Pub Date : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919816
A. Joseph, S. Bhatnagar
In this paper, we propose multi-timescale, sequential algorithms for deterministic optimization which can find high-quality solutions. The algorithms fundamentally track the well-known derivative-free model-based search methods in an efficient and resourceful manner with additional heuristics to accelerate the scheme. Our approaches exhibit competitive performance on a selected few global optimization benchmark problems.
{"title":"Stochastic Approximation Trackers for Model-Based Search","authors":"A. Joseph, S. Bhatnagar","doi":"10.1109/ALLERTON.2019.8919816","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919816","url":null,"abstract":"In this paper, we propose multi-timescale, sequential algorithms for deterministic optimization which can find high-quality solutions. The algorithms fundamentally track the well-known derivative-free model-based search methods in an efficient and resourceful manner with additional heuristics to accelerate the scheme. Our approaches exhibit competitive performance on a selected few global optimization benchmark problems.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125586419","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 : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919764
N. Jain, Adrish Banerjee
Visible light communication (VLC) uses run length limited (RLL) code for avoiding flicker and supporting various dimming ranges. In this paper, we propose a low complexity split phase code as RLL code in serial concatenation with a convolutional code as a forward error correction (FEC) code for VLC. We use the extrinsic information transfer (EXIT) chart to explain the iterative decoding behavior of the proposed serial concatenated scheme. We also use puncturing and compensation symbols to support various dimming range in VLC. Thereafter, we derive an expression for upper bound to the average probability of bit error for the proposed VLC system, under maximum likelihood decoding. Furthermore, we propose a method of code mixing in inner RLL code to improve the bit error rate performance in the low signal to noise ratio regime. EXIT chart is used to analyze the effect of RLL code mixing on the convergence threshold of iterative concatenated coding scheme.
{"title":"On Performance Analysis and Code Design for Visible Light Communication","authors":"N. Jain, Adrish Banerjee","doi":"10.1109/ALLERTON.2019.8919764","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919764","url":null,"abstract":"Visible light communication (VLC) uses run length limited (RLL) code for avoiding flicker and supporting various dimming ranges. In this paper, we propose a low complexity split phase code as RLL code in serial concatenation with a convolutional code as a forward error correction (FEC) code for VLC. We use the extrinsic information transfer (EXIT) chart to explain the iterative decoding behavior of the proposed serial concatenated scheme. We also use puncturing and compensation symbols to support various dimming range in VLC. Thereafter, we derive an expression for upper bound to the average probability of bit error for the proposed VLC system, under maximum likelihood decoding. Furthermore, we propose a method of code mixing in inner RLL code to improve the bit error rate performance in the low signal to noise ratio regime. EXIT chart is used to analyze the effect of RLL code mixing on the convergence threshold of iterative concatenated coding scheme.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122742402","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 : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919777
Yimeng Wang, Yongbo Li, Tian Lan, V. Aggarwal
A Data Processing Network (DPN) streams massive volumes of data collected and stored by the network to multiple processing units to compute desired results in a timely fashion. Due to ever-increasing traffic, distributed cache nodes can be deployed to store hot data and rapidly deliver them for consumption. However, prior work on caching policies has primarily focused on the potential gains in network performance, e.g., cache hit ratio and download latency, while neglecting the impact of cache on data processing and consumption.In this paper, we propose a novel framework, DeepChunk, which leverages deep Q-learning for chunk-based caching in DPN. We show that cache policies must be optimized for both network performance during data delivery and processing efficiency during data consumption. Specifically, DeepChunk utilizes a model-free approach by jointly learning limited network, data streaming, and processing statistics at runtime and making cache update decisions under the guidance of powerful deep Q-learning. It enables a joint optimization of multiple objectives including chunk hit ratio, processing stall time, and object download time while being self-adaptive under the time-varying workload and network conditions. We build a prototype implementation of DeepChunk with Ceph, a popular distributed object storage system. Our extensive experiments and evaluation demonstrate significant improvement, i.e., 43% in total reward and 39% in processing stall time, over a number of baseline caching policies.
