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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2025年 6卷 434-452页

作者： Gao, Tiancheng Akrout, Mohamed Bellili, Faouzi Mezghani, Amine Univ Manitoba Elect & Comp Engn ECE Winnipeg MB R3T 5V6 Canada

Estimating time-varying signals becomes particularly challenging in the face of non-Gaussian (e.g., sparse) and/or rapidly time-varying process noise. By building upon the recent progress in the approximate message passing (AMP) paradigm, this paper unifies the vector variant of AMP (i.e., VAMP) with the Kalman filter (KF) into a unified message passing framework. The new algorithm (coined VAMP-KF) does not restrict the process noise to a specific structure (e.g., same support over time), thereby accounting for non-Gaussian process noise sources that are uncorrelated both component-wise and over time. For the sake of theoretical performance prediction, we conduct a state evolution (SE) analysis of the proposed algorithm and show its consistency with the asymptotic empirical mean-squared error (MSE). Numerical results using sparse noise dynamics with different sparsity ratios demonstrate unambiguously the effectiveness of the proposed VAMP-KF algorithm and its superiority over state-of-the-art algorithms both in terms of reconstruction accuracy and computational complexity.

关键词： Noise Vectors Noise measurement approximation algorithms Heuristic algorithms Message passing Covariance matrices Kalman filters Filtering Uncertainty Bayesian inference expectation propagation vector approximate message passing (VAMP) Kalman filter time-varying signals rapidly sparse process noise

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Aggressive approximation of the SoftMax Function for Power-Efficient Hardware Implementations

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1652-1656页

作者： Spagnolo, Fanny Perri, Stefania Corsonello, Pasquale Univ Calabria Dept Informat Modeling Elect & Syst Engn I-87036 Arcavacata Di Rende Italy Univ Calabria Dept Mech Energy & Management Engn I-87036 Arcavacata Di Rende Italy

Neural Network models most often exploit the SoftMax function in the classification stage for computing probabilities through exponentiation and division operations. To reduce the complexity and the energy consumption of such stage, several hardware-friendly approximation strategies have been disclosed in the recent past. This brief evaluates the effects of an aggressive approximation of the SoftMax layer on both classification accuracy and hardware characteristics. Experimental results demonstrate that the proposed circuit, when implemented in a 28 nm FDSOI technology, saves similar to 65% of silicon area with respect to competitors, dissipating less than 1 pJ. FPGA implementation results confirm a massive energy dissipation reduction with respect to the conventional baseline architecture, without introducing penalties in the Top-1 accuracy.

关键词： Computer architecture Table lookup Hardware approximation algorithms Fitting Energy consumption Task analysis Deep neural networks SoftMax approximate computing low-power hardware architectures

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Symbol-Level Precoding for MIMO ISAC Transmission Based on Interference Exploitation

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IEEE COMMUNICATIONS LETTERS 2024年第2期28卷 283-287页

作者： Chen, Yunwang Liu, Fan Liao, Zihan Dong, Fuwang Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China

Integrated sensing and communication (ISAC) is a key feature in 6G wireless communication systems, providing ubiquitous sensing and communication (S&C) services. In this letter, we focus on improving the performance of the ISAC system by utilizing constructive interference (CI) to optimize the sensing performance, which is characterized by the Cramer-Rao bound (CRB) of target response matrix (TRM) estimation. To achieve higher estimation accuracy while maintaining communication quality, we propose two methods: a heuristic semidefinite relaxation (SDR)-based alternative minimization approach and a successive convex approximation (SCA) method. These methods address the optimization problem by dealing with the non-convexity present in the objective function. The numerical results demonstrate the effectiveness of the proposed algorithms in enhancing the ISAC performance compared to the baseline block-level based precoding designs.

关键词： Sensors Interference Precoding Radar approximation algorithms Streams Optimization Integrated sensing and communication symbol-level precoding constructive interference semidefinite relaxation successive convex approximation

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Towards Multi-Pass Streaming Lower Bounds for Optimal approximation of Max-Cut 34

Towards Multi-Pass Streaming Lower Bounds for Optimal Approx...

