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MUSIC-Lite: Efficient MUSIC Using Approximate Computing: An OFDM Radar Case Study

IEEE EMBEDDED SYSTEMS LETTERS 2024年第4期16卷 329-332页

作者： Bhattacharjya, Rajat Sarkar, Arnab Maity, Biswadip Dutt, Nikil Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721306 India

Multiple signal classification (MUSIC) is a widely used direction of arrival (DoA)/angle of arrival (AoA) estimation algorithm applied to various application domains, such as autonomous driving, medical imaging, and astronomy. However, MUSIC is computationally expensive and challenging to implement in low-power hardware, requiring exploration of tradeoffs between accuracy, cost, and power. We present MUSIC-lite, which exploits approximate computing to generate a design space exploring accuracy-area-power tradeoffs. This is specifically applied to the computationally intensive singular value decomposition (SVD) component of the MUSIC algorithm in an orthogonal frequency-division multiplexing (OFDM) radar use case. MUSIC-lite incorporates approximate adders into the iterative CORDIC algorithm that is used for hardware implementation of MUSIC, generating interesting accuracy-area-power tradeoffs. Our experiments demonstrate MUSIC-lite's ability to save an average of 17.25% on-chip area and 19.4% power with a minimal 0.14% error for efficient MUSIC implementations.

关键词： OFDM Approximate computing Pattern classification Radar approximation algorithms Hardware Multiple signal classification Space exploration System-on-chip Frequency division multiplexing CORDIC multiple signal classification (MUSIC) orthogonal frequency-division multiplexing (OFDM) radar singular value decomposition (SVD)

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Differentially Private Hierarchical Clustering with Provable approximation Guarantees 40

Differentially Private Hierarchical Clustering with Provable...

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40th International Conference on Machine Learning

作者： Imola, Jacob Epasto, Alessandro Mandian, Mohammad Cohen-Addad, Vincent Mirrokni, Vahab UCSD Dept Comp Sci & Engn La Jolla CA 92093 USA Google New York NY USA

Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approximation algorithms for hierarchical clustering under the rigorous framework introduced by Dasgupta (2016). We show strong lower bounds for the problem: that any epsilon-DP algorithm must exhibit O(|V|(2)/epsilon)-additive error for an input dataset V. Then, we exhibit a polynomial-time approximation algorithm with O(|V|(2.5)/epsilon)-additive error, and an exponential-time algorithm that meets the lower bound. To overcome the lower bound, we focus on the stochastic block model, a popular model of graphs, and, with a separation assumption on the blocks, propose a private 1 + o(1) approximation algorithm which also recovers the bottom-level blocks exactly. Finally, we perform an empirical study of our algorithms and validate their performance.

关键词： approximation algorithms

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Improved Simultaneous Perturbation Stochastic approximation-based Consensus Algorithm for Tracking* 31

Improved Simultaneous Perturbation Stochastic Approximation-...

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31st Mediterranean Conference on Control and Automation (MED)

作者： Erofeeva, Victoria Granichin, Oleg RAS Inst Problems Mech Engn St Petersburg Russia St Petersburg State Univ Res Lab Anal & Modeling Social Processes Fac Mathemat & Mech St Petersburg Russia

ISBN: (纸本)9798350315431

In this paper, we consider a distributed stochastic optimization problem where the goal is to cooperatively minimize a non-stationary mean-risk functional. Such problem is an integral part of many important problems in wireless networks, transportation systems, sensor networks, and others. In particular, we focus on the reduction of computational effort needed to achieve a certain level of accuracy. Thus, we propose an improved Simultaneous Perturbation Stochastic approximation-based consensus algorithm that achieves better accuracy in contrast to an existing solution over the same time horizon and provide its theoretical analysis. We also show the convergence to a bound for mean-squared errors of estimates. The simulation validates the new algorithm in a multi-sensor multi-target problem.

关键词： approximation algorithms

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Robust Power Control for Spectrum Sharing Vehicular Communications Using OTFS Modulation

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 6721-6726页

作者： Li, Yue Han, Shiying Sun, Guiling Nankai Univ Coll Elect Informat & Opt Engn Key Lab Optoelect Sensor & Sensing Network Technol Tianjin 300350 Peoples R China

Orthogonal time frequency space (OTFS) is a promising two-dimensional modulation technique to provide better communication performance than orthogonal frequency division multiplexing in high mobility scenarios. Since the channel parameters in such scenarios vary fast, they are hard to be acquired timely and accurately. In this correspondence, we focus on spectrum sharing vehicular communication networks that use OTFS modulation to mitigate the Doppler effect with imperfect channel state information (CSI). We aim to optimize the transmit powers to maximize the average achievable rate of the vehicle-to-infrastructure (V2I) link, while meeting the reliability requirement of the vehicle-to-vehicle (V2V) link. To tackle this non-convex optimization problem, we propose an efficient algorithm named constrained stochastic successive convex approximation (CSSCA)-based power control algorithm by constructing convex surrogate functions of both the objective and constraint functions. Simulation results validate the convergence and the robustness of the proposed algorithm as compared to the benchmark schemes.