{"title":"Deep Q-Learning for Chunk-based Caching in Data Processing Networks","authors":"Yimeng Wang, Yongbo Li, Tian Lan, V. Aggarwal","doi":"10.1109/ALLERTON.2019.8919777","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919777","url":null,"abstract":"A Data Processing Network (DPN) streams massive volumes of data collected and stored by the network to multiple processing units to compute desired results in a timely fashion. Due to ever-increasing traffic, distributed cache nodes can be deployed to store hot data and rapidly deliver them for consumption. However, prior work on caching policies has primarily focused on the potential gains in network performance, e.g., cache hit ratio and download latency, while neglecting the impact of cache on data processing and consumption.In this paper, we propose a novel framework, DeepChunk, which leverages deep Q-learning for chunk-based caching in DPN. We show that cache policies must be optimized for both network performance during data delivery and processing efficiency during data consumption. Specifically, DeepChunk utilizes a model-free approach by jointly learning limited network, data streaming, and processing statistics at runtime and making cache update decisions under the guidance of powerful deep Q-learning. It enables a joint optimization of multiple objectives including chunk hit ratio, processing stall time, and object download time while being self-adaptive under the time-varying workload and network conditions. We build a prototype implementation of DeepChunk with Ceph, a popular distributed object storage system. Our extensive experiments and evaluation demonstrate significant improvement, i.e., 43% in total reward and 39% in processing stall time, over a number of baseline caching policies.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123009474","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}
This paper focuses on application of subspace identification methods to predict the thermal dynamics of bio-implants, e.g. UEA. Recursive subspace identification method implemented in this paper predicts the temperature readings of heat sensors in an online fashion within a finite time window and updates the system parameters iteratively to improve the performance of the algorithm. Algorithm validation is realized using COMSOL software simulations as well as using an in vitro experimental system. Both simulation and experimental results indicate that the proposed method can accurately predict the thermal dynamics of the system. The experimental results show online prediction of the thermal effect with a mean squared error of $1. 569 times 10^{-2}$ °C for randomly generated Gaussian inputs and $3. 46 times 10^{-3}$ °C for square wave inputs after adaptive filters converge.
{"title":"Recursive Subspace Identification for Online Thermal Management of Implantable Devices","authors":"Ayca Ermis, Yen-Pang Lai, Xinhai Pan, Ruizhi Chai, Ying Zhang","doi":"10.1109/ALLERTON.2019.8919656","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919656","url":null,"abstract":"This paper focuses on application of subspace identification methods to predict the thermal dynamics of bio-implants, e.g. UEA. Recursive subspace identification method implemented in this paper predicts the temperature readings of heat sensors in an online fashion within a finite time window and updates the system parameters iteratively to improve the performance of the algorithm. Algorithm validation is realized using COMSOL software simulations as well as using an in vitro experimental system. Both simulation and experimental results indicate that the proposed method can accurately predict the thermal dynamics of the system. The experimental results show online prediction of the thermal effect with a mean squared error of $1. 569 times 10^{-2}$ °C for randomly generated Gaussian inputs and $3. 46 times 10^{-3}$ °C for square wave inputs after adaptive filters converge.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129824817","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 : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919824
Namjoon Suh, Ruizhi Zhang, Y. Mei
Ising model is a general framework for capturing the dependency structure among random variables. It has many interesting real-world applications in the fields of medical imaging, genetics, disease surveillance, etc. Nonetheless, literature on the online change-point detection of the interaction parameter in the model is rather limited. This might be attributed to following two challenges: 1) the exact evaluation of the likelihood function with the given data is computationally infeasible due to the presence of partition function and 2) the post-change parameter usually is unknown. In this paper, we overcome these two challenges via our proposed adaptive pseudo-CUSUM procedure, which incorporates the notion of pseudo-likelihood function under the CUSUM framework. Asymptotic analysis, numerical simulation, and case study corroborate the statistical efficiency and the practicality of our proposed scheme.