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

作者： Chen, Lijie Kol, Gillat Paramonov, Dmitry Saxena, Raghuvansh R. Song, Zhao Yu, Huacheng Univ Calif Berkeley Berkeley CA 94720 USA Princeton Univ Princeton NJ USA Microsoft Corp Redmond WA 98052 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9781611977554

We consider the Max-Cut problem, asking how much space is needed by a streaming algorithm in order to estimate the value of the maximum cut in a graph. This problem has been extensively studied over the last decade, and we now have a near-optimal lower bound for one-pass streaming algorithms, showing that they require linear space to guarantee a better-than-2 approximation [50, 52]. This result relies on a lower bound for the cycle-finding problem, showing that it is hard for a one-pass streaming algorithm to find a cycle in a union of matchings. The end-goal of our research is to prove a similar lower bound for multi-pass streaming algorithms that guarantee a better-than-2 approximation for Max-Cut, a highly challenging open problem. In this paper, we take a significant step in this direction, showing that even o(log n)-pass streaming algorithms need n(Omega(1)) space to solve the cycle-finding problem. Our proof is quite involved, dividing the cycles in the graph into "short" and "long" cycles, and using tailor-made lower bound techniques to handle each case.

关键词： approximation algorithms

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Energy-Efficient Decentralized Federated Learning for UAV Swarm With Spiking Neural Networks and Leader Election Mechanism

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第10期13卷 2742-2746页

作者： Shang, Chen Hoang, Dinh Thai Hao, Min Niyato, Dusit Yu, Jiadong Hong Kong Univ Sci & Technol Guangzhou Internet Things Thrust Guangzhou 510230 Guangdong Peoples R China Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

Federated Learning (FL) has been considered a critical technique for assisting Unmanned Aerial Vehicle (UAV) swarm to efficiently perform tasks in dynamic environments. However, deploying FL in UAV swarm is constrained by the limited energy of the UAVs and the complex communication environments within UAV swarm networks. This letter introduces a leader election-assisted Spiking Neural Networks (SNNs)-driven decentralized FL framework for UAV swarm. This framework enables UAV swarm to train a high-performance FL model while minimizing energy and time consumption, thereby enhancing real-time decision ability of UAV swarm. In particular, the SNN-driven FL allows UAV swarm to train a shared model with less energy consumption through its discrete spike event. To this end, we conduct a systematic analysis of the training challenges associated with SNN-driven FL, and we then propose an approximate derivative algorithm to address these challenges. Furthermore, we develop an intelligent leader selection scheme based on Bayes theorem designed to reduce time consumption of model parameter transmission and accelerate the model aggregation. Simulation results show that the proposed scheme outperforms baseline schemes in terms of model performance, energy and time consumption.

关键词： Autonomous aerial vehicles Computational modeling Training Data models Energy consumption Task analysis approximation algorithms Unmanned aerial vehicle federated learning spiking neural network

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Optimizing Distribution Network Design Using Conic Relaxation for Maximum Cut Formulations

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2025年 72卷 1592-1607页

作者： Luo, Jian Song, Huimin Wu, Zhiqiao Zheng, Yukai Hainan Univ Int Business Sch Haikou 570228 Peoples R China Dongbei Univ Finance & Econ Sch Management Sci & Engn Dalian 116025 Peoples R China

In the home appliance retail industry, delivery services have faced challenging situations caused by cross-area distributions in recent years, making it necessary to determine the service area to reduce the distribution coupling of different areas. Given the special characteristics of home appliance products, we first propose a multidimensional weight model by incorporating a designed fuzzy membership function that corrects the cost errors brought about by travel distance. Determination of the service area is then formulated as multiple maximum cut problems. Moreover, with good theoretical properties, a linear conic programming (LCoP) algorithm is developed to obtain the proximate global optimum solution for the maximum cut problem by applying conic relaxation. The proposed LCoP algorithm is applied iteratively to solve the formulated maximum cut problems. From numerical results, the LCoP algorithm produces a better approximated maximum cut than Williamson and Goemans' algorithm and quantum approximate optimization algorithm, both of which become increasingly less effective as the problem size increases. Compared to the original RiRiShun Logistics (RRS) distribution network and tested algorithm, implementing the proposed planning approach benefit more to RRS with respect to the cross-area distribution frequency and transportation costs.

关键词： Costs Distribution networks approximation algorithms Transportation Optimization Computational modeling Industries Home appliances Resilience Planning Cross-area distribution distribution system linear conic programming algorithm (LCoP) maximum cut problem

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Low Complexity Design of Logistic Distance Metric Adaptive Filter for Impulsive Noise Environments

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2024年第8期32卷 1409-1413页

作者： Ghosh, Shouharda Meher, Pramod Kumar Ray, Dwaipayan George, Nithin V. Indian Inst Technol Gandhinagar Dept Elect Engn Gandhinagar 382355 India CV Raman Global Univ Dept Comp Sci & Engn Bhubaneswar 752054 Odisha India Qualcomm Bangalore Design Ctr Bengaluru 560066 India

In many practical scenarios, non-Gaussian noise contaminates the desired signal and introduces outliers. The recently proposed logistic distance metric adaptive filter (LDMAF) outperforms the existing algorithms and provides better performance in the presence of such outliers. There is a need for efficient hardware architecture for the implementation of LDMAF. This article proposes an efficient VLSI architecture of LDMAF. The implementation of error-gradient function of LDMAF puts significant implementation problem in terms of delay and cost. We introduce here an efficient tangent-based piecewise linear (TPL) approximation algorithm for implementing the corresponding architecture. The proposed approach improves the power, performance, and area (PPA) metrics over state-of-the-art implementations of other robust algorithms while meeting system performance within an acceptable deviation.

