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A Stochastic Particle Variational Bayesian inference Inspired Deep-Unfolding Network for Sensing Over Wireless Networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第10期42卷 2832-2846页

作者： Hu, Zhixiang Liu, An Xu, Wenkang Quek, Tony Q. S. Zhao, Minjian Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Singapore Univ Technol & Design Dept Informat Syst Technol & Design Singapore 487372 Singapore

Future wireless networks are envisioned to provide ubiquitous sensing services, driving a substantial demand for multi-dimensional non-convex parameter estimation. This entails dealing with non-convex likelihood functions containing numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and leveraging prior information, but poses a long-standing challenge on computing intractable posterior distributions. Most existing variational methods depend on specific distribution assumptions for obtaining closed-form solutions, and are difficult to apply in practical scenarios. Given these challenges, firstly, we propose a parallel stochastic particle VBI (PSPVBI) algorithm. Due to innovations like particle approximation, added updates of particle positions, and parallel stochastic successive convex approximation (PSSCA), PSPVBI can flexibly drive particles to fit the posterior distribution with acceptable complexity, yielding high-precision estimates of the target parameters. Furthermore, additional speedup can be obtained by deep-unfolding this algorithm. Specifically, superior hyperparameters are learned to dramatically reduce iterations. In this PSPVBI-induced deep-unfolding network, some techniques related to gradient computation, data sub-sampling, differentiable sampling, and generalization ability are also employed to facilitate the practical deployment. Finally, we apply the learnable PSPVBI (LPSPVBI) to solve two important positioning/sensing problems over wireless networks. Simulations indicate that the LPSPVBI algorithm outperforms existing solutions.

关键词： inference algorithms Bayes methods Approximation algorithms Convergence Sensors Optimization Stochastic processes Non-convex parameter estimation variational Bayesian inference deep-unfolding particle approximation stochastic successive convex approximation

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SBL algorithms for the Multiple Measurement Vector Problem: New Modeling and inference Methods

SBL Algorithms for the Multiple Measurement Vector Problem: ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Vinay Kanakeri Florian Meyer Bhaskar D. Rao Department of Electrical and Computer Engineering University of California San Diego

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper introduces new and practically relevant non-Gaussian priors for the Sparse Bayesian Learning (SBL) framework applied to the Multiple Measurement Vector (MMV) problem. We extend the Gaussian Scale Mixture (GSM) framework to model prior distributions for row vectors, exploring the use of shared and different hyperparameters across different measurements. We propose Expectation Maximization (EM) based algorithms to estimate the parameters of the prior density along with the hyperparameters. To promote sparsity more effectively in a non-Gaussian setting, we show the importance of incorporating learning of the parameters of the mixing density. Such an approach effectively utilizes the common support notion in the MMV problem and promotes sparsity without explicitly imposing a sparsity-promoting prior, indicating the methods’ robustness to model mismatches. Numerical simulations are provided to compare the proposed approaches with the existing SBL algorithm for the MMV problem.

关键词： GSM Signal processing algorithms Signal processing Numerical simulation Vectors inference algorithms Robustness Numerical models Bayes methods Speech processing

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Beyond the Cloud: Edge inference for Generative Large Language Models in Wireless Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 643-658页

作者： Zhang, Xinyuan Nie, Jiangtian Huang, Yudong Xie, Gaochang Xiong, Zehui Liu, Jiang Niyato, Dusit Shen, Xuemin BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Singapore Univ Technol & Design Informat Syst Technol & Design ISTD Pillar Singapore 487372 Singapore Purple Mt Labs Nanjing 211111 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Generative Artificial Intelligenge (GAI) is revolutionizing the world with its unprecedented content creation ability. Large Language Model (LLM) is one of its most embraced branches. However, due to LLM's substantial size and resource-intensive nature, it is cloud-hosted, raising concerns about privacy, usage limitations, and latency. In this paper, we propose to utilize ubiquitous distributed wireless edge computing resources for real-time LLM inference. Specifically, we introduce a novel LLM edge inference framework, incorporating batching and model quantization to ensure high throughput inference on resource-limited edge devices. Then, based on the architecture of transformer decoder-based LLMs, we formulate an edge inference optimization problem which is NP-hard, considering batch scheduling and joint allocation of communication and computation resources. The solution is the optimal throughput under edge resource constraints and heterogeneous user requirements on latency and accuracy. To solve this NP-hard problem, we develop an OT-GAH (Optimal Tree-search with Generalized Assignment Heuristics) algorithm with reasonable complexity and 1/2-approximation ratio. We first design the OT algorithm with online tree-pruning for single-edge-node multi-user case, which navigates the inference request selection within the tree structure to miximize throughput. We then consider the multi-edge-node case and propose the GAH algorithm, which recrusively invokes the OT in each node's inference scheduling iteration. Simulation results demonstrate the superiority of OT-GAH batching over other benchmarks, revealing an over 45% time complexity reduction compared to brute-force searching.

