2017 51st Asilomar Conference on Signals, Systems, and Computers最新文献_第10页

Interference alignment based precoder-decoder design for radar-communication co-existence 基于干扰对准的雷达-通信共存预编解码器设计

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335561

Yuanhao Cui, V. Koivunen, Xiaojun Jing

{"title":"Interference alignment based precoder-decoder design for radar-communication co-existence","authors":"Yuanhao Cui, V. Koivunen, Xiaojun Jing","doi":"10.1109/ACSSC.2017.8335561","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335561","url":null,"abstract":"Co-existence of radar and communication systems is necessary to facilitate new wireless systems and services due to shortage of useful radio spectrum. Moreover, changes in spectrum regulation will be introduced where spectrum is allocated in larger chunks and different radio systems need to share the spectrum. For example LTE, WI-FI and 5G systems will have to share spectrum with S-band radars. Managing interference is a key task in spectrum sharing and co-existence. In this paper we propose a joint Precoder-Decoder design that maximizes SINRs for co-existing radar and communication systems. Multicarrier waveforms are assumed for both subsystems. Interference Alignment (IA) constraints are imposed to facilitate easier interference cancellation in finding the Precoder and Decoder. Therefore, a maximum Degree of Freedom (DoF) upper bound for K + 1 radar-communication users interference channel can be achieved. Our simulation studies demonstrate that the interference can be practically fully cancelled in both communication and radar systems. This leads to improved detection performance in radar and higher rate in communication subsystems. A significant performance gain over a subspace-based Precoder design is obtained as well.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121658437","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: 10

Multiscale modeling of dependencies between spikes and fields 尖峰和场之间依赖关系的多尺度建模

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335438

Hamidreza Abbaspourazad, Han-Lin Hsieh, M. Shanechi

{"title":"Multiscale modeling of dependencies between spikes and fields","authors":"Hamidreza Abbaspourazad, Han-Lin Hsieh, M. Shanechi","doi":"10.1109/ACSSC.2017.8335438","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335438","url":null,"abstract":"Measuring and modeling the brain at multiple spatiotemporal scales are important for studying brain function and developing high-performance brain-machine interfaces. To better understand neural encoding of behavior, the conditional dependencies between different scales of brain activity should be considered in the multiscale modeling framework. Here, we develop a new multiscale model that characterizes the conditional dependence between spikes and fields during behavior. We modeled this mutual dependence by incorporating the effect of field features on each neuron's firing rate function. To reduce the number of model parameters to be learned, we assumed that the strength of dependency between a pair of neuron and field feature is a function of the distance between electrodes from which they are recorded. We then constructed the shape of this unknown spatial dependency function with a weighted sum of Gaussian kernels. We devised an unsupervised learning algorithm using expectation-maximization to learn the kernel weights as well as other model parameters. Using simulated data, we show that this learning algorithm accurately identifies the multiscale model parameters and the conditional dependency functions. This modeling and learning framework can help study spike-field encoding and dependencies and could enhance future neurotechnologies.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196000","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

An efficient software implementation of correctly rounded operations extending FMA: A + b + c and a × b + c × d 一个有效的软件实现正确四舍五入运算扩展FMA: A + b + c和A × b + c × d

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335379

C. Lauter

引用次数: 0

Model-based iterative reconstruction for neutron laminography 基于模型的中子层析迭代重建

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335686

S. Venkatakrishnan, E. Cakmak, Hassina Billheux, P. Bingham, Richard Archibald

{"title":"Model-based iterative reconstruction for neutron laminography","authors":"S. Venkatakrishnan, E. Cakmak, Hassina Billheux, P. Bingham, Richard Archibald","doi":"10.1109/ACSSC.2017.8335686","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335686","url":null,"abstract":"Neutron-based parallel-beam laminography is an important 3D characterization tool because it can image thick specimens with unique shapes and provides a complimentary contrast to X-rays for several elements relevant to the material sciences and biology. However, the inversion of neutron laminography data is complicated because of the non-traditional geometry of the set-up, the presence of noise and the occurrence of gamma hits on the detector during the course of an experiment. In this paper, we present a model-based/regularized-inversion reconstruction algorithm for neutron laminography. We introduce a new forward-model/data fitting term and combine it with a flexible regularizer function to formulate the reconstruction as minimizing a cost-function. We then present a novel optimization algorithm that is based on combining a majorization-minimization technique with a first-order method that is amenable to simple parallelization on multi-core architectures. Using simulated and experimental data, we demonstrate that it is possible to acquire high quality reconstructions compared to the typically used filtered-back projection algorithm and algebraic reconstruction techniques.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122258845","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: 7

