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Over-the-Air Phased Array Calibration Based on the SPGD Algorithm

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第3期23卷 1109-1113页

作者： Wang, Xiaobin Gong, Zhishuang Ge, Guangding Ma, Ji Wang, Yong Huawei Technol Co Ltd Xian 710075 Peoples R China

Phase errors of phased array antenna (PAA) can cause pattern distortion. This letter presents an over-the-air (OTA) phased array calibration method by an iterative stochastic parallel gradient descent (SPGD) algorithm. During the measurement, all the phased array elements are excited simultaneously, and an external receiving probe is placed to a given direction in the far-field for amplitude measurement, which is used as the fitness function. The basic principle is to find the optimal phases that maximize the fitness function by gradient operation. We numerically simulated and experimentally validated in a 4 x 4 uniform rectangular array (URA) at 2.6 GHz. The experimental results show that the proposed method needs 6N similar to 7N measurements to calibrate an N-element PAA and its calibration accuracy is comparable to that of the well-known rotating element electric field vector (REV) method. The proposed method provides a new idea for fast and accurate OTA phase calibration for PAA.

关键词： Phased arrays Calibration Phase shifters Antenna arrays Convergence approximation algorithms Antenna measurements Over-the-air (OTA) phased array antenna (PAA) phased array calibration stochastic parallel gradient descent (SPGD) algorithm

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approximation algorithms for the Street sweeping problem

Approximation algorithms for the Street sweeping problem

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： L. F. Hernández Sánchez L. E. Chávez Lomelí F. J. Zaragoza Martínez Posgrado en Optimización UAM Azcapotzalco Mexico UAM Azcapotzalco Mexico City Mexico

The Street sweeping problem (SSP) is a variation of the Windy postman problem (WPP) in which we must construct two tours traversing every edge, and each edge must be traversed once in each direction: one on the first tour and the opposite in the second tour. The computational complexity of this problem remains open. We present a (3/2 α + 1)-approximation algorithm for the SSP using an α-approximation algorithm for the WPP. We also present exact algorithms for some classes of graphs.

关键词： approximation algorithms approximation methods Vectors Cities and towns Algorithm design and analysis Routing Polynomials

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A Kernel Normalized Data-Reusing Generalized Maximum Correntropy Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第7期71卷 3593-3597页

作者： Zhao, Ji Mu, Yuzong Li, Qiang Tang, Lingli Zhang, Hongbin Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Dept Social Sci Mianyang 621010 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

The kernel adaptive filtering (KAF) algorithms have drawn increasing attention due to their advantages such as universal nonlinear approximation, linearity, and convexity in reproducing kernel Hilbert space (RKHS). This fact motivates us to develop a new KAF algorithm called kernel normalized data-reusing generalized maximum correntropy (KNDR-GMC). The cost function for KNDR-GMC is constructed by combining the generalized correntropy criterion, data-reusing method, and Gaussian kernel to deal with nonlinear system identification under impulsive noisy environments. Compared with existing algorithms, in a chaotic time-series prediction and a real-world application, the simulation results verify that KNDR-GMC achieves better filtering accuracy and a faster convergence rate.

关键词： Kernel Cost function Prediction algorithms Noise measurement Convergence approximation algorithms Density functional theory Kernel adaptive filtering generalized correntropy data-reusing impulsive noise

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Parallel Greedy algorithms for Steiner Forest

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第6期36卷 1311-1325页

作者： Ghalami, Laleh Grosu, Daniel Wayne State Univ Detroit MI 48202 USA

The Steiner Forest Problem is a fundamental combinatorial optimization problem in operations research and computer science. Given an undirected graph with non-negative weights for edges and a set of pairs of vertices called terminals, the Steiner Forest Problem is to find the minimum cost subgraph that connects each of the terminal pairs together. We design a family of parallel greedy algorithms based on a sequential heuristic greedy algorithm called Paired Greedy, which iteratively connects the terminal pairs that have the minimum distance. The family of parallel algorithms consists of a set of algorithms exhibiting various degrees of parallelism determined by the number of pairs that are connected in parallel in each iteration of the algorithms. We implement and run the algorithms on a multi-core system and perform an extensive experimental analysis. We analyzed the performance of the algorithms on a rich library of Steiner Forest instances with various underlying graph types. The results show that our proposed parallel algorithms achieve significant speedup with respect to the sequential Paired Greedy algorithm and provide solutions with costs that are very close to those of the solutions obtained by the sequential Paired Greedy algorithm. We provide recommendation on selecting the type of parallel algorithm and its parameters in order to achieve the most efficient results for each class of instances.