关键词： Computational modeling Quantization (signal) Accuracy inference algorithms Throughput Wireless networks Transformers Resource management Optimization Chatbots Generative AI edge inference wireless networks multiuser edge computing

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A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy inference System

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IEEE ACCESS 2024年 12卷 26683-26695页

作者： Ramesh, B. Lakshmanna, Kuruva Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamil Nadu India

Diabetes is the "mother of all diseases" as it affects multiple organs of body of an individual in some way. Its timely detection and management are critically important. Otherwise, the long run, it can cause several complications in a diabetic. Heart disease is one of the major complications of *** work proposed an Optimal Scrutiny Boosted Graph Convolutional LSTM (O-SBGC-LSTM), SBGC-LSTM enhanced by Eurygaster Optimization Algorithm (EOA) to tune hyperparameters for early prevention and detection of diabetes disease. This work proposed an Optimal Scrutiny Boosted Graph Convolutional LSTM (O-SBGC-LSTM), SBGC-LSTM enhanced by Eurygaster Optimization Algorithm (EOA) to tune hyperparameters for early prevention and detection of diabetes disease. This method not only captures discriminative features in spatial configuration and temporal dynamics but also explore the co-occurrence relationship between spatial and temporal domains. This method also presents a temporal hierarchical architecture to increase temporal receptive fields of top SBGC-LSTM layer, which boosts the ability to learn high-level semantic representation and significantly reduces computation cost. The performance of O-SBGC-LSTM was found overall to be satisfactory, reaching >98% accuracy in most studies. In comparison with classic machine learning approaches, proposed hybrid DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Furthermore, prevention is better than cure. Additionally, employed fuzzy based inference techniques to enhance the prevention procedure using suggestion table.

关键词： Diabetes Diseases Heart Fuzzy logic Predictive models Prediction algorithms Training Detection algorithms Coronary arteriosclerosis Convolutional neural networks Long short term memory Fuzzy systems inference algorithms detection prevention coronary heart disease optimal scrutiny boosted graph convolutional LSTM deep learning fuzzy inference systems

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Spatial-Temporal Data inference With Graph Attention Neural Networks in Sparse Mobile Crowdsensing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4617-4626页

作者： Yang, Guisong Wen, Panpan Liu, Yutong Kong, Linghe Liu, Yunhuai Univ Shanghai Sci & Technol Dept Comp Sci & Engn Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Peking Univ Dept Comp Sci & Engn Beijing 100871 Peoples R China Peking Univ Chongqing Res Inst Big Data Chongqing Peoples R China

Sparse mobile crowdsensing, driven by the increasing ubiquity of smartphones, has emerged as a popular method for data collection. This approach can reconstruct the whole sensing map by inferring missing temporal data from their spatial-temporal correlations to sparse samples. Traditional methods face challenges in capturing non-linear correlations in sensors and error accumulation. To address these issues, we propose a dynamic signal map reconstruction model, i.e., Spatial-Temporal Graph Attention (STGA). Specifically, STGA first applies graph embedding to convert sensing points into graph structures, followed by using a message-passing mechanism based on Graph Attention Networks (GAT) to capture complex spatial-temporal dependent features in sensing data. Then, we propose hierarchical attention mechanism, where different nodes are selected and weighted, and the temporal and spatial sequences of the nodes are stratified separately. By collecting a small amount of observation data, the spatial-temporal characteristics of nodes in the current sparse sensing data graph are effectively learned and the signal graph is updated. Attention mechanism quantitatively evaluates and weights the measurement points to further reduce the measurement error. Evaluations conducted on three typical urban sensing datasets demonstrate the effectiveness of the proposed methods in enhancing the inference accuracy of sparse sensing data.

