2020 54th Asilomar Conference on Signals, Systems, and Computers最新文献_第3页

Massive MIMO goes Sub-GHz: Implementation and Experimental Exploration for LPWANs 大规模MIMO走向Sub-GHz: lpwan的实现与实验探索

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443312

Gilles Callebaut, Sara Gunnarsson, Andrea P. Guevara, F. Tufvesson, S. Pollin, L. Perre, A. Johansson

引用次数: 6

On Parametric Model Mismatch in Nonlinear EKF Approximations 非线性EKF近似中的参数模型失配问题

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443571

H. Khaledian, J. Vilà‐Valls, É. Chaumette, X. Prats

引用次数: 2

Graph-aided Online Learning with Expert Advice 图形辅助在线学习与专家建议

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443490

P. M. Ghari, Yanning Shen

引用次数: 0

K-SVD based Periodicity Dictionary Learning 基于K-SVD的周期性字典学习

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443567

Pranav Kulkarni, P. P. Vaidyanathan

引用次数: 1

The Impact of Terminal Mobility on the Performance of a Panel-Based Large Intelligent Surface 终端移动性对基于面板的大型智能表面性能的影响

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443479

Andreia Pereira, F. Rusek, M. Gomes, R. Dinis

{"title":"The Impact of Terminal Mobility on the Performance of a Panel-Based Large Intelligent Surface","authors":"Andreia Pereira, F. Rusek, M. Gomes, R. Dinis","doi":"10.1109/IEEECONF51394.2020.9443479","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443479","url":null,"abstract":"A Large Intelligent Surface (LIS) is a recently pro-posed concept that relies on the integration of a vast number of antenna-elements over an entire surface able to transmit and/or receive information, especially suitable for high speed indoor communications and massive internet of things (IoT) applications. In this paper, we study the impact of terminal mobility on the performance of a panel-based LIS, considering different resource allocation approaches (including panel selection and/or panel-terminal association) and small panel areas. Such configuration represents a clear advantage in terms of flexibility, together with the fact that, from an economy of scale perspective, the production of this type of configuration choosing smaller areas seems more foreseeable.Our performance results show that there is no need to have a contagious distribution of panels, instead they can be physically separated according to a given distribution. It is also observed that, keeping a regularly fixed or predefined panel scheme, leading only to panel-terminal association, are able to fight against the unpredictability of terminal movements and overcome the computational complexity imposed by the optimum approach (that includes both panel selection and terminal-panel allocation stages), presenting maximum rate losses of 16−30%.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124727813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robustifying FISTA via the infinity norm of its smooth component’s gradient 利用平滑分量梯度的无穷范数对FISTA进行鲁棒化

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443517

Paul Rodríguez

{"title":"Robustifying FISTA via the infinity norm of its smooth component’s gradient","authors":"Paul Rodríguez","doi":"10.1109/IEEECONF51394.2020.9443517","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443517","url":null,"abstract":"The FISTA is a well-known and fast procedure for solving optimization problems composed by the sum of two convex functions, i.e. F = f + g, such that ∇f is L-Lipschitz continuous and g is possibly nonsmooth.FISTA’s well-studied theoretical RoC (rate of convergence) is $mathcal{O}left( {{k^{ - 2}}} right)$; however, in the praxis, it depends on both, the extragradient rule and the step-size (SS) that estimates L. An ill-chosen SS (i.e. a large pre-defined constant), at worst, can force the objective to diverge; furthermore, some adaptive SS methods (i.e. line search, Cauchy, etc.) can slow down or force the objective to present an oscillatory behavior.In this work we present a simple add-on feature to robustify FISTA against an ill-chosen SS when F is the ℓ1 regularized problem. It is based on modifying some entries of ∇fk so as to $left{ {{{left| {nabla {f_k}} right|}_infty }} right}$ is turned into a non-increasing sequence. Furthermore, tracking and limiting $left{ {{{left| {nabla {f_k}} right|}_infty }} right}$ can be used (i) as an early warning method to avoid divergence k } and (ii) to allow larger or even consistently increasing SS sequences.Our computational results particularly target Convolutional Sparse Representations (CSR), where our method indeed boots FISTA’s practical performance.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129362877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strategic Remote Estimation 战略遥测