关键词： Forestry Greedy algorithms Costs Parallel algorithms Steiner trees approximation algorithms Training Partitioning algorithms Optimization Libraries Steiner forest parallel algorithms multi-core

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A Game-Theoretic Approach to Solving the Roman Domination Problem

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第5期12卷 1024-1040页

作者： Chen, Xiuyang Tang, Changbing Zhang, Zhao Chen, Guanrong Xinjiang Univ Coll Math & Syst Sci Urumqi 830000 Peoples R China Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Peoples R China Zhejiang Normal Univ Coll Math Sci Jinhua 321004 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

The Roman domination problem is an important combinatorial optimization problem that is derived from an old story of defending the Roman Empire and now regains new significance in cyber space security, considering backups in the face of a dynamic network security requirement. In this paper, firstly, we propose a Roman domination game (RDG) and prove that every Nash equilibrium (NE) of the game corresponds to a strong minimal Roman dominating function (S-RDF), as well as a Pareto-optimal solution. Secondly, we show that RDG is an exact potential game, which guarantees the existence of an NE. Thirdly, we design a game-based synchronous algorithm (GSA), which can be implemented distributively and converge to an NE in O(n) rounds, where $n$ is the number of vertices. In GSA, all players make decisions depending on local information. Furthermore, we enhance GSA to be enhanced GSA (EGSA), which converges to a better NE in O(n(2)) rounds. Finally, we present numerical simulations to demonstrate that EGSA can obtain a better approximate solution in promising computation time compared with state-of-the-art algorithms.

关键词： Games Resource description framework Nash equilibrium approximation algorithms Distributed algorithms Optimization Multi-agent systems Heuristic algorithms Gallium arsenide Faces Distributed algorithm game theory multi-agent system potential game Roman dominating function

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Secure Transmission Design for Rate-Splitting Empowered STAR-RIS-Aided Networks

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第9期13卷 2581-2585页

作者： Wang, Bing Tao, Xiaofeng Han, Shujun Yang, Kai Wu, Huici Beijing Univ Posts & Telecommun Natl Engn Res Ctr Mobile Network Technol Beijing 100876 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518055 Peoples R China

Rate splitting multiple access (RSMA) empowered simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided networks have been extensively researched to enhance spectrum efficiency and coverage capability without considering eavesdropping. However, the 360 degrees service coverage provided by STAR-RIS inevitably leads to comprehensive eavesdropping, posing more significant security risks for private information transmission compared to traditional reflection-only RIS. In this letter, we propose a secure transmission scheme for STAR-RIS aided downlink communication systems, which is empowered by a novel multiple access paradigm called RSMA. To address the security challenge, we study the proposed security transmission scheme by jointly designing active beamforming, passive beamforming, and common rate allocation to maximize the sum secrecy rate. To tackle the highly coupled non-convex optimization problem, we propose an efficient and low-complexity successive convex approximation-based penalization RSMA-STAR (PE-RSSTA) algorithm. The numerical results demonstrate that: i) the algorithm converges through iterations;ii)the proposed algorithm improves secrecy rate performance by 5.22% compared to the PE space division multiple access (SDMA) STAR algorithm and achieves a notable 27.37% improvement over the conventional RIS algorithm.

关键词： Security Resource management Interference approximation algorithms Eavesdropping Downlink Array signal processing Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) rate-splitting multiple access (RSMA) sum secrecy rate downlink communication

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LSAC: A Low-Power Adder Tree for Digital Computing-in-Memory by Sparsity and Approximate Circuits Co-Design

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第2期71卷 852-856页

作者： He, Chaojie Wang, Zi Xiang, Feibin Dai, Zhuoyu He, Yifan Yue, Jinshan Liu, Yongpan Chinese Acad Sci Inst Microelect Beijing 100029 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

The energy-efficient computing-in-memory (CIM) architectures have drawn much attention as the increasing demands of neural networks. Several SRAM-based CIM architectures adopt a digital implementation, using the digital adder trees (ATs) to perform in-memory multiply-accumulate (MAC) operations. Compared with the analog-domain CIM, the digital CIM eliminates errors caused by analog circuits to achieve high accuracy. However, the digital AT still incurs much power/area overhead. This brief proposes a novel low-power AT solution by sparsity and approximate circuits co-design. Several sparsity modes are explored to perform approximate logic substitution of the full adder. Besides, fine-grain pruning algorithm and offline data rearrangement compensate for the accuracy loss incurred by approximation. The proposed approximation scheme achieves at least a 19.3% reduction in area and a 30.0% reduction in power consumption. The maximum inference accuracy of the LeNet model on MNIST dataset is slightly 0.06% lower than the baseline accuracy. On the retrained Vgg8 and Vgg16 models on Cifar-10 dataset, the proposed three approximation strategies incur at most 0.99% accuracy decreases.

关键词： Adders Engines Logic gates Random access memory Computer architecture approximation algorithms Common Information Model (computing) Digital computing-in-memory adder tree approximate full adder structural pruning data rearrangement

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Discrete Offset-Symmetric Gaussians for Differential Privacy

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1915-1919页

作者： Korki, Mehdi Sadeghi, Parastoo Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia Univ New South Wales Sch Engn & Technol Canberra ACT 2612 Australia

In many applications of differential privacy (DP), continuous distributions such as the Laplace or the Gaussian are employed to perturb data queries. However, continuous distributions are not particularly suitable for discrete data and their quantization can compromise the privacy guarantees of DP. In this letter, we extend a recently proposed continuous mechanism for DP called offset-symmetric Gaussian tail (OSGT) distribution to its discrete version, which we call DOSGT. Our findings demonstrate that the one-dimensional DOSGT mechanism achieves the same level of (epsilon, delta (epsilon)-DP as the continuous OSGT, which is better than what is achievable by the discrete Gaussian at the same variance. We also derive the R & eacute;nyi differential privacy (RDP) of the DOSGT mechanism. We then present a simple and efficient algorithm for accurately sampling from DOSGT distribution, showcasing its applicability in DP scenarios involving integer-valued queries.

