Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9244957
Zehan Chao, Longxiu Huang, D. Needell
In our paper, we have studied the tensor completion problem when the sampling pattern is deterministic. We first propose a simple but efficient weighted HOSVD algorithm for recovery from noisy observations. Then we use the weighted HOSVD result as an initialization for the total variation. We have proved the accuracy of the weighted HOSVD algorithm from theoretical and numerical perspectives. In the numerical simulation parts, we also showed that by using the proposed initialization, the total variation algorithm can efficiently fill the missing data for images and videos.
{"title":"Tensor Completion through Total Variation with Initialization from Weighted HOSVD","authors":"Zehan Chao, Longxiu Huang, D. Needell","doi":"10.1109/ITA50056.2020.9244957","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244957","url":null,"abstract":"In our paper, we have studied the tensor completion problem when the sampling pattern is deterministic. We first propose a simple but efficient weighted HOSVD algorithm for recovery from noisy observations. Then we use the weighted HOSVD result as an initialization for the total variation. We have proved the accuracy of the weighted HOSVD algorithm from theoretical and numerical perspectives. In the numerical simulation parts, we also showed that by using the proposed initialization, the total variation algorithm can efficiently fill the missing data for images and videos.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124857690","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}
Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9244971
J. Gibson
Relative entropy is used to investigate whether a sequence is memoryless or has memory and to discern the presence of any structure in the sequence. Particular emphasis is placed on obtaining expressions for finite sequence length N and autoregressive sequences with known and unknown autocorrelations. We relate our results to the terms entropy gain, information gain, and redundancy as defined in agent learning studies, and show that these terms can be bounded using the mean squared error due to linear prediction of a stationary sequence.
{"title":"Agent Learning and Autoregressive Modeling","authors":"J. Gibson","doi":"10.1109/ITA50056.2020.9244971","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244971","url":null,"abstract":"Relative entropy is used to investigate whether a sequence is memoryless or has memory and to discern the presence of any structure in the sequence. Particular emphasis is placed on obtaining expressions for finite sequence length N and autoregressive sequences with known and unknown autocorrelations. We relate our results to the terms entropy gain, information gain, and redundancy as defined in agent learning studies, and show that these terms can be bounded using the mean squared error due to linear prediction of a stationary sequence.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121667413","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}
Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9244934
Chao Gan, Ruida Zhou, Jing Yang, Cong Shen
In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete-time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the sensing phase, the user is able to sense a subset of channels sequentially before it decides to use one of them in the following transmission phase. We assume the channel states alternate between busy and idle according to independent Bernoulli random processes from frame to frame. To capture the inherent uncertainty in channel sensing, we assume the reward of each transmission when the channel is idle is a random variable. We also associate random costs with sensing and transmission actions. Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i.e., reward-minus-cost).We start with an offline setting where the statistics of the channel status, costs and reward are known beforehand. We show that the the optimal policy exhibits a recursive double-threshold structure, and the user needs to compare the channel statistics with those thresholds sequentially in order to decide its actions. With such insights, we then study the online setting, where the statistical information of the channels, costs and reward are unknown a priori. We judiciously balance exploration and exploitation, and show that the cumulative regret scales in O(log T). We also establish a matched lower bound, which implies that our online algorithm is order-optimal. Simulation results corroborate our theoretical analysis.
{"title":"Cost-Aware Learning and Optimization for Opportunistic Spectrum Access","authors":"Chao Gan, Ruida Zhou, Jing Yang, Cong Shen","doi":"10.1109/ITA50056.2020.9244934","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244934","url":null,"abstract":"In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete-time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the sensing phase, the user is able to sense a subset of channels sequentially before it decides to use one of them in the following transmission phase. We assume the channel states alternate between busy and idle according to independent Bernoulli random processes from frame to frame. To capture the inherent uncertainty in channel sensing, we assume the reward of each transmission when the channel is idle is a random variable. We also associate random costs with sensing and transmission actions. Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i.e., reward-minus-cost).We start with an offline setting where the statistics of the channel status, costs and reward are known beforehand. We show that the the optimal policy exhibits a recursive double-threshold structure, and the user needs to compare the channel statistics with those thresholds sequentially in order to decide its actions. With such insights, we then study the online setting, where the statistical information of the channels, costs and reward are unknown a priori. We judiciously balance exploration and exploitation, and show that the cumulative regret scales in O(log T). We also establish a matched lower bound, which implies that our online algorithm is order-optimal. Simulation results corroborate our theoretical analysis.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121893514","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}
Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9245011
E. Torres, Simon T. Schafer, F. Gage, T. Sejnowski
New methods in genomics allow the tracking of single cell transcriptome across tens of thousands of genes for hundreds of cells dynamically changing over time. These advancements open new computational problems and provide opportunity to explore new solutions to the interrogation of the transcriptome data in humans and in animal models. Common data analysis pipelines include a dimensionality reduction step to facilitate visualizing the data in two or three dimensions, (e.g. using t-distributed stochastic neighbor embedding (t-SNE)). Such methods reveal structure in high-dimensional data, while aiming at accurately representing global structure of the data. A potential pitfall of some methods is gross data loss when constraining the analyses to gene space data that is not asynchronously changing from day to day, or that express more stable variability of some genes relative to other genes.