{"title":"Adaptive Online Monitoring of the Ising model","authors":"Namjoon Suh, Ruizhi Zhang, Y. Mei","doi":"10.1109/ALLERTON.2019.8919824","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919824","url":null,"abstract":"Ising model is a general framework for capturing the dependency structure among random variables. It has many interesting real-world applications in the fields of medical imaging, genetics, disease surveillance, etc. Nonetheless, literature on the online change-point detection of the interaction parameter in the model is rather limited. This might be attributed to following two challenges: 1) the exact evaluation of the likelihood function with the given data is computationally infeasible due to the presence of partition function and 2) the post-change parameter usually is unknown. In this paper, we overcome these two challenges via our proposed adaptive pseudo-CUSUM procedure, which incorporates the notion of pseudo-likelihood function under the CUSUM framework. Asymptotic analysis, numerical simulation, and case study corroborate the statistical efficiency and the practicality of our proposed scheme.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129302694","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 : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919965
Hamidreza Tavafoghi, A. Shetty, K. Poolla, P. Varaiya
We investigate the effect of information/navigation platforms in transportation networks. Specifically, we analyze the outcome when these platforms are owned by for-profit strategic companies such as Google and Apple. We consider two business models, one that makes a profit through advertisements and user information collection, and one that generates revenue from its user by charging a subscription fee. We show that social welfare in an environment with a single platform can be higher than the one when multiple platforms compete with one another. This is in contrast to the standard result for classical goods where competition always improves social welfare. Most importantly, we show that in a competitive environment with multiple platforms, each platform finds it optimal to disclose its information perfectly about the current condition of the network for free. Consequently, in a competitive market (almost) all information platforms must have an ad-based business model and reveal perfect information about the transportation network. Our results provide a purely economic justification on why in practice no navigation application discloses partial information to improve the congestion as suggested previously in the literature.
{"title":"Strategic Information Platforms in Transportation Networks","authors":"Hamidreza Tavafoghi, A. Shetty, K. Poolla, P. Varaiya","doi":"10.1109/ALLERTON.2019.8919965","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919965","url":null,"abstract":"We investigate the effect of information/navigation platforms in transportation networks. Specifically, we analyze the outcome when these platforms are owned by for-profit strategic companies such as Google and Apple. We consider two business models, one that makes a profit through advertisements and user information collection, and one that generates revenue from its user by charging a subscription fee. We show that social welfare in an environment with a single platform can be higher than the one when multiple platforms compete with one another. This is in contrast to the standard result for classical goods where competition always improves social welfare. Most importantly, we show that in a competitive environment with multiple platforms, each platform finds it optimal to disclose its information perfectly about the current condition of the network for free. Consequently, in a competitive market (almost) all information platforms must have an ad-based business model and reveal perfect information about the transportation network. Our results provide a purely economic justification on why in practice no navigation application discloses partial information to improve the congestion as suggested previously in the literature.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127613890","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 : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919866
Qing Li, Yang Chen
A novel deep learning framework for lossy compression is proposed. The framework is based on Deep AutoEncoder (DAE) stacked of Restricted Boltzmann Machines (RBMs), which form Deep Belief Networks (DBNs). The proposed DAE compression scheme is one variant of the known fixed-distortion scheme, where the distortion is fixed and the compression rate is left to optimize. The fixed distortion is achieved by the DBN Blahut-Arimoto algorithm to approximate the Nth-order rate distortion approximating posterior. The trained DBNs are then unrolled to create a DAE, which produces an encoder and a reproducer. The unrolled DAE is fine-tuned with back-propagation through the whole autoencoder to minimize reconstruction errors.
{"title":"Learning to Compress Using Deep AutoEncoder","authors":"Qing Li, Yang Chen","doi":"10.1109/ALLERTON.2019.8919866","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919866","url":null,"abstract":"A novel deep learning framework for lossy compression is proposed. The framework is based on Deep AutoEncoder (DAE) stacked of Restricted Boltzmann Machines (RBMs), which form Deep Belief Networks (DBNs). The proposed DAE compression scheme is one variant of the known fixed-distortion scheme, where the distortion is fixed and the compression rate is left to optimize. The fixed distortion is achieved by the DBN Blahut-Arimoto algorithm to approximate the Nth-order rate distortion approximating posterior. The trained DBNs are then unrolled to create a DAE, which produces an encoder and a reproducer. The unrolled DAE is fine-tuned with back-propagation through the whole autoencoder to minimize reconstruction errors.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128044120","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 : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919712
Ho‐Lim Choi, J. Hammer
This note presents a methodology for the design and the implementation of robust sampled-data systems with maximal sampling periods. The methodology applies to nonlinear input-affine systems. It is shown that optimal outcomes can be approximated by bang-bang controllers that are easy to design and implement.
