed system, the aforementioned aspects are implemented into system-level experiments using three PMs operating at 28 GHz for downlink wireless communication in both single-user and multiuser scenarios. Proper results are delivered, which successfully prove the PM hybrid MIMO beamforming system functionalities.

关键词： Array signal processing MIMO communication Channel estimation signal processing algorithms Antenna arrays Training Symbols Antenna arrays communication systems signal processing

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Robust Tracking of Multiple Targets Subspace Based on Distributed Array Networks

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 9758-9768页

作者： Chen, Ningkang Wei, Pine Gao, Lin Li, Wanchun Liu, Liang Zhang, Huaguo Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610054 Peoples R China

Robust subspace tracking is a real-time solution for suppressing harsh interference and estimating the target subspace in complex electromagnetic environments. In this article, a robust and decentralized subspace tracking method is proposed. The multisensor array is divided into a number of nodes, each attached with a processor and no central processing unit (CPU) is deployed. In order to reduce the impact of a burst pulse, a robust real-time update algorithm of the subspace is designed through the weighted least squares method. The optimization model established in this article is robust to synchronization error. Then a subspace real-time update algorithm is proposed based on the average consensus (AC) method that integrates the information of distributed arrays. The computational complexity and convergence properties of the proposed algorithm are also investigated. Simulation results show that the proposed approach converges to the subspace estimation error by an order of magnitude compared with the single-node algorithm.

关键词： signal processing algorithms Target tracking Peer-to-peer computing Arrays Array signal processing Real-time systems Eigenvalues and eigenfunctions Average consensus (AC) distributed PCA recursive least squares robust subspace tracking

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High-Accurate Robust Total Variation Denoising Algorithm With Adjustable Exponential Upper Bound Function for Micro-Thrust Measurement

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Liu, Zhikang Chen, Xingyu Xu, Jiawen Zhang, Chengxin Xu, Luxiang Guo, Ning Zhao, Liye Yan, Ruqiang Southeast Univ Sch Instrument Sci & Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Minist Educ Key Lab Microinertial Instruments & Adv Nav Techno Nanjing 210096 Peoples R China Univ Chinese Acad Sci Sch Phys & Photoelect Engn Hangzhou 310024 Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Sch Fundamental Phys & Math Sci Key Lab Gravitat Wave Precis Measurement Zhejiang Hangzhou 310024 Peoples R China Lanzhou Inst Phys Sci & Technol Vacuum Technol & Phys Lab Lanzhou 730010 Peoples R China Xi An Jiao Tong Univ Int Machinery Ctr Sch Mech Engn Xian 710049 Shanxi Peoples R China

Micro-Newton thrusters are widely utilized in the field of astronautics. Typically, micro-Newton thrust signal processing aims at the restoration of piecewise constant signals (PCSs). Improving the amplitude accuracy of the denoised signal remains a major difficulty. In this research, we proposed a new nonlinear total variation denoising (TVD) algorithm with an adjustable majorization-minimization (MM) upper bound function for optimizing the iterative solution process of TVD. The influences of the upper bound function of the TVD on denoising are analyzed in detail. It is demonstrated that the proposed adjustable parameter -a-based TVD (aTVD) method shows extraordinary robustness for denoising signals with different noise levels. In addition, this algorithm can also adjust the sensitivity of the jumps in signals for optimized amplitude accuracy effectively. Numerical examples show that the proposed method has 40.4% and 18.2% accuracy improvement over conventional TVD using traditional quadratic upper bound and enhanced TVD using exponential upper bound function. Moreover, the advantages of the proposed method are validated in an experiment of measured thrust signal processing. The proposed method can be further adopted to improve the performance of signal denoising algorithms developed from the TVD method.

关键词： Noise reduction Upper bound Gravitational waves TV signal processing algorithms Nonlinear filters Geologic measurements Denoising algorithm majorization-minimization (MM) algorithm thrust measurement total variation (TV) upper bound

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Two-Dimensional Autofocus Combined With the TSA for Spotlight SAR

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 9990-10000页

作者： Lin, Hao Xue, Xiaoyu Li, Ning Xing, Mengdao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Understa Xian 710071 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Peoples R China

For high-resolution spotlight synthetic aperture radar, Doppler aliasing is always introduced in the long synthetic aperture observation time. The existence of signal Doppler aliasing will greatly affect the accuracy of motion compensation. To avoid Doppler aliasing, it is an effective strategy to use the subaperture (SA) approach. However, it will introduce the long synthetic aperture time stitching problem of signal and phase error. Compared with the SA approach, the two-step approach (TSA) based on the full aperture not only eliminates the Doppler aliasing but also avoids the signal splicing problem. Therefore, a two-dimensional (2-D) frequency-domain autofocus combined with the TSA is adopted in this article. First, TSA is used to perform dealiasing and coarse focusing imaging on full-aperture (FA) data. Second, the phase error history and the phase error form in the TSA process are analyzed and deduced, and an accurate prior structure of the phase errors is established. Then, the accurate inversion of the common 2-D phase errors is realized based on this prior structure. Finally, the autofocus process of the FA signal combined with the TSA is realized in the nonaliasing 2-D frequency domain. The validity of the recommended algorithm is verified by simulation and experimental results.