{"title":"Dynamic Interrogation of Stochastic Transcriptome Trajectories (DIST2)","authors":"E. Torres, Simon T. Schafer, F. Gage, T. Sejnowski","doi":"10.1109/ITA50056.2020.9245011","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9245011","url":null,"abstract":"New methods in genomics allow the tracking of single cell transcriptome across tens of thousands of genes for hundreds of cells dynamically changing over time. These advancements open new computational problems and provide opportunity to explore new solutions to the interrogation of the transcriptome data in humans and in animal models. Common data analysis pipelines include a dimensionality reduction step to facilitate visualizing the data in two or three dimensions, (e.g. using t-distributed stochastic neighbor embedding (t-SNE)). Such methods reveal structure in high-dimensional data, while aiming at accurately representing global structure of the data. A potential pitfall of some methods is gross data loss when constraining the analyses to gene space data that is not asynchronously changing from day to day, or that express more stable variability of some genes relative to other genes.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134554268","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}
Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9244976
Hadi Sarieddeen, Mohamed-Slim Alouini, T. Al-Naffouri
Last piece of RF spectrum (100 GHz—10 THz)
最后一段射频频谱(100ghz - 10thz)
{"title":"Theory for Terahertz Communications","authors":"Hadi Sarieddeen, Mohamed-Slim Alouini, T. Al-Naffouri","doi":"10.1109/ITA50056.2020.9244976","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244976","url":null,"abstract":"Last piece of RF spectrum (100 GHz—10 THz)","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125196966","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}
Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9244973
N. Lee
Pilot-free (or non-coherent) communications are of great significance for short-packet communications in beyond 5G applications. The absence of pilots brings a fundamental challenge when decoding a message because of no knowledge of channel state information at a receiver. In this paper, we aim to show that a massive number of antennas at a base station (BS) is very useful for pilot-free uplink communications. Specifically, for a single-input multiple-output channel, we show that the spectral efficiency linearly scales with channel coherence time, provided that the number of antennas is infinite. We prove this result by both a variant of binary sparse superposition codes and a compressive covariance sensing-based decoding method. We also present a novel covariance matching pursuit (CMP) decoding method that is computationally efficient yet achieving a nearoptimal decoding performance. By simulations, we demonstrate the proposed decoding algorithm significantly outperforms the existing approximated message-passing based algorithm.
{"title":"Massive MIMO is Very Useful for Pilot-Free Uplink Communications","authors":"N. Lee","doi":"10.1109/ITA50056.2020.9244973","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244973","url":null,"abstract":"Pilot-free (or non-coherent) communications are of great significance for short-packet communications in beyond 5G applications. The absence of pilots brings a fundamental challenge when decoding a message because of no knowledge of channel state information at a receiver. In this paper, we aim to show that a massive number of antennas at a base station (BS) is very useful for pilot-free uplink communications. Specifically, for a single-input multiple-output channel, we show that the spectral efficiency linearly scales with channel coherence time, provided that the number of antennas is infinite. We prove this result by both a variant of binary sparse superposition codes and a compressive covariance sensing-based decoding method. We also present a novel covariance matching pursuit (CMP) decoding method that is computationally efficient yet achieving a nearoptimal decoding performance. By simulations, we demonstrate the proposed decoding algorithm significantly outperforms the existing approximated message-passing based algorithm.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114508742","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}
Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9244950
Mattia Lecci, Paolo Testolina, M. Giordani, Michele Polese, T. Ropitault, C. Gentile, Neeraj Varshney, Anuraag Bodi, M. Zorzi
Millimeter-wave (mmWave) communication is one of the cornerstone innovations of fifth-generation (5G) wireless networks, thanks to the massive bandwidth available in these frequency bands. To correctly assess the performance of such systems, however, it is essential to have reliable channel models, based on a deep understanding of the propagation characteristics of the mmWave signal. In this respect, ray tracers can provide high accuracy, at the expense of a significant computational complexity, which limits the scalability of simulations. To address this issue, in this paper we present possible simplifications that can reduce the complexity of ray tracing in the mmWave environment, without significantly affecting the accuracy of the model. We evaluate the effect of such simplifications on linklevel metrics, testing different configuration parameters and propagation scenarios.
