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A Novel Method to Solve the Maximum Weight Clique Problem for Instantly Decodable Network Coding

IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 2181-2192页

作者： Mei, Zhonghui Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China

Minimizing the decoding delay, the completion time, or the delivery time of instantly decodable network coding (IDNC) can all be approximated to a maximum weight clique (MWC) problem, which is well known to be NP hard. Due to its good tradeoff between performance and computational complexity, a heuristic approach named as maximum weight vertex (MWV) search is widely employed to select MWC for IDNC. However, in MWV, when there are few coding connection edges among the adjacent vertices of a vertex, its modified vertex weight cannot well reflect the weight of the MWC containing the vertex, which leads to incorrect selection of MWC. This paper proposes a new method to calculate the modified weight of a vertex by summing the weights of the vertices in the approximate maximum weight path (A-MWP) generated by this vertex. Since the vertices in an A-MWP can form a maximal clique, the proposed modified vertex weight may well indicate the weight of the MWC containing the vertex. The proposed algorithm has the same computational complexity as the MWV algorithm. Simulation results show that when employing any of the three performance metrics of IDNC, our proposed algorithm can achieve better system performance than the MWV algorithm.

关键词： Receivers Decoding Delays approximation algorithms Search problems Schedules Network coding Weight measurement Velocity measurement Time measurement Instantly decodable network coding (IDNC) maximum weight clique (MWC) problem wireless broadcast

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A Layered Cross Correlation Back-Projection Algorithm Based on Ray-Theory for Electromagnetic Imaging in Stratified Medium

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Lan, Tian Wang, Jingbo Liao, Jiancheng Gong, Junbo Yang, Xiaopeng Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China

In this article, we present a layered cross correlation back-projection (LCBP) method based on ray-theory for electromagnetic (EM) imaging in stratified medium. To argue the challenge of resolving time delay in the stratified medium in traditional time-domain methods, we first construct a ray propagation model, then calculate the time delay at any point on the path and solve the sparse time delay at the deep position by linear interpolation, Finally, a clutter suppression method is used to reduce interference through the cross correlation of signals between different subapertures. LCBP achieves fast imaging in a stratified medium environment whereas retaining the advantages of the BP algorithm in the focusing ability. In order to verify the performance of the proposed method, the simulation and experimental data are carried out with comparisons of the phase shift migration (PSM) imaging method and layered range migration (LRM), and the proposed method exhibits excellent imaging quality.

关键词： Imaging Mathematical models Delay effects Electromagnetic scattering Antenna measurements approximation algorithms Radar imaging Permittivity Vectors Electromagnetics Cross correlation back-projection (BP) electromagnetic (EM) imaging ray theory stratified medium

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A 3-D Track Reconstruction Algorithm for Preresearch of the STCF MDC L1 Trigger

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2025年第3期72卷 429-437页

作者： Hao, Yidi Feng, Changqing Zhou, Zixuan Dong, Wenhao Fang, Zhujun Hu, Xueye Zhou, Hang Liu, Shubin Univ Sci & Technol China State Key Lab Particle Detect & Elect Hefei 230026 Peoples R China Univ Sci & Technol China Dept Modern Phys Hefei 230026 Peoples R China

The proposed Super Tau Charm Facility (STCF) is an electron-positron collider with high luminosity. Under the conditions of a high radiation, high counting-rate environment, and a pure physics event rate of up to 400 kHz, for a Level 1 (L1) trigger system, it is crucial to suppress background events to an acceptable rate. To this end, this study presents a 3-D track reconstruction algorithm for the preresearch of the STCF main drift chamber (MDC) L1 trigger. The proposed algorithm can reject tracks outside the interaction region and provide 3-D track information to the global trigger logic (GTL) of an L1 trigger for further analysis with other subdetectors. By using hit information from the MDC and the transverse momentum ( p(T)) and azimuthal angle (phi) from previous research on 2-D reconstruction, this study reconstructs the z (longitudinal) position for the track vertex using a neural network-based method with an innovative stereo track segment (TS) design. The neural network is trained using high granularity quantization (HGQ) to reduce resource consumption while maintaining resolution. The proposed method is implemented into a field-programmable gate array (FPGA) using the hls4ml. The achieved resolution of the z-vertex reconstruction of a single track ( z is an element of [-50, 50] cm) is approximately 2.8 cm, which can ensure rejecting 97% beam background tracks in a +/- 3 sigma interval.

