2019 53rd Asilomar Conference on Signals, Systems, and Computers最新文献_第5页

On the Lower Bound of Modularity for Graph Fission 图裂变的模性下界

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048934

J. Roth

引用次数: 0

Coding Performance Modeling for Short-Packet Communications 短分组通信的编码性能建模

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9049004

Wei Yang, Ying Wang, J. Soriaga, T. Ji, K. Mukkavilli

引用次数: 2

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO 基于协方差的大规模MIMO随机接入相变分析

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048886

Zhilin Chen, Foad Sohrabi, Ya-Feng Liu, Wei Yu

{"title":"Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO","authors":"Zhilin Chen, Foad Sohrabi, Ya-Feng Liu, Wei Yu","doi":"10.1109/IEEECONF44664.2019.9048886","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048886","url":null,"abstract":"This paper studies the massive random access problem in which a large number of sporadically active devices wish to communicate to a base-station (BS) equipped with a large number of antennas. The devices are pre-assigned unique pilot sequences for random access. It has been shown previously that the device activity detection problem at the BS can be formulated as a maximum likelihood estimation (MLE) problem, whose solution depends on the sample covariance matrix of the received signal. This paper adopts the MLE formulation, and proposes an approach to analyze the covariance based detection by studying the asymptotic properties of the MLE via its associated Fisher information matrix. This paper proposes a necessary condition on the Fisher information matrix such that the estimation error tends to zero in the massive multiple-input multiple-output (MIMO) regime. A phase transition analysis is carried out based on the necessary condition. This paper also analyzes the distribution of the estimation error for the case with a large but finite number of antennas at the BS. Numerical experiments validate the analysis.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"86 1","pages":"36-40"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72774053","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: 34

A Novel Riemannian Optimization Approach and Algorithm for Solving the Phase Retrieval Problem 一种新的求解相位检索问题的黎曼优化方法和算法

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9049040

Ahmed Douik, Fariborz Salehi, B. Hassibi

引用次数: 0

Data-Driven Neuromorphic DRAM-based CNN and RNN Accelerators 基于数据驱动的神经形态dram的CNN和RNN加速器

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048865

T. Delbrück, Shih-Chii Liu

引用次数: 4

1-Bit Sparse Gridless Super-Resolution Doa Estimation For Coprime Arrays 一种1位稀疏无网格的超分辨率互素阵列Doa估计

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048961

Anupama Govinda Raj, J. McClellan

{"title":"1-Bit Sparse Gridless Super-Resolution Doa Estimation For Coprime Arrays","authors":"Anupama Govinda Raj, J. McClellan","doi":"10.1109/IEEECONF44664.2019.9048961","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048961","url":null,"abstract":"Direction of Arrival (DOA) estimation using 1-bit analog-to-digital converters (ADCs) offers significant cost, power, and hardware complexity reduction for sensor arrays. We propose a 1-bit sparse super-resolution DOA method for coprime arrays to achieve search-free DOA estimation, under the assumption of uncorrelated sources. The approach extends gridless DOA estimation for coprime arrays based on sparse super-resolution (SR) theory to 1-bit measurements. Using the arcsine law, a scaled version of the full precision covariance matrix can be recovered from the 1-bit data. The vectorized covariance matrix becomes the effective measurements from the coprime virtual array, and then the DOA estimation problem is expressed as an infinite-dimensional atomic norm minimization problem in the continuous angle domain. The corresponding dual problem is converted to a finite semidefinite program with linear matrix inequality constraints, that is solvable in polynomial time. Finally, the search-free DOA estimates are obtained using the unit-circle zeros of a nonnegative polynomial formed from the dual polynomial, followed by an ℓ1 norm minimization. The angular resolution and accuracy of the proposed method is compared to state-of-the-art approaches such as 1-bit and full-precision versions of spatially smoothed MUSIC and a discrete offgrid method, as well as the full-precision gridless SR method.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"120 1","pages":"108-112"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82105622","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

Training DNA Perceptrons via Fractional Coding 通过分数编码训练DNA感知器

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048931

Xingyi Liu, K. Parhi

引用次数: 2

A Closer Look at Disentangling in β-VAE 近距离观察β-VAE中的解缠

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9048921

Harshvardhan Digvijay Sikka, Weishun Zhong, J. Yin, C. Pehlevan

引用次数: 12

Time Delay Estimation from Multiband Radio Channel Samples in Nonuniform Noise 非均匀噪声下多波段无线电信道样本的时延估计

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9049037

T. Kazaz, G. Janssen, A. V. D. Veen

{"title":"Time Delay Estimation from Multiband Radio Channel Samples in Nonuniform Noise","authors":"T. Kazaz, G. Janssen, A. V. D. Veen","doi":"10.1109/IEEECONF44664.2019.9049037","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9049037","url":null,"abstract":"The multipath radio channel is considered to have a non-bandlimited channel impulse response. Therefore, it is challenging to achieve high resolution time-delay (TD) estimation of multipath components (MPCs) from bandlimited observations of communication signals. It this paper, we consider the problem of multiband channel sampling and TD estimation of MPCs. We assume that the nonideal multi-branch receiver is used for multiband sampling, where the noise is nonuniform across the receiver branches. The resulting data model of Hankel matrices formed from acquired samples has multiple shift-invariance structures, and we propose an algorithm for TD estimation using weighted subspace fitting. The subspace fitting is formulated as a separable nonlinear least squares (NLS) problem, and it is solved using a variable projection method. The proposed algorithm supports high resolution TD estimation from an arbitrary number of bands, and it allows for nonuniform noise across the bands. Numerical simulations show that the algorithm almost attains the Cramér Rao Lower Bound, and it outperforms previously proposed methods such as multiresolution TOA, MI-MUSIC, and ESPRIT.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"21 1","pages":"1237-1241"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81817011","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: 6

Federated Learning with Autotuned Communication-Efficient Secure Aggregation 具有自调优通信高效安全聚合的联邦学习

2019 53rd Asilomar Conference on Signals, Systems, and Computers Pub Date : 2019-11-01 DOI: 10.1109/IEEECONF44664.2019.9049066

Keith Bonawitz, Fariborz Salehi, Jakub Konecný, H. B. McMahan, M. Gruteser

{"title":"Federated Learning with Autotuned Communication-Efficient Secure Aggregation","authors":"Keith Bonawitz, Fariborz Salehi, Jakub Konecný, H. B. McMahan, M. Gruteser","doi":"10.1109/IEEECONF44664.2019.9049066","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9049066","url":null,"abstract":"Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user’s device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how random rotation can enable users’ model updates to be quantized much more efficiently, reducing the communication cost between users and the server. Meanwhile, secure aggregation enables the server to learn an aggregate of at least a threshold number of device’s model contributions without observing any individual device’s contribution in unaggregated form. In this paper, we highlight some of the challenges of setting the parameters for secure aggregation to achieve communication efficiency, especially in the context of the aggressively quantized inputs enabled by random rotation. We then develop a recipe for auto-tuning communication-efficient secure aggregation, based on specific properties of random rotation and secure aggregation – namely, the predictable distribution of vector entries post-rotation and the modular wrapping inherent in secure aggregation. We present both theoretical results and initial experiments.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"1 1","pages":"1222-1226"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84031503","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: 65