{"title":"Simplified Ray Tracing for the Millimeter Wave Channel: A Performance Evaluation","authors":"Mattia Lecci, Paolo Testolina, M. Giordani, Michele Polese, T. Ropitault, C. Gentile, Neeraj Varshney, Anuraag Bodi, M. Zorzi","doi":"10.1109/ITA50056.2020.9244950","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244950","url":null,"abstract":"Millimeter-wave (mmWave) communication is one of the cornerstone innovations of fifth-generation (5G) wireless networks, thanks to the massive bandwidth available in these frequency bands. To correctly assess the performance of such systems, however, it is essential to have reliable channel models, based on a deep understanding of the propagation characteristics of the mmWave signal. In this respect, ray tracers can provide high accuracy, at the expense of a significant computational complexity, which limits the scalability of simulations. To address this issue, in this paper we present possible simplifications that can reduce the complexity of ray tracing in the mmWave environment, without significantly affecting the accuracy of the model. We evaluate the effect of such simplifications on linklevel metrics, testing different configuration parameters and propagation scenarios.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114509730","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}
Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9244969
Shakiba Yaghoubi, Georgios Fainekos
In this paper, a reinforcement learning approach for designing feedback neural network controllers for nonlinear systems is proposed. Given a Signal Temporal Logic (STL) specification which needs to be satisfied by the system over a set of initial conditions, the neural network parameters are tuned in order to maximize the satisfaction of the STL formula. The framework is based on a max-min formulation of the robustness of the STL formula. The maximization is solved through a Lagrange multipliers method, while the minimization corresponds to a falsification problem. We present our results on a vehicle and a quadrotor model and demonstrate that our approach reduces the training time more than 50 percent compared to the baseline approach.
{"title":"Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers: A Lagrange Multipliers Approach","authors":"Shakiba Yaghoubi, Georgios Fainekos","doi":"10.1109/ITA50056.2020.9244969","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244969","url":null,"abstract":"In this paper, a reinforcement learning approach for designing feedback neural network controllers for nonlinear systems is proposed. Given a Signal Temporal Logic (STL) specification which needs to be satisfied by the system over a set of initial conditions, the neural network parameters are tuned in order to maximize the satisfaction of the STL formula. The framework is based on a max-min formulation of the robustness of the STL formula. The maximization is solved through a Lagrange multipliers method, while the minimization corresponds to a falsification problem. We present our results on a vehicle and a quadrotor model and demonstrate that our approach reduces the training time more than 50 percent compared to the baseline approach.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131954296","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}
Pub Date : 2020-02-02DOI: 10.1109/ITA50056.2020.9244931
A. Steiner, S. Shamai
This work considers a layered coding approach for efficient transmission of data over a wireless block fading channel, connected to a limited capacity reliable link, known as the bottleneck channel. Two main approaches are considered, the first is an oblivious approach, where the sampled noisy observations are compressed and transmitted over the bottleneck channel without having any knowledge of the original information codebook. This is compared to a decode-forward (non-oblivious) approach where the sampled noisy data is decoded, and whatever is successfully decoded is reliably transmitted over the bottleneck channel. The work is extended for an uncertain bottleneck channel capacity setting, where transmitter is not aware of the available backhaul capacity per transmission, but rather its capacity distribution. In both settings it is possible to analytically describe in closed form expressions, the optimal continuous layering power distribution which maximizes the average achievable rate.
{"title":"Broadcast Approach under Information Bottleneck Capacity Uncertainty","authors":"A. Steiner, S. Shamai","doi":"10.1109/ITA50056.2020.9244931","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244931","url":null,"abstract":"This work considers a layered coding approach for efficient transmission of data over a wireless block fading channel, connected to a limited capacity reliable link, known as the bottleneck channel. Two main approaches are considered, the first is an oblivious approach, where the sampled noisy observations are compressed and transmitted over the bottleneck channel without having any knowledge of the original information codebook. This is compared to a decode-forward (non-oblivious) approach where the sampled noisy data is decoded, and whatever is successfully decoded is reliably transmitted over the bottleneck channel. The work is extended for an uncertain bottleneck channel capacity setting, where transmitter is not aware of the available backhaul capacity per transmission, but rather its capacity distribution. In both settings it is possible to analytically describe in closed form expressions, the optimal continuous layering power distribution which maximizes the average achievable rate.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131437","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}
Pub Date : 2020-01-21DOI: 10.1109/ita50056.2020.9244954
Songyang Zhang, Shuguang Cui, Zhi Ding
Goal: Segment the gray-scale point clouds, i.e., s = [ X 1 X 2 X 3 ] ∈ ℝ N×3 based on hypergraph spectral clustering.
目标:基于超图光谱聚类分割灰度点云，即s = [X 1 X 2 X 3]∈λ N×3。
{"title":"Point Cloud Segmentation based on Hypergraph Spectral Clustering","authors":"Songyang Zhang, Shuguang Cui, Zhi Ding","doi":"10.1109/ita50056.2020.9244954","DOIUrl":"https://doi.org/10.1109/ita50056.2020.9244954","url":null,"abstract":"Goal: Segment the gray-scale point clouds, i.e., s = [ X 1 X 2 X 3 ] ∈ ℝ N×3 based on hypergraph spectral clustering.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130293148","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}
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