关键词： Wires Three-dimensional displays Reconstruction algorithms Detectors Physics Data acquisition Neural networks approximation algorithms Monte Carlo methods Logic Hls4ml Level 1 (L1) trigger algorithm main drift chamber (MDC) multilayer perceptron (MLP) neural network Super Tau Charm Facility (STCF)

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Off-Policy Conservative Distributional Reinforcement Learning With Safety Constraints

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 2033-2045页

作者： Zhang, Hengrui Lin, Youfang Han, Sheng Wang, Shuo Lv, Kai Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing Key Lab Traff Data Min & Embodied Intellig Beijing 100044 Peoples R China

Safe exploration can be regarded as a constrained Markov decision problem (CMDP) where the expected long-term cost is constrained. Previous off-policy algorithms convert the constrained optimization problem into the corresponding unconstrained dual problem by introducing the Lagrangian relaxation technique. However, the cost function of the above algorithms provides inaccurate estimations and causes the instability of the Lagrange multiplier learning. In this article, we present a novel off-policy reinforcement learning (RL) algorithm called conservative distributional maximum a posteriori policy optimization (CDMPO). At first, to accurately judge whether the current situation satisfies the constraints, CDMPO adapts distributional RL method to estimate the Q-function and C-function. Then, CDMPO uses a conservative value function loss to reduce the number of violations of constraints during the exploration process. In addition, we utilize adaptive proportional integral derivative (APID) to update the Lagrange multiplier stably. In our experiments, we select eight representative constrained tasks from two well-known safe RL benchmarks (Safety Gym and Bullet Safety Gym), providing a comprehensive evaluation of our methods across diverse scenarios. Empirical results show that the proposed method has fewer violations of constraints in the early exploration process. The final test results also illustrate that our method has better-risk control capabilities.

关键词： Safety Estimation Reinforcement learning Oscillators approximation algorithms Costs Constraint optimization Markov decision processes Cost function Control systems Constrained Markov decision process (CMDP) constrained reinforcement learning (CRL) reinforcement learning (RL) safe exploration

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A Novel Sinusoidal Encoder-to-Digital Converter Based on Integrated Phase Shift and Interpolation Technique

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Bingyuan Lai, Xinquan Niu, Zhiwen Li, Linxi Xidian Univ Minist Educ Inst Elect Comp Aided Design Xian 710071 Peoples R China Xidian Univ Minist Educ Key Lab High Speed Circuit Design & Electromagnet Xian 710071 Peoples R China

Sinusoidal encoders are used to measure angular or linear displacement. These encoders provide electrical signals depending on the sine/cosine function of the position theta . A suitable converter is necessary to derive theta from sine/cosine signals (SCSs). This article proposes a novel demodulation method with low cost, low power consumption, and high accuracy. The proposed strategy converts the SCSs into a pseudolinear constructor using analog phase shift technology at first. Nonlinearity quantization of the pseudolinear constructor determines coarse phase theta(C) indirectly. Then, a fine measurement theta(F) is derived from the residual signals by the analog interpolation technology. These two parts are added to yield a precise position measurement theta(es) . The theoretical maximum error of the proposed algorithm is only 2.88x 10 -7 degrees over a full range. The proposed converter has been fabricated in the 0.18- mu m CMOS technology with an active silicon area of 0.5 mm(2). The experimental results prove the converter feasibility with 0.33 degrees accuracy with a five-pole inductive angle sensor, occupying 3.3-mW power consumption. This article describes all the details of the theory, simulation, and experimental results.

关键词： Demodulation approximation algorithms Accuracy Interpolation Integrated circuits Data mining Training Robustness Resistors Linearity Angle demodulation integrated circuit (IC) interpolation phase shift position measurement pseudolinearization ratio-metric linearization sinusoidal encoder

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Speeding-Up Evolutionary algorithms to Solve Black-Box Optimization Problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 117-131页

作者： Echevarrieta, Judith Arza, Etor Perez, Aritz Basque Ctr Appl Math Machine Learning Dept Bilbao 48009 Spain

Population-based evolutionary algorithms are often considered when approaching computationally expensive black-box optimization problems. They employ a selection mechanism to choose the best solutions from a given population after comparing their objective values, which are then used to generate the next population. This iterative process explores the solution space efficiently, leading to improved solutions over time. However, one of the challenges of these algorithms is that they require a large number of evaluations to provide a quality solution, which might be computationally expensive when the evaluation cost is high. In some cases, it is possible to replace the original objective function with a less accurate approximation of lower cost. This introduces a tradeoff between the evaluation cost and its accuracy. In this article, we propose a technique capable of choosing an appropriate approximate function cost during the execution of the optimization algorithm. The proposal finds the minimum evaluation cost at which the solutions are still properly ranked, and consequently, more evaluations can be computed in the same amount of time with minimal accuracy loss. An experimental section on four very different problems reveals that the proposed approach can reach the same objective value in less than half of the time in certain cases.