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443376

Griffin Rule, E. Akyol

{"title":"Strategic Remote Estimation","authors":"Griffin Rule, E. Akyol","doi":"10.1109/IEEECONF51394.2020.9443376","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443376","url":null,"abstract":"Motivated by the emerging networked applications that involve strategic communication between selfish, rational agents whose objectives are misaligned, this paper studies a strategic variation of the well-known remote estimation problem. The classical remote estimation problem studies the design of a system, consisting of a sensor, an encoder, and a decoder, to observe, transmit and estimate a discrete time stochastic process with minimal transmission energy and estimation error. In order to minimize transmission energy, the encoder makes a real-time (sequential) to send or not to send communication decision based on sensor’s observation of the stochastic process. In the variation studied in this paper, the encoder aims to render the estimate at the receiver biased in a particular, statistical manner, hence this consideration transforms the classical remote estimation from a team problem to a game. Specifically, we analyze the problem of sending N samples within a time window of length M. Assuming quadratic distortion measures and affine strategies for sender and receiver, which were shown to be optimal for Gaussian strategic communication, we first determine the optimal sequential transmission policies for this problem in the context of the Stackelberg equilibrium– where the encoder (sensor) is the leader and the decoder (estimator) is the follower– using dynamic programming. We demonstrate the effectiveness of our approach via numerical results.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"26 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127056423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Enabled Adaptive Wireless Power Transmission System for Neuroscience Study 神经科学研究中的机器学习自适应无线电力传输系统

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443501

Hyun-Myung Woo, Woo Seok Kim, Sungcheol Hong, Vivek Jeevakumar, Clay M. Smithhart, Theodore John Price, Byung-Jun Yoon, Sung Il Park

{"title":"Machine Learning Enabled Adaptive Wireless Power Transmission System for Neuroscience Study","authors":"Hyun-Myung Woo, Woo Seok Kim, Sungcheol Hong, Vivek Jeevakumar, Clay M. Smithhart, Theodore John Price, Byung-Jun Yoon, Sung Il Park","doi":"10.1109/IEEECONF51394.2020.9443501","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443501","url":null,"abstract":"We propose a novel machine learning (ML) approach for the adaptive control of wireless power transmission systems for neuroscience studies. Recent advances in wireless technologies have led to new tools and techniques for neuroscience research, particularly in the context of techniques for optogenetics. Such tools eliminate the need for a battery or a tether to an external power supply and enable experiments that can examine complex behaviors such as social interactions in ways. However, current strategies for radio frequency power control, even with optimized transmission antenna designs, fail more often than they succeed in three-dimensional cages or complex environments that demand coverage over large areas. Here, we propose a ML-based algorithm that can effectively address these issues. In our proposed algorithm, we use deep convolutional network networks (CNN) to automatically track the movement and predict the posture of a lab animal, based on which the antenna system is dynamically switched to activate the antenna that maximizes the power efficiency. This dramatically improves the volumetric and angular coverage in the cage as well as the efficiency of the overall power transmission system in in vitro and in vivo experiments, which showcase the potential for their widespread use in various neuroscience studies.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127534068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems 基于强化学习的毫米波和大规模MIMO系统波束模式设计

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443430

Yu Zhang, Muhammad Alrabeiah, A. Alkhateeb

引用次数: 2

The Population Map of Changes in the Spatiotemporal Sensitivity of Visual Neurons Across Saccadic Eye Movements 视神经元时空敏感性随跳跃性眼球运动变化的总体图

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443422

Manish Roy, Amir Akbarian, B. Noudoost, Neda Nategh

引用次数: 0