Physical layer security in massive MIMO systems 大规模MIMO系统中的物理层安全

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335124

R. Schaefer, Gayan Amarasuriya, H. Poor

引用次数: 18

Noncoherent multi-user MIMO communications using covariance CSIT 使用协方差CSIT的非相干多用户MIMO通信

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335551

C. Thomas, Wassim Tabikh, D. Slock, Yuan-Wu Yi

引用次数: 4

Non-parametric learning to infer wireless relays, routes and traffic patterns from time series of spectrum activity 从频谱活动的时间序列推断无线中继、路由和交通模式的非参数学习

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335484

S. Kokalj-Filipovic, P. Spasojevic, A. Poylisher

{"title":"Non-parametric learning to infer wireless relays, routes and traffic patterns from time series of spectrum activity","authors":"S. Kokalj-Filipovic, P. Spasojevic, A. Poylisher","doi":"10.1109/ACSSC.2017.8335484","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335484","url":null,"abstract":"Non-parametric inference techniques are proposed to understand latent structure behind sequences of spectral activity indicators, i.e. packet start and stop times, of networked wireless transmitters. We aim to infer the latent network structure and characterize information flow between spectrally monitored nodes. The practical aspect of learning is to aid the reasoning of a cognitive network about its unknown and dynamic spectrum environment. We first segment the observed on-off time series into temporal segments of statistically discernible behavioral states. Each state segment has distinct emission statistics and a specific duration, learned by using a Bayesian non-parametric method, referred to as HDP-HSMM [1] in our prior work [2]. The end result is that new times series of state segments are derived from the observations of each nodes activity. We propose test statistics, loosely related to Granger-causality between per-node sequences of state segments, to trace the impact of one nodes traffic to another. We define extendable statistical models of causality in which not only state changes are considered as events, but also the nature of those changes, i.e. whether the new state has similar observation statistics in both nodes. Our approach is non-parametric as it does not require knowledge about underlying network protocols.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127262201","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

Feature based order recognition of continuous-phase FSK using principal component analysis 基于主成分分析的连续相位FSK序列特征识别

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335157

Ambaw B. Ambaw, M. Doroslovački

{"title":"Feature based order recognition of continuous-phase FSK using principal component analysis","authors":"Ambaw B. Ambaw, M. Doroslovački","doi":"10.1109/ACSSC.2017.8335157","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335157","url":null,"abstract":"Principal component analysis (PCA) is a technique that performs a linear transformation on the input space to align directions of maximum variation with the directions of the axises. In this paper, we study the feasibility of principal component analysis based order recognition of continuous phase FSK. The approximate entropy (ApEn) of the received signal, ApEn of the phase of the received signal, and ApEn of the instantaneous frequency of the received signal are used as a set of distinguishing features. The work aims in devising an unsupervised learning algorithm under noisy, carrier frequency offset and fast fading channel conditions. The instantaneous frequency is shaped by using root raised cosine pulses. Performance of principal component based method is compared to stacked autoencoder (SAE) based approach which is more computationally complex technique that can model relatively complicated relationships and non-linearities. For fair comparison both the PCA and SAE based methods use approximate entropy features. The benefit of employing PCA is that after PCA transformations the computation cost can really be decreased a lot. Also in both methods, no a priori information is required about carrier phase, symbol rate and carrier amplitude. The PCA based method shows higher accuracy than the SAE method.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126423887","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

On out-of-band emissions of quantized precoding in massive MU-MIMO-OFDM 大规模MU-MIMO-OFDM中量化预编码的带外发射研究

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335128

Sven Jacobsson, G. Durisi, M. Coldrey, Christoph Studer

引用次数: 12

Physics-driven deep training of dictionary-based algorithms for MR image reconstruction 基于字典的磁共振图像重建算法的物理驱动深度训练

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335685

S. Ravishankar, Il Yong Chun, J. Fessler

引用次数: 12