关键词： Differential privacy Tail Random variables Privacy Gaussian distribution Noise approximation algorithms discrete Gaussian mechanism approximate differential privacy

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A Greedy Descent Method for Budget Constrained Continuous Influence Maximization in Online Social Network

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Liu, Wei Deng, Ziwei Chen, Yixin Chen, Ling Yangzhou Univ Coll Informat & Engn Yangzhou 225009 Jiangsu Peoples R China Washington Univ Dept Comp Sci St Louis MO 63130 USA

Continuous influence maximization (CIM) in social networks aims to maximize the expected influence spreading by assigning each user a continuous weight reflecting the likelihood and cost for him becoming a seed. Traditional CIM assumes a budget constrain to limit the total cost of all users. However, this assumption does not tenable in practical applications. In practice, it is not necessary to incur costs for all the users. Instead, the budget should be set only for the cost associated with the seed set. In this article, an extended CIM problem of cost distribution under budget (CDB) is defined, which aims to assign different costs to the customers according to their ability to spread influence, ensuring that the cost of each potential seed set does not exceed the budget, while the expected spreading of the product's influence is maximized. The NP-hardness of CDB and the monotonicity and submodularity of its objective function are investigated. We formulate the CDB problem into a constrained optimization, and present a greedy descent-based algorithm for the problem. In each iteration of the greedy descent method, the influence increment of each node is calculated according to its estimated influence spreading range. The cost distribution is updated along the direction with the maximum increment. The optimal cost distribution can be obtained after several iterations. Precision of the results obtained by the proposed algorithm is analyzed. To avoid the time-consuming simulations, we design an effective algorithm for estimating the seed influence spreading range. Experiment results on real and synthetic networks show that the proposed algorithm can significantly improve the expected influence spreading.

关键词： Costs Social networking (online) approximation algorithms Linear programming Data mining Technological innovation Optimization Integrated circuit modeling Epidemics Electronic mail Continuous influence maximization cost distribution under budget greedy descent social networks viral marketing

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Parameterized approximation Scheme for Biclique-free Max k-Weight SAT and Max Coverage 34

Parameterized Approximation Scheme for Biclique-free Max k-W...

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

作者： Jain, Pallavi Kanesh, Lawqueen Panolan, Fahad Saha, Souvik Sahu, Abhishek Saurabh, Saket Upasana, Anannya Indian Inst Technol Jodhpur Jodhpur Rajasthan India Indian Inst Technol Hyderabad Hyderabad Telangana India HBNI Inst Math Sci Mumbai Maharashtra India HBNI Natl Inst Sci Educ & Res Mumbai Maharashtra India Univ Bergen Bergen Norway

ISBN: (纸本)9781611977554

Max-SAT with cardinality constraint (CC-Max-Sat) is one of the classical NP-complete problems, that generalizes Maximum Coverage, Partial Vertex Cover, Max-2-SAT with bisection constraints, and has been extensively studied across all algorithmic paradigms. In this problem, we are given a CNF-formula Phi, and a positive integer k, and the goal is to find an assignment ss with at most k variables set to true (also called a weight k-assignment) such that the number of clauses satisfied by ss is maximized. The problem is known to admit an approximation algorithm with factor 1 - 1/e, which is probably optimal. In fact, the problem is hard to approximate within 0.944, assuming Unique Games Conjecture, even when the input formula is 2-CNF. Furthermore, assuming Gap-Exponential Time Hypothesis (Gap-ETH), for any epsilon > 0 and any function h, no h(k)(n+ m)(o(k)) time algorithm can approximate Maximum Coverage (a monotone version of CC-Max-Sat) with n elements and m sets to within a factor (1 - 1/e + epsilon), even with a promise that there exist k sets that fully cover the whole universe. These intractable results lead us to explore families of formula, where we can circumvent these barriers. Towards this we consider K-d,K-d-free formulas (that is, the clause-variable incidence bipartite graph of the formula excludes K-d,K-d as an induced subgraph). We show that for every epsilon > 0, there exists an algorithm for CC-Max-Sat on K-d,K-d-free formulas with approximation ratio (1-epsilon) and running in time 2(O((dk/epsilon)d))(n+ m)(O(1)) (these algorithms are called FPT-AS). For, Maximum Coverage on Kd,d-free set families, we obtain FPT-AS with running time (dk/epsilon)O((dk))n(O(1)). Our second result considers "optimizing k", with fixed covering constraint for the Maximum Coverage problem. To explain our result, we first recast the Maximum Coverage problem as the Max Red Blue Dominating Set with Covering Constraint problem. Here, input is a bipartite graph G = (A, B, E),

关键词： approximation algorithms

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