关键词： Doppler effect Channel estimation Power control Optimization approximation algorithms Time-frequency analysis Symbols Quality of service Delays Communication networks Vehicular communications orthogonal time frequency space power control imperfect channel state information constrained stochastic successive convex approximation

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An approximation Algorithm for the (Metric) Clustered Path Traveling Salesman Problem 17th

An Approximation Algorithm for the (Metric) Clustered Path...

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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Zhang, Jiaxuan Gao, Suogang Hou, Bo Liu, Wen School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Computational Mathematics and Applications Shijiazhuang050024 China

ISBN: (纸本)9789819644476

We consider the (metric) clustered path traveling salesman problem. In this problem, we are given a complete graph G=(V,E) along with a nonnegative edge cost function satisfying the triangle inequality, where V is partitioned into disjoint subsets V1,…,Vk called clusters and s∈V1,t∈Vk are two given vertices. The objective of the problem is to find a Hamiltonian path of G with minimum cost from s to t, satisfying that all vertices in each cluster are visited consecutively by this path. In this paper, we consider the problem in the case where endpoints of the subpath induced by the path on each cluster are both specified and a 2-approximation algorithm is given. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： approximation algorithms

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Toeplitz Low-Rank approximation with Sublinear Query Complexity 34

Toeplitz Low-Rank Approximation with Sublinear Query Complex...

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34th ACM-SIAM Annual Symposium on Discrete algorithms (SODA)

作者： Kapralov, Michael Lawrence, Hannah Makarov, Mikhail Musco, Cameron Sheth, Kshiteej Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Lausanne Switzerland MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Univ Massachusetts Manning Coll Informat & Comp Sci Amherst MA 01003 USA

ISBN: (纸本)9781611977554

We present a sublinear query algorithm for outputting a near-optimal low-rank approximation to any positive semidefinite Toeplitz matrix T epsilon R-dxd. In particular, for any integer rank k <= d and epsilon,delta > 0, our algorithm makes (O) over tilde (k(2) center dot log(1/delta) center dot poly(1/epsilon)) queries to the entries of T and outputs a rank (O) over tilde (k center dot log(1/delta)/epsilon) matrix (T) over tilde epsilon R-dxd such that parallel to T - ($) over tilde parallel to(F) <= (1 +epsilon) center dot parallel to T - T-k parallel to F + (d)parallel to T parallel to(F). Here, parallel to center dot parallel to(F) is the Frobenius norm and T-k is the optimal rank-k approximation to T, given by projection onto its top k eigenvectors. (O) over tilde(center dot) hides polylog(d) factors. Our algorithm is structure-preserving, in that the approximation (T) over tilde is also Toeplitz. A key technical contribution is a proof that any positive semidefinite Toeplitz matrix in fact has a near-optimal low-rank approximation which is itself Toeplitz. Surprisingly, this basic existence result was not previously known. Building on this result, along with the well-established off-grid Fourier structure of Toeplitz matrices [Cybenko'82], we show that Toeplitz (T) over tilde with near optimal error can be recovered with a small number of random queries via a leverage-score-based off-grid sparse Fourier sampling scheme.

关键词： approximation algorithms

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Pareto Optimization for Subset Selection via Steering Mutation With Theoretical Guarantee

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2025年第2期20卷 80-94页

作者： Zhang, Lei Wang, Zhanpeng Yang, Haipeng Cheng, Fan Anhui Univ Hefei 230601 Peoples R China

Subset selection, which refers to the selection of a finite number of variables to optimize a given objective function, is a fundamental problem in various applications. Among the existing algorithms for solving this problem, evolutionary algorithms (EAs) based on Pareto optimization have demonstrated good performance in acquiring high-quality subsets with a theoretical guarantee. However, most existing EAs ignore the importance of variables in designing evolutionary operators (for example, mutations), which makes the search inefficient. To fill this gap, this study proposes a steering mutation-based evolutionary algorithm called SMSS for subset selection, where the importance of variables is fully utilized to ensure both the effectiveness and theoretical guarantee of the algorithm. Specifically, a novel steering mutation operator is designed in SMSS to effectively select items of high importance and discard those of low importance in the Pareto optimization process, which provides the generation of final subsets with better quality. In addition, it is proved that SMSS can attain a (1-e(-gamma))-optimal polynomial-time approximation guarantee in scenarios where the objective function exhibits monotonicity. Extensive experiments on two subset selection applications (unsupervised feature selection and sparse regression) demonstrate the superiority of the proposed algorithm over state-of-the-art algorithms.