关键词： Doppler effect Azimuth Imaging Time-domain analysis Apertures signal processing algorithms Radar imaging 2-D autofocus process motion compensation (MoCo) synthetic aperture radar (SAR) two-step approach (TSA)

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Two-Dimensional Direction Finding Based on Cylindrical Nested Conformal Array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6853-6862页

作者： Fu, Mingcheng Zheng, Zhi Wang, Wen-Qin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Shenzhen Peoples R China UESTC Inst Elect & Informat Engn Dongguan 523808 Guangdong Peoples R China

Recently, nested conformal arrays have attracted considerable interest due to their ability to provide increased array aperture and enhanced degrees of freedom compared to uniform conformal arrays. In this article, we address the problem of 2-D direction finding using the cylindrical nested conformal array (CNCA). First of all, we derive the coarray signal model of the CNCA and utilize different coarray signal components to construct multiple Toeplitz matrices. Based on the constructed Toeplitz matrices, we further develop two direction-finding algorithms to achieve the decoupled estimation of the 2-D direction. Unlike the exiting approaches, the proposed two algorithms exploit more coarray output components, and hence, they have better direction-finding performance. Moreover, our algorithms can be applied to a larger number of nested subarrays in the CNCA. Numerical results demonstrate the superiority of the proposed algorithms in comparison to the exiting methods.

关键词： Sensors signal processing algorithms Manifolds Estimation Covariance matrices Azimuth Apertures Cylindrical nested conformal array (CNCA) difference coarray ESPRIT-like reduced-dimensional (R-D) MUSIC two-dimensional (2-D) direction finding

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A Dual-Recursive-Least-Squares Algorithm for Automotive Radar Interference Suppression

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第10期24卷 10603-10617页

作者： Wang, Ping Yin, Xuefeng Rodriguez-Pineiro, Jose Chen, Zhuoyu Zhu, Pengqi Li, Gang Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China Zongmu Technol Shanghai 201210 Peoples R China

Automotive radar sensors are widely used in vehicles so as to allow the "advanced driver assistance systems (ADAS)" to work. As far as we are concerned, no mandatory regulations were established so far for interference control in the cases where multiple vehicles equipped with automotive radars exist in close proximity. Consequently, those inter-vehicle cross-interference easily result in the radar performance degradation, e.g. incapability of target detection and identification. In this paper, an interference suppression algorithm is proposed which utilizes two Recursive-Least-Square (RLS) adaptive filters to estimate, the interference signals caused by the "aggressor" radar and the echo signals from the "ego" radar. The algorithm operates in the iterative mode with its initial input being the originally received beat signals involving interference. In each iteration, the estimates of the interference signals and of the clean signals are calculated sequentially by the two RLS filters, and the beat signal is updated with parts of the fragments substituted with their interference-mitigated counterparts. Such iterative operations stop when the convergence criterion, e.g. none of the fragments in the beat signals are considered being interfered, is achieved or the iteration number reaches the limit predefined due to the practical constraints. Simulation and measurement results demonstrate that the proposed dual-RLS-based algorithm outperforms the existing methods in terms of lower interference-to-signal ratio resultant and superior capability of retrieving the original range-Doppler profile.

关键词： Radar Interference Chirp Radar antennas Automotive engineering signal processing algorithms Frequency-domain analysis Frequency-modulated continuous wave (FMCW) radar interference suppression adaptive noise canceller RLS adaptive algorithm

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Parameter Estimation Using a Triad of Electrically Long Dipole Despite Data Model Mismatch

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第2期59卷 1670-1682页

作者： Zhong, Wenye Song, Yang Cai, Guofa Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China

Most dipole-antenna signal processing algorithms have focused on "short dipole" whose physical length ($L$) is under ($1/10$) of a wavelength ($\lambda$). Recent works now handle "long dipoles" of an electrical length $L/\lambda \in [0.1, 1]$. This article advances an algorithmic paradigm that uses short-dipole algorithm on long-dipole data. By studying the mismatched Cramer-Rao bound, we find that applying short-dipole algorithm to a long-dipole data model may yield parameters' estimates that are not far from their true values especially when $L/\lambda$ is small. This phenomenon motivates a two-step algorithm where we apply short-dipole algorithm to get parameters' coarse estimates, which are then fine-tuned by an iterative algorithm. Our algorithm maximally retains the merits of the existing short-dipole algorithms while processing the signals received by a long dipole triad. Monte Carlo simulations verify the effectiveness of the proposed paradigm.

关键词： signal processing algorithms Direction-of-arrival estimation Estimation error Antenna measurements Dipole antennas Azimuth Data models Dipoles ESPRIT mismatched Cramer-Rao bound (MCRB) MUSIC mismatch model

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