关键词： Sensors Crowdsensing Correlation inference algorithms Data models Costs Accuracy Sparse Mobile Crowdsensing Graph Convolution Networks Spatial-Temporal Correlations Signal Map

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High Efficiency inference Accelerating Algorithm for NOMA-Based Edge Intelligence

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第11期23卷 17539-17556页

作者： Yuan, Xin Li, Ning Zhang, Tuo Li, Muqing Chen, Yuwen Ortega, Jose Fernan Martinez Guo, Song Harbin Inst Technol Sch Ocean Engn Harbin 150001 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Beijing Univ Technol & Sci Coll Comp Sci Beijing 100124 Peoples R China Univ Politecn Madrid Dept Ingn & Arquitecturas Telematicas Madrid 28040 Spain Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

Even the artificial intelligence (AI) has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate. Thus, the model split inference is proposed to improve the performance of edge intelligence (EI), in which the AI model is divided into different sub-models and the resource-intensive sub-model is offloaded to edge server wirelessly for reducing resource requirements and inference latency. Unfortunately, with the sharp increasing of edge devices, the shortage of spectrum resource in edge network becomes seriously in recent years, which limits the performance improvement of EI. Refer to the NOMA-based edge computing (EC), integrating non-orthogonal multiple access (NOMA) technology with split inference in EI is attractive. However, the NOMA-based communication aspect and the influence of intermediate data transmission fail to be considered properly in model split inference of EI in previous works, and the sophistication in resource allocation caused by NOMA scheme makes it further complicated. Thus, the Effective Communication and Computing resource allocation algorithm is proposed in this paper for accelerating the split inference in NOMA-based EI, shorted as ECC. Specifically, the ECC takes the energy consumption and the inference latency into account to find the optimal model split strategy and resource allocation strategy (subchannel, transmission power, computing resource). Since the minimum inference delay and energy consumption cannot be satisfied simultaneously, the gradient descent (GD) based algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach (Li-GD) is developed to reduce the complexity of the GD algorithm caused by parameter discretization. The key idea of Li-GD is that: the initial value of the ith layer's GD procedure is selected from the optimal results of the former (i-1) layers' GD procedure whose intermediate data

关键词： Computational modeling Resource management inference algorithms Delays Servers NOMA Energy consumption Edge intelligence model split inference accelerating

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Location-Privacy-Aware Service Migration Against inference Attacks in Multiuser MEC Systems

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IEEE INTERNET OF THINGS JOURNAL 2024年第1期11卷 1413-1426页

作者： Wang, Weixu Zhou, Xiaobo Qiu, Tie He, Xin Ge, Shuxin Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China Anhui Normal Univ Sch Comp & Informat Wuhu 241002 Anhui Peoples R China

In multiaccess edge computing (MEC) systems, service migration has been extensively applied to ensure service quality by migrating services to follow mobile users. The existing migration methods mainly focus on optimizing service response latency and migration costs by predicting user's movements. However, some malicious adversaries can learn auxiliary knowledge, i.e., users' mobility model and service migration trajectory, and launch location inference attacks to infer user locations. This leads to serious personal security threats, like malvertising, fraud and kidnapping. In this article, we propose a location privacy-aware service migration method to against adversaries' location inference attacks in multiuser MEC systems. First, we adopt an entropy-based location privacy metric to accurately measure user's location privacy leakage risk. Then, we formulate the service migration progress as a joint optimization problem that minimizes service response latency and location privacy leakage risk. To cope with interuser interference, we developed a multiagent soft actor-critic (MASAC) algorithm to help users collaboratively make service migration decisions. Finally, simulations based on real-world user movement trajectories were conducted to demonstrate the superiority of the proposed method. Evaluation and analysis results showed that our proposed method can effectively protect user location privacy while maintaining a low service response latency.

