2020 54th Asilomar Conference on Signals, Systems, and Computers最新文献_第6页

Robust Registration of Multi-modal Medical Images Using Huber’s Criterion 基于Huber准则的多模态医学图像鲁棒配准

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443321

N. Ouzir, E. Ollila, S. Vorobyov

引用次数: 0

Improved Time-Frequency Representation of Multi-Component FM Signals with Compressed Observations 多分量调频信号压缩时频表示的改进

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443566

Vaishali S. Amin, Yimin D. Zhang, B. Himed

引用次数: 4

QR decomposition and Parallel Factor-based Model for Two-Dimensional Direction of Arrival Angle Estimation 二维方向到达角估计的QR分解与并行因子模型

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443370

N. Tayem

引用次数: 0

Power Efficient Multi-Carrier Baseband Processing for 5G and 6G Wireless 5G和6G无线的高效多载波基带处理

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443544

P. Skrimponis, Seyed Hadi Mirfarshbafan, Christoph Studer, S. Rangan

引用次数: 2

Downlink Channel Feedback for FDD Massive MIMO Systems via Tensor Compression and Sampling 基于张量压缩和采样的FDD海量MIMO系统下行信道反馈

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443316

M. S. Ibrahim, Charilaos I. Kanatsoulis, N. Sidiropoulos

{"title":"Downlink Channel Feedback for FDD Massive MIMO Systems via Tensor Compression and Sampling","authors":"M. S. Ibrahim, Charilaos I. Kanatsoulis, N. Sidiropoulos","doi":"10.1109/IEEECONF51394.2020.9443316","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443316","url":null,"abstract":"In frequency division duplex (FDD) massive MIMO systems, each user equipment (UE) device is required to frequently feed back downlink channel state information to its serving base station (BS), resulting in huge uplink overhead. This paper considers the problem of downlink channel feedback in practical wideband massive MIMO systems operating in the FDD mode. The starting point is to leverage the spatial structure of the BS antenna array to reshape the channel matrix in tensor form, and then bring in orthonormal Tucker (also known as multilinear / higher-order SVD) factorization to approximate the channel tensor on the UE side, and feed the obtained factors back to the BS. The resulting decomposition results in a significant reduction in the number of parameters that needs to be fed back from each UE to its serving BS, without sacrificing channel estimation accuracy. Furthermore, this paper exploits a recently-developed sampling/compression mechanism that efficiently handles the considered problem. Using this approach, each UE is only required to feed back a small number of samples/measurements from the channel tensor, thus shifting the computational burden of tensor decomposition/compression to the BS which naturally possesses higher computational capabilities compared to the UE. Experimental results using channels constructed from a practical ray-tracing simulator demonstrate the superiority of the proposed tensor-based approaches relative to the state-of-the-art.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"50 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":"132073148","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

Random Matrix Theory Analysis of the Dominant Mode Rejection Beamformer White Noise Gain with Overestimated Rank 秩估计过高的主导抑制波束增益的随机矩阵理论分析

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443277

Christopher C. Hulbert, K. Wage

引用次数: 1

Full-Duplex Self Cancellation Techniques Using Independent Component Analysis 使用独立分量分析的全双工自抵消技术

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443385

Hsi-Hung Lu, M. Fouda, Chung-An Shen, A. Eltawil

引用次数: 5

Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions 用于解释深度神经网络决策的树状视图和解纠缠表示

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443487

P. Sattigeri, K. Ramamurthy, Jayaraman J. Thiagarajan, B. Kailkhura

引用次数: 0

On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation 数字全双工自干扰消除的实现复杂性研究

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443274

Andreas Toftegaard Kristensen, Alexios Balatsoukas-Stimming, A. Burg

{"title":"On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation","authors":"Andreas Toftegaard Kristensen, Alexios Balatsoukas-Stimming, A. Burg","doi":"10.1109/IEEECONF51394.2020.9443274","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443274","url":null,"abstract":"In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"13 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":"122078712","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: 3

METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics 度量-贝叶斯:基于贝叶斯非参数的高杂波跟踪测量估计

2020 54th Asilomar Conference on Signals, Systems, and Computers Pub Date : 2020-11-01 DOI: 10.1109/IEEECONF51394.2020.9443335

Bahman Moraffah, C. Richmond, Raha Moraffah, A. Papandreou-Suppappola

{"title":"METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics","authors":"Bahman Moraffah, C. Richmond, Raha Moraffah, A. Papandreou-Suppappola","doi":"10.1109/IEEECONF51394.2020.9443335","DOIUrl":"https://doi.org/10.1109/IEEECONF51394.2020.9443335","url":null,"abstract":"Robust tracking of a target in a clutter environment is an important and challenging task. In recent years, the nearest neighbor methods and probabilistic data association filters were proposed. However, the performance of these methods diminishes as number of measurements increases. In this paper, we propose a robust generative approach to effectively model multiple sensor measurements for tracking a moving target in an environment with high clutter. We assume a time-dependent number of measurements that include sensor observations with unknown origin, some of which may only contain clutter with no additional information. We robustly and accurately estimate the trajectory of the moving target in high clutter environment with unknown number of clutters by employing Bayesian nonparametric modeling. In particular, we employ a class of joint Bayesian nonparametric models to construct the joint prior distribution of target and clutter measurements such that the conditional distributions follow a Dirichlet process. The marginalized Dirichlet process prior of the target measurements is then used in a Bayesian tracker to estimate the dynamically-varying target state. We show through experiments that the tracking performance and effectiveness of our proposed framework are increased by suppressing high clutter measurements. In addition, we show that our proposed method outperforms existing methods such as nearest neighbor and probability data association filters.","PeriodicalId":248190,"journal":{"name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","volume":"12 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":"115044944","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