关键词： Costs Linear programming Optimization Proposals Computational modeling approximation algorithms Function approximation Approximate objective function computationally expensive black-box problem evaluation cost population-based evolutionary algorithm

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A Low-Complexity Detection Framework for Signed Quadrature Spatial Modulation Based on Approximated MMSE Sparse Detectors

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IEEE SYSTEMS JOURNAL 2025年第1期19卷 32-42页

作者： Albreem, Mahmoud A. Al Habbash, Alaa H. Abu-Hudrouss, Ammar M. EL Astal, M. -t. Univ Sharjah Dept Elect Engn Sharjah 27272 U Arab Emirates Palestinian ICT Res Agcy P ICTRA Gaza 860 Palestine Islamic Univ Gaza Sch Engn Gaza 41256118 Palestine

The design of low-complexity data detection techniques for massive multiple-input multiple-output (mMIMO) systems continues to attract considerable industry and research attention due to the critical need to achieve the right tradeoff between complexity and performance, especially with the signed quadrature spatial modulation (SQSM) scheme. However, the SQSM scheme attains a high spectral efficiency and good performance but suffers from a high computational complexity with mMIMO systems. In this article, we propose an efficient low-complexity detection framework for the SQSM scheme. Sparsity detection is amalgamated in this article with minimum mean-square error (MMSE) detector by decoupling the detection of the real and imaginary vector streams. Unfortunately, the MMSE-based detector has a matrix inversion which incurs a high computational complexity. Therefore, we employed several iterative methods;i.e., conjugate gradient and Gauss-Seidel, to avoid the exact matrix inversion, and hence, the computational complexity is significantly reduced. Moreover, the proposed framework can host other iterative methods such as the JA, successive over relaxation, accelerated over relaxation, Neumann series, Newton iteration, two-parameter over relaxation, and Richardson methods. The proposed detection framework attains a significant complexity reduction with a small or insignificant deterioration in the performance.

关键词： Detectors Symbols Computational complexity Transmitting antennas Iterative methods Receiving antennas Vectors approximation algorithms Matching pursuit algorithms GSM Fifth generation (5G) conjugate gradient (CG) Gauss-Seidel minimum mean-square error (MMSE) massive multiple-input multiple-output (MIMO) (mMIMO) signed quadrature spatial modulation (SQSM)

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Hybrid Block Successive approximation for One-Sided Non-Convex Min-Max Problems: algorithms and Applications

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 3676-3691页

作者： Lu, Songtao Tsaknakis, Ioannis Hong, Mingyi Chen, Yongxin Univ Minnesota Minneapolis MN 55455 USA IBM Thomas J Watson Res Ctr IBM Res AI Yorktown Hts NY 10598 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

The min-max problem, also known as the saddle point problem, is a class of optimization problems which minimizes and maximizes two subsets of variables simultaneously. This class of problems can be used to formulate a wide range of signal processing and communication (SPCOM) problems. Despite its popularity, most existing theory for this class has been mainly developed for problems with certain special convex-concave structure. Therefore, it cannot be used to guide the algorithm design for many interesting problems in SPCOM, where various kinds of non-convexity arise. In this work, we consider a block-wise one-sided non-convex min-max problem, in which the minimization problem consists of multiple blocks and is non-convex, while the maximization problem is (strongly) concave. We propose a class of simple algorithms named Hybrid Block Successive approximation (HiBSA), which alternatingly performs gradient descent-type steps for the minimization blocks and gradient ascent-type steps for the maximization problem. A key element in the proposed algorithm is the use of certain regularization and penalty sequences, which stabilize the algorithm and ensure convergence. We show that HiBSA converges to some properly defined first-order stationary solutions with quantifiable global rates. To validate the efficiency of the proposed algorithms, we conduct numerical tests on a number of problems, including the robust learning problem, the non-convex min-utility maximization problems, and certain wireless jamming problem arising in interfering channels.