关键词： approximation algorithms Adaptive filters Computer architecture Very large scale integration Measurement Logistics Feedforward systems approximate computing impulsive noise logistic distance metric adaptive filter (LDMAF) piecewise linear approximation system identification VLSI architectures

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inSTEP: An In Situ Stochastic approximation based Scalar Quantizer for Narrowband IoT 22

inSTEP: An In Situ Stochastic Approximation based Scalar Qua...

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22nd IEEE Statistical Signal Processing Workshop (SSP)

作者： Anavangot, Vijay Indian Inst Technol Dept Elect Engn Mumbai 400076 Maharashtra India

ISBN: (纸本)9781665452458

Lack of long range connectivity and low bandwidth of LoRa networks have hindered NB-IoT deployments in large geographical areas. TV White Spaces (TVWS) offer a viable solution to address the limitations of LoRa by allowing unutilized TV spectrum to be shared with NB-IoT devices on an ad hoc basis. This is enabled by a geolocation database that will broadcast the utilized TV bands to IoT clients through a rate-constrained channel. However, there are regulatory and practical challenges in using the TVWS that are to be addressed. To overcome these challenges, we propose an adaptive data-driven quantizer design algorithm for sharing the utilized TV bands' data with IoT clients through the rate-constrained channel. This design is compliant with the regulatory norms and is capable of allocating unused TV bands dynamically. The performance results of our quantizer are discussed in the paper based on two TVWS datasets.

关键词： Quantization (signal) Statistical learning approximation algorithms

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Reinforcement Learning With Model Predictive Control for Highway Ramp Metering

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 5988-6004页

作者： Airaldi, Filippo De Schutter, Bart Dabiri, Azita Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance traffic flow management by use of an innovative approach to the problem of ramp metering control that embeds Reinforcement Learning (RL) techniques within the Model Predictive Control (MPC) framework. The control problem is formulated as an RL task by crafting a suitable stage cost function that is representative of the traffic conditions, variability in the control action, and violations of the constraint on the maximum number of vehicles in queue. An MPC-based RL approach, which leverages the MPC optimal problem as a function approximation for the RL algorithm, is proposed to learn to efficiently control an on-ramp and satisfy its constraints despite uncertainties in the system model and variable demands. Simulations are performed on a benchmark small-scale highway network to compare the proposed methodology against other state-of-the-art control approaches. Results show that, starting from an MPC controller that has an imprecise model and is poorly tuned, the proposed methodology is able to effectively learn to improve the control policy such that congestion in the network is reduced and constraints are satisfied, yielding an improved performance that is superior to the other controllers.

关键词： Predictive models Road transportation Uncertainty System identification Predictive control Prediction algorithms Function approximation Convergence approximation algorithms Accuracy Ramp metering reinforcement learning model predictive control

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Optimistic algorithms for Safe Linear Bandits Under General Constraints

IEEE OPEN JOURNAL OF CONTROL SYSTEMS

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IEEE OPEN JOURNAL OF CONTROL SYSTEMS 2025年 4卷 103-116页

作者： Hutchinson, Spencer Zibaie, Arghavan Pedarsani, Ramtin Alizadeh, Mahnoosh Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

The stochastic linear bandit problem has emerged as a fundamental building-block in machine learning and control, and a realistic model for many applications. By equipping this classical problem with safety constraints, the safe linear bandit problem further broadens its relevance to safety-critical applications. However, most existing algorithms for safe linear bandits only consider linear constraints, making them inadequate for many real-world applications, which often have non-linear constraints. To alleviate this limitation, we study the problem of safe linear bandits under general (non-linear) constraints. Under a novel constraint regularity condition that is weaker than convexity, we give two algorithms with O(d root T) regret. We then give efficient implementations of these algorithms for several specific settings. Lastly, we give simulation results demonstrating the effectiveness of our algorithms in choosing dynamic pricing signals for a demand response problem under distribution power flow constraints.

关键词： Heuristic algorithms Smart grids Safety Noise Computational modeling approximation algorithms Vectors Uncertainty Standards Load flow Safe bandit safe learning stochastic linear bandit

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