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decentralized Expectation Consistent Signal Recovery for Phase Retrieval

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 1484-1499页

作者： Wang, Chang-Jen Wen, Chao-Kai Tsai, Shang-Ho Jin, Shi Natl Chiao Tung Univ Inst Elect & Control Engn Hsinchu 30010 Taiwan Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 804 Taiwan Natl Chiao Tung Univ Dept Elect Engn Hsinchu 30010 Taiwan Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

In this study, we present a phase retrieval solution that aims to recover signals from noisy phaseless measurements. A recently proposed scheme known as generalized expectation consistent signal recovery (GEC-SR), has shown better accuracy, speed, and robustness than many existing methods. However, sensing high-resolution images with large transform matrices presents a computational burden for GEC-SR, thereby limiting its applications to areas, such as real-time implementation. Moreover, GEC-SR does not support distributed computing, which is an important requirement to modern computing. To address these issues, we propose a novel decentralized algorithm called & x201C;deGEC-SR & x201D;by leveraging the core framework of GEC-SR. deGEC-SR exhibits excellent performance similar to GEC-SR but runs tens to hundreds of times faster than GEC-SR. We derive the theoretical state evolution for deGEC-SR and demonstrate its accuracy using numerical results. Analysis allows quick generation of performance predictions and enriches our understanding on the proposed algorithm.

关键词： Signal processing algorithms Transforms Approximation algorithms Inference algorithms Robustness Bayes methods Phase measurement Phase retrieval Bayes-optimal inference expectation consistent decentralized algorithm distributed processing

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decentralized User Scheduling for Rate-Constrained Sum-Utility Maximization in the MIMO IBC

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第10期68卷 6215-6229页

作者： Antonioli, Roberto Pinto Fodor, Gabor Soldati, Pablo Maciel, Tarcisio Ferreira Univ Fed Ceara Wireless Telecom Res Grp BR-60440900 Fortaleza Ceara Brazil Ericsson Res Radio Dept S-16480 Stockholm Sweden KTH Royal Inst Technol Div Decis & Control Syst S-10044 Stockholm Sweden Ericsson Res Stand & Technol S-16480 Stockholm Sweden

While the problems of sum-rate maximization and sum-power minimization subject to quality of service (QoS) constraints in the multiple input multiple output interference broadcast channel (MIMO IBC) have been widely studied, most of the proposed solutions have neglected the user scheduling aspect assuming that a feasible set of users has been previously selected. However, ensuring QoS for each user in the MIMO IBC involves the joint optimization of transmit/receive beamforming vectors, transmit powers, and user scheduling variables. To address the full problem, we propose a novel formulation of a rate-constrained sum-utility maximization problem which allows to either deactivate users or minimize the QoS degradation for some scheduled users in infeasible scenarios. Remarkably, this is achieved avoiding the complexity of traditional combinatorial formulations, but rather by introducing a novel expression of the QoS constraints that allows to solve the problem in a continuous domain. We propose centralized and decentralized solutions, where the decentralized solutions focus on practical design and low signaling overhead. The proposed solutions are then compared with benchmarking algorithms, where we show the effectiveness of the joint scheduling and transceiver design as well as the flexibility of the proposed solution performing advantageously in several MIMO IBC scenarios.

关键词： Quality of service MIMO communication Interference Scheduling Transceivers Signal to noise ratio Degradation User scheduling transceiver design decentralized algorithm MIMO IBC sum-rate maximization

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Energy-Efficient Base Station Association and Beamforming for Multi-Cell Multiuser Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第4期19卷 2841-2854页

作者： An, Jianping Zhang, Yihao Gao, Xiaozheng Yang, Kai Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

This paper investigates the joint base station (BS) association and beamforming for energy efficiency maximization in coordinated multi-cell multiuser downlink systems. In particular, we assume that only the channel distribution information is known to the BSs. The considered problem is difficult to be solved optimally due to the non-smooth and non-convex functions in the formulation. Therefore, we propose an iterative suboptimal algorithm to solve the problem efficiently based on the successive convex approximation (SCA). More specifically, the convex approximation of the original problem at each iteration can be solved efficiently by the second-order cone programming and the solution obtained by the proposed algorithm satisfies the generalized Karush-Kuhn-Tucker (KKT) conditions. To facilitate the implementation of decentralized beamforming, we transform the convex approximation problem at each iteration of the SCA into an equivalent form, which is amenable to applying the alternating direction method of multipliers (ADMM). By combining the SCA and the ADMM, a decentralized energy-efficient beamforming algorithm is proposed. Numerical results are presented to show the performance of the proposed algorithms.