关键词： Evolutionary computation Pareto optimization Linear programming approximation algorithms Feature extraction

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A Recursive Puncturing Method for PAC Codes Based on the Partial Order

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IEEE COMMUNICATIONS LETTERS 2025年第5期29卷 978-982页

作者： Li, Xiaoyuan Lin, Dengsheng Xiao, Yue Xiao, Ming Univ Elect Sci & Technol China UESTC Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China KTH Royal Inst Technol Div Informat Sci & Engn S-10044 Stockholm Sweden

The development of polarization-adjusted convolutional (PAC) codes has introduced a class of efficient designs for short packet transmission. In this contribution, aiming at more flexible code length and rate matching for time-varying channel scenarios, a low-complexity puncturing algorithm for PAC codes is proposed. Specifically, we introduce a Gaussian approximation (GA) algorithm for PAC codes and propose a GA-based optimization method for punctured patterns. Building on this, we present a Gaussian inverse mapping method based on partial order, utilizing a recursive approach to construct the initial set, which significantly reduces the search complexity. Subsequently, we develop a recursive puncturing algorithm based on partial order. Finally, we integrate this method with Reed-Muller (RM) rules, further reducing the complexity.

关键词： Codes Picture archiving and communication systems Polar codes Gaussian approximation Convolutional codes Optimization approximation algorithms Vectors Indexes Error probability Polarization-adjusted convolutional codes puncturing gaussian approximation partial order

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Towards Optimal Placement and Runtime Migration of Time-Sensitive Services of Connected and Automated Vehicles

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 13-33页

作者： Elgarhy, Osama Le Moullec, Yannick Reggiani, Luca Azeem, Muhammad Moazam Taleb, Tarik Alam, Muhammad Mahtab Tallinn Univ Technol Thomas Johann Seebeck Dept Elect Tallinn 19086 Estonia Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar Ruhr Univ Bochum Fac Elect Engn & Informat Technol D-44801 Bochum Germany

In this paper, the goal is to reduce the time needed for the placement and migration of services of Connected Automated Vehicles (CAVs) using precise hybrid positioning method. First, to place a service in a Multi-access Edge Computing (MEC) node, there should be sufficient resources in the served MEC node;otherwise, the service would be placed on the neighboring MEC node or even on the core node, resulting in higher delays. We start by modeling our problem with the aid of traffic theory to analytically obtain the necessary number of resources for achieving the desired delay. Second, to reduce the migration process delay, the migration should begin before the vehicle reaches the MEC node. Thus, an AI lane-based scheme is proposed to predict candidate nodes for migration based on precise positioning. Precise positioning data is acquired from a Real-Time Kinematic Global Navigation Satellite System (RTK- GNSS) measurement campaign. The obtained imbalanced raw data is treated and used in the prediction scheme, and the resulting prediction accuracy achieves 99.3%. Finally, we formulate a service placement and migration delay optimization problem and propose an algorithm to solve it. The algorithm shows a latency reduction of approximately 50% compared to the core placement and up to 29% compared to the benchmark prediction algorithm. Moreover, the simulation results for the proposed service placement and migration algorithm show that in case the MEC resource calculations are not used, the delay is 2.2 times greater than when they are used.

关键词： Delays Prediction algorithms Optimization Convolutional neural networks Accuracy Quality of service Genetic algorithms Energy consumption approximation algorithms 5G mobile communication 5G beyond 5G MEC position prediction RTK service migration service placement vehicular communication

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A Unified Affine-Projection-Like Adaptive Algorithm for System Identification

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第2期71卷 982-986页

作者： Li, Yingsong Fu, Yonglin Miao, Yongchun Huang, Zhixiang Diniz, Paulo S. R. Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Anhui Peoples R China Univ Fed Rio de Janeiro Program Elect Engn BR-21941972 Rio De Janeiro Brazil

A unified affine-projection-like adaptive (UAPLA) algorithm is devised and verified for system identification. The UAPLA algorithm uses a generalized cost function encompassing some data-reusing methods to cope with colored input signals. Furthermore, the UAPLA algorithm is derived based on the new cost function to approximate several affine-projection algorithms. As a result, the proposed UAPLA algorithm approximates some classical adaptive filters that can be considered as special cases of the UAPLA, allowing flexibility to achieve low estimation errors under impulsive noises. The obtained results conducted by simulations help to corroborate the superiority of the proposed UAPLA algorithm over other popular AP algorithms.

关键词： approximation algorithms Convergence Signal processing algorithms System identification Mathematical models Echo cancellers Robustness Unified adaptive filter algorithm general cost function system identification impulsive noise

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