关键词： Signal processing algorithms approximation algorithms Optimization Convergence Minimization Jamming Power control Min-max optimization saddle point problems block successive approximation gradient descent and ascent

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CRS: A Cost-Aware Resource Scheduling Framework for Deep Learning Task Orchestration in Mobile Clouds

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第2期24卷 600-613页

作者： Xiao, Linchang Xiao, Zili Wu, Di Hu, Miao Zhou, Yipeng Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Macquarie Univ Dept Comp Fac Sci & Engn Sydney NSW 2112 Australia

Deep learning (DL) has found extensive application in supporting various mobile applications. The efficient execution of DL tasks is paramount for ensuring the effectiveness of AI-driven mobile applications. While previous research has predominantly focused on minimizing the completion time of DL tasks, the associated cost of execution has often been overlooked. Nonetheless, cost becomes a critical factor, particularly when utilizing DL infrastructure rented from third-party cloud service providers. In this paper, we propose a cost-aware resource scheduling framework named CRS for orchestrating DL task execution in mobile cloud systems. Our aim is to minimize server rental costs by strategically orchestrating DL jobs with diverse deadlines and workload scales across rented cloud servers. We formally define the problem and prove its NP-hardness by reducing it to a multiple knapsack problem (MKP). To solve this problem, we devise an approximation algorithm with a guaranteed upper bound performance ratio of 1 + 1/e-1. We evaluate CRS against state-of-the-art baselines through simulations of various job arrival scenarios in a real elastic mobile cloud system. The results demonstrate that CRS, on average, reduces rental costs by 45.1% compared to other baselines, while simultaneously achieving a shorter average job completion time (JCT) and maximum job completion time (i.e., makespan).

关键词： Graphics processing units Servers Cloud computing Costs approximation algorithms Dynamic scheduling Training Deadline constraints deep learning task job completion time job scheduling mobile cloud system

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Enhanced Performance of New Scaling-Free CORDIC for Memory-Based Fast Fourier Transform Architecture

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IEEE ACCESS 2025年 13卷 19828-19844页

作者： Paramasivam, C. Chauhan, Sandeep Singh Meena, Veerpratap Sreejagathi, A. Hasini, B. A. V. N. Kishore, K. L. K. Vamsikrishna, T. V. N. G. Sai, M. Durga Ananta Didi, Abdessamad Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Bengaluru 560035 India Natl Inst Technol Jamshedpur Dept Elect Engn Jamshedpur 831014 Jharkhand India Natl Energy Ctr Nucl Sci & Technol Rabat 10001 Morocco

Coordinate rotation digital computer (CORDIC) algorithm is an iterative method and it performs the vector rotation operation by micro-rotation with scaling operation in each iteration. This study introduces a high-performance power-efficient new scaling-free coordinate rotation digital computer (NSF-CORDIC) algorithm to perform vector rotation operation in circular coordinates. The scaling operation required in the existing algorithm has been completely removed by the fourth-order approximation of Taylor series (TS). The number of iterations is reduced by an optimized shift value prediction technique for known and fixed angles, like the twiddle factor angle available in the fast Fourier transform (FFT) algorithm. The angle of convergence (AOC) of the algorithm is 57.1 degrees, and it is extended to 180 degrees using the pre-rotation operation and optimized shift value prediction technique. In addition to that, the new CORDIC cell architecture is designed to perform Taylor series-based iterations. Further, a memory-based FFT architecture is designed using a new CORDIC cell. In the FFT architecture, the CORDIC cell performs all the multiplication of twiddle factor. Therefore, the complex constant multiplier required to execute the twiddle factor multiplication is eliminated. The proposed architectures are implemented in the Zynq-7ZC706 FPGA board using the Vivado EDA tool. The scaling-free CORDIC cell architecture has 31% and 3% slice reduction, 72% and 45% slice delay product reduction, and 38% and 23% power reduction compared to the two different existing scaling-free CORDIC designs. The memory-based FFT architecture has 37.8% and 19.6% slice reduction, 38.4% and 27% slice delay product reduction, and 45% and 34% power reduction compared to the two different existing memory-based FFT architecture.

关键词： Computer architecture Fast Fourier transforms Vectors approximation algorithms Microprocessors Read only memory Prediction algorithms Transforms Memory architecture Delays Coordinate rotation digital computer (CORDIC) fast Fourier transform (FFT) memory-based FFT architecture new scaling-free CORIDC micro-rotation scaling operation

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