关键词： Energy efficient base station association coordinated beamforming outage probability decentralized algorithm

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Zonally Robust decentralized Optimization for Global Energy Interconnection: Case Study on Northeast Asian Countries

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2020年第4期17卷 2120-2129页

作者： Ding, Tao Yang, Qingrun Wen, Ya Ning, Ye Yang, Yongheng Blaabjerg, Frede Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Peoples R China Global Energy Interconnect Res Inst Beijing 100072 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Nowadays, the entire world is facing challenges in energy and environment. To resolve these problems, the power systems are interconnected to promote the development of renewable energy sources (RESs). However, the economic dispatch (ED) problem for the global energy interconnection (GEI) should tackle two issues: 1) handle the uncertainty from RES and allocate the responsibility among the interconnected countries and 2) protect the information privacy through the dispatch. Motivated by the above, this article proposes a zonally adjustable robust decentralized ED model for the GEI. In the model, each country is only responsible for its own uncertainty, and tie-line power flows remain unchanged under uncertainties. Moreover, an alternating direction method of multipliers (ADMM)-based fully distributed algorithm is used, in which only limited information should be exchanged between neighboring countries. Finally, a case study on the Northeast Asian countries verifies the effectiveness of the proposed method. Note to Practitioners-Since the renewable energy generation has a spatial correlation among regional countries, global energy interconnection (GEI) aims to combine several power systems together to promote the renewable energy accommodation. However, two problems need to be considered: 1) Information Privacy: The information privacy of the power system in each country should be preserved, which prevents the GEI from conducting a centralized optimal dispatch framework and 2) Uncertainty: The uncertain output of renewable energy resources brings challenge to the power system secure operation. The main contribution of this article is to set up a zonally robust decentralized optimization for the GEI, where the zonally robust economic dispatch (ED) is conducted by the area control error (ACE) system to manage the difference between scheduled and actual generation under the uncertainties, and the alternating direction method of multipliers (ADMMs) algorithm is adopted f

关键词： Robustness Uncertainty Optimization Economics Power systems Distributed algorithms Privacy decentralized algorithm economic dispatch (ED) global energy interconnection (GEI) zonal robustness

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decentralized dynamic functional network connectivity: State analysis in collaborative settings

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HUMAN BRAIN MAPPING 2020年第11期41卷 2909-2925页

作者： Baker, Bradley T. Damaraju, Eswar Silva, Rogers F. Plis, Sergey M. Calhoun, Vince D. Emory Univ Georgia State Univ Georgia Inst Technol Triinst Res Ctr Neuroimaging & Data Sci TReNDS Atlanta GA 30322 USA Mind Res Network Albuquerque NM USA Univ New Mexico Albuquerque NM 87131 USA

As neuroimaging data increase in complexity and related analytical problems follow suite, more researchers are drawn to collaborative frameworks that leverage data sets from multiple data-collection sites to balance out the complexity with an increased sample size. Although centralized data-collection approaches have dominated the collaborative scene, a number of decentralized approaches-those that avoid gathering data at a shared central store-have grown in popularity. We expect the prevalence of decentralized approaches to continue as privacy risks and communication overhead become increasingly important for researchers. In this article, we develop, implement and evaluate a decentralized version of one such widely used tool: dynamic functional network connectivity. Our resulting algorithm, decentralized dynamic functional network connectivity (ddFNC), synthesizes a new, decentralized group independent component analysis algorithm (dgICA) with algorithms for decentralized k-means clustering. We compare both individual decentralized components and the full resulting decentralized analysis pipeline against centralized counterparts on the same data, and show that both provide comparable performance. Additionally, we perform several experiments which evaluate the communication overhead and convergence behavior of various decentralization strategies and decentralized clustering algorithms. Our analysis indicates that ddFNC is a fine candidate for facilitating decentralized collaboration between neuroimaging researchers, and stands ready for the inclusion of privacy-enabling modifications, such as differential privacy.

关键词： brain imaging data sharing decentralized decentralized algorithm dFNC dynamic functional network connectivity

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Phase Retrieval With Learning Unfolded Expectation Consistent Signal Recovery algorithm

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 780-784页

作者： Wang, Chang-Jen Wen, Chao-Kai Tsai, Shang-Ho Jin, Shi Natl Chiao Tung Univ Inst Elect & Control Engn Hsinchu 300 Taiwan Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 804 Taiwan Natl Chiao Tung Univ Dept Elect Engn Hsinchu 300 Taiwan Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery (GEC-SR) shows better accuracy, speed, and robustness than numerous existing methods. decentralized GEC-SR (deGEC-SR) addresses the scalability issue in high-resolution images. However, the convergence speed and stability of these algorithms heavily rely on the settings of several handcrafted tuning factors with inefficient turning process. In this work, we propose deGEC-SR-Net by unfolding the iterative deGEC-SR algorithm into a learning network architecture with trainable parameters. The parameters of deGEC-SR-Net are determined by data-driven training. Numerical results show that deGEC-SR-Net provides substantially faster convergence than deGEC-SR and exhibits superior robustness to noise and prior mis-specifications.