关键词： Privacy Trajectory Interference Costs inference algorithms Q-learning Internet of Things Location privacy multiaccess edge computing (MEC) multiuser service migration soft actor-critic (SAC)

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Adversarial inference Control in Cyber-Physical Systems: A Bayesian Approach With Application to Smart Meters

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IEEE ACCESS 2024年 12卷 24933-24948页

作者： Avula, Ramana R. Oechtering, Tobias J. Mansson, Daniel RISE Res Inst Sweden Dept Electrificat & Reliabil S-50462 Gothenburg Sweden KTH Royal Inst Technol Div Intelligent Syst S-10044 Stockholm Sweden KTH Royal Inst Technol Div Electromagnet Engn & Fus Sci S-10044 Stockholm Sweden

With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user's control strategy. For finite state-space problems, we derive the fixed-point Bellman's equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible "Co-LivEn" dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.

关键词： Smart meters Cyber-physical systems Bayes methods Privacy Mathematical models Hidden Markov models Markov processes Adversarial machine learning Deep reinforcement learning Control systems Electricity supply industry Water resources Gas industry inference algorithms Adversarial inference Bayesian control cyber-physical systems deep reinforcement learning privacy control smart meters

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Analysis of Short Video Platform algorithms Based on Causal inference: The Formation and Decoupling of the Information Cocoon Effect

Analysis of Short Video Platform Algorithms Based on Causal ...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Jingya Cheng School of Culture and Communication Guangdong Business and Technology University Zhaoqing China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

Short video platforms push content to users through recommendation algorithms, which greatly improves user experience, but also triggers the information cocoon effect. This paper explores the impact of the recommendation algorithm of short video platforms on the information cocoon effect based on the theory of causal inference, and proposes algorithm design and model simulation methods to decouple the relationship between the information cocoon effect and content diversity. First, this paper constructs a causal inference model to identify the formation mechanism of the information cocoon effect in the recommendation algorithm. Then, by introducing a diversity constraint factor, adjustments are made in the recommendation algorithm to increase the probability that users receive different types of content, thereby reducing the generation of the information cocoon effect. The model simulation results show that without affecting the user experience, the optimized recommendation algorithm improves the information diversity index by 15.7 % , while the intensity of the information cocoon effect is reduced by 22.4%. This study shows that the optimization of the recommendation algorithm based on causal inference can effectively alleviate the information cocoon effect in the short video platform, improve the breadth of user information acquisition, and has high practical value.

关键词： Simulation inference algorithms User experience Indexes Internet of Things Computer security Optimization

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Arithmetic Vs. Expected Mean of Probabilistic Asynchronous Affine inference

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5593-5603页

作者： Apostolakis, Georgios Bletsas, Aggelos Tech Univ Crete Sch ECE Khania 73100 Greece Tech Univ Crete Telecommun Syst Inst TSI Khania 73100 Greece Rutgers Univ New Brunswick WINLAB Piscataway NJ 08854 USA Rutgers Univ New Brunswick Dept ECE Piscataway NJ 08854 USA State Univ New Jersey Piscataway NJ 08854 USA

Distributed execution of algorithms over various terminals is a topic that regains increasing popularity;when tolerance to failures is also required, asynchronous operation is brought to the light, while probabilistic asynchronous operation can model the probability of failure for each terminal. This work focuses on the probabilistic asynchronous affine update model, applicable in a wide range of inference algorithms, possibly executed over distributed terminals. The existing literature focuses on the asymptotic properties of the expected mean. Instead, this work offers the asymptotic analysis for the arithmetic mean, utilized for discovering fixed points, as it is the only quantity that can be practically offered experimentally. It is shown that the asymptotic behavior of the arithmetic mean is different than the expected mean's and a sufficient condition is provided for convergence of the arithmetic mean to a fixed point. The lack of necessity for this condition is explained and the subcases, where the arithmetic mean converges, diverges or has an unpredictable behavior, are distinguished. Additionally, cases where the individual iterations never converge (e.g., oscillate infinitely) but their arithmetic mean does and offers fixed point, are also highlighted. This is another concrete example of the arithmetic mean utility. Applications of the affine model are also briefly discussed. Finally, simulations corroborate theoretical findings for various affine model setups.

关键词： Arithmetic Vectors Convergence inference algorithms Probabilistic logic Signal processing algorithms Mathematical models Eigenvalues and eigenfunctions Covariance matrices Sufficient conditions Asynchrony affine iterations convergence region asymptotic properties arithmetic mean distributed implementation inference

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