{"title":"Sampled-Data Systems: Maximal Sampling Period","authors":"Ho‐Lim Choi, J. Hammer","doi":"10.1109/ALLERTON.2019.8919712","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919712","url":null,"abstract":"This note presents a methodology for the design and the implementation of robust sampled-data systems with maximal sampling periods. The methodology applies to nonlinear input-affine systems. It is shown that optimal outcomes can be approximated by bang-bang controllers that are easy to design and implement.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132551473","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 : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919700
Catherine Medlock, A. Oppenheim, I. Chuang, Qi Ding
Receiver operating characteristics (ROCs) are a well-established representation of the tradeoff between detection and false alarm probabilities in classical binary hypothesis testing. We use classical ROCs as motivation for two types of operating characteristics for binary hypothesis testing in quantum systems – decision operating characteristics (QDOCs) and measurement operating characteristics (QMOCs). Both are described in the context of a framework we propose that encompasses the typical formulations of binary hypothesis testing in both the classical and quantum scenarios. We interpret Helstrom’s well-known result [1] regarding discrimination between two quantum density operators with minimum probability of error in this framework. We also present a generalization of previous results [2], [3] regarding the correspondence between classical Parseval frames and quantum measurements. The derivation naturally leads to a constructive procedure for generating many different measurements besides Helstrom’s optimal measurement, some standard and others non-standard, that achieve minimum probability of error.
{"title":"Operating Characteristics for Binary Hypothesis Testing in Quantum Systems","authors":"Catherine Medlock, A. Oppenheim, I. Chuang, Qi Ding","doi":"10.1109/ALLERTON.2019.8919700","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919700","url":null,"abstract":"Receiver operating characteristics (ROCs) are a well-established representation of the tradeoff between detection and false alarm probabilities in classical binary hypothesis testing. We use classical ROCs as motivation for two types of operating characteristics for binary hypothesis testing in quantum systems – decision operating characteristics (QDOCs) and measurement operating characteristics (QMOCs). Both are described in the context of a framework we propose that encompasses the typical formulations of binary hypothesis testing in both the classical and quantum scenarios. We interpret Helstrom’s well-known result [1] regarding discrimination between two quantum density operators with minimum probability of error in this framework. We also present a generalization of previous results [2], [3] regarding the correspondence between classical Parseval frames and quantum measurements. The derivation naturally leads to a constructive procedure for generating many different measurements besides Helstrom’s optimal measurement, some standard and others non-standard, that achieve minimum probability of error.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134319420","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 : 2019-09-01DOI: 10.1109/ALLERTON.2019.8919804
Moustafa Ebada, Sebastian Cammerer, Ahmed Elkelesh, S. Brink
In this work, we introduce a deep learning-based polar code construction algorithm. The core idea is to represent the information/frozen bit indices of a polar code as a binary vector which can be interpreted as trainable weights of a neural network (NN). For this, we demonstrate how this binary vector can be relaxed to a soft-valued vector, facilitating the learning process through gradient descent and enabling an efficient code construction. We further show how different polar code design constraints (e.g., code rate) can be taken into account by means of careful binary-to-soft and soft-to-binary conversions, along with rate-adjustment after each learning iteration. Besides its conceptual simplicity, this approach benefits from having the “decoder-in-the-toop”, i.e., the nature of the decoder is inherently taken into consideration while learning (designing) the polar code. We show results for belief propagation (BP) decoding over both AWGN and Rayleigh fading channels with considerable performance gains over state-of-the-art construction schemes.
{"title":"Deep Learning-Based Polar Code Design","authors":"Moustafa Ebada, Sebastian Cammerer, Ahmed Elkelesh, S. Brink","doi":"10.1109/ALLERTON.2019.8919804","DOIUrl":"https://doi.org/10.1109/ALLERTON.2019.8919804","url":null,"abstract":"In this work, we introduce a deep learning-based polar code construction algorithm. The core idea is to represent the information/frozen bit indices of a polar code as a binary vector which can be interpreted as trainable weights of a neural network (NN). For this, we demonstrate how this binary vector can be relaxed to a soft-valued vector, facilitating the learning process through gradient descent and enabling an efficient code construction. We further show how different polar code design constraints (e.g., code rate) can be taken into account by means of careful binary-to-soft and soft-to-binary conversions, along with rate-adjustment after each learning iteration. Besides its conceptual simplicity, this approach benefits from having the “decoder-in-the-toop”, i.e., the nature of the decoder is inherently taken into consideration while learning (designing) the polar code. We show results for belief propagation (BP) decoding over both AWGN and Rayleigh fading channels with considerable performance gains over state-of-the-art construction schemes.","PeriodicalId":120479,"journal":{"name":"2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131727004","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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