关键词： Phase retrieval deep neural network unfolding decentralized algorithm

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Green technology innovation path based on blockchain algorithm

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2021年 31卷 100587-100587页

作者： Li, Donghan Northeastern Univ Sch Business Adm Shenyang 110819 Liaoning Peoples R China

The core of green technological innovation is to protect the ecology. The innovative introduction of ecological concepts into the existing ecosystem can ensure the sustainable development and progress of society. This paper mainly studies the path analysis of green technology innovation based on blockchain algorithm. Aiming at the high latency and low efficiency of pbft algorithm in green technology innovation, a low latency consensus algorithm ipbft based on an alternative voting mechanism is proposed. Explore the influencing factors of domestic realization of green technology innovation, clarify the reasons that hinder green technology innovation and give reasonable strategies to help domestic green technology innovation work to be carried out faster and better. This article compares the ipbft algorithm with the improved consensus algorithms cpbft, vpbft and pbft in recent years. Experimental results show that the consensus delay of the ipbft algorithm is much lower than that of the other three algorithms, and the average delay of the ipbft single consensus is only about 370ms. In different transaction volume and different node number throughput performance tests, the ipbft throughput in these two tests is better than the other three algorithms. In the view switching phase, the ipbft algorithm also takes less time than the other three algorithms. The above results show that the ipbft algorithm has lower consensus delay and higher throughput, which can reduce network communication overhead and save system transmission energy consumption. It can ensure the security and authenticity of information in the green technology innovation based on blockchain, and reduce system overhead.

关键词： Blockchain technology decentralized algorithm Green technology innovation Consensus algorithm

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Resource-Aware Control via Dynamic Pricing for Congestion Game with Finite-Time Guarantees 21

Resource-Aware Control via Dynamic Pricing for Congestion Ga...

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21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (IEEE SPAWC)

作者： Tampubolon, Ezra Ceribasic, Haris Boche, Holger Tech Univ Munich Lehrstuhl Theoret Informationstech Munich Germany Munich Ctr Quantum Sci & Technol MCQST Munich Germany

ISBN: (纸本)9781728154787

Congestion game is a widely used model for modern networked applications. A central issue in such applications is that the selfish behavior of the participants may result in resource overloading and negative externalities for the system participants. In this work, we propose a pricing mechanism that guarantees the sub-linear increase of the time-cumulative violation of the resource load constraints. The feature of our method is that it is resource-centric in the sense that it depends on the congestion state of the resources and not on specific characteristics of the system participants. This feature makes our mechanism scalable, flexible, and privacy-preserving. Moreover, we show by numerical simulations that our pricing mechanism has no significant effect on the agents' welfare in contrast to the improvement of the capacity violation.

关键词： Congestion Game Resource Allocation decentralized algorithm Mirror Descent Pricing algorithm Network Routing

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decentralized EXPECTED CONSISTENT SIGNAL RECOVERY FOR QUANTIZATION MEASUREMENTS

DECENTRALIZED EXPECTED CONSISTENT SIGNAL RECOVERY FOR QUANTI...

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

作者： Wang, Chang-Jen Wei, Chao-Kai Tsai, Shang-Ho Jin, Shi Natl Chiao Tung Univ Inst Elect & Control Engn Hsinchu Taiwan Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung Taiwan Natl Chiao Tung Univ Dept Elect Engn Hsinchu Taiwan Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China

ISBN: (纸本)9781509066315

Signal recovery through coarse quantization of a linear transform output has many applications in engineering, such as channel estimation and signal detection in massive MIMO systems. A recently proposed scheme, known as generalized expectation consistent signal recovery (GEC-SR), can achieve Bayesian inference and exhibit better robustness than many existing methods. However, recovering signals with large transform matrices continue to present a computational burden for GEC-SR. In this study, we develop a novel decentralized architecture by leveraging the core framework of GEC-SR called "deGEC-SR." deGEC-SR offers excellent performance as GEC-SR and runs tens of times faster than GEC-SR. We derive the theoretical state evolution of deGEC-SR and demonstrate its accuracy using numerical results.

关键词： Quantization measurement expectation consistent Bayes-optimal inference decentralized algorithm

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LINEAR SPEEDUP IN SADDLE-POINT ESCAPE FOR decentralized NON-CONVEX OPTIMIZATION

LINEAR SPEEDUP IN SADDLE-POINT ESCAPE FOR DECENTRALIZED NON-...

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

作者： Vlaski, Stefan Sayed, Ali H. Ecole Polytech Fed Lausanne Sch Engn Lausanne Switzerland

ISBN: (纸本)9781509066315

Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to noncooperative approaches in the strongly-convex setting. More recently, these results have been extended to the pursuit of first-order stationary points in non-convex environments. In this work, we examine in detail the dependence of second-order convergence guarantees on the spectral properties of the combination policy for non-convex multi agent optimization. We establish linear speedup in saddle-point escape time in the number of agents for symmetric combination policies and study the potential for further improvement by employing asymmetric combination weights. The results imply that a linear speedup can be expected in the pursuit of second-order stationary points, which exclude local maxima as well as strict saddle-points and correspond to local or even global minima in many important learning settings.

关键词： Non-convex optimization saddle-point second-order stationarity minima decentralized algorithm centralized algorithm diffusion strategy

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