Pub Date : 2017-02-01DOI: 10.1109/ITA.2017.8023450
Murat A. Erdogdu
We consider the problem of convex constrained minimization of an average of n functions, where the parameter and the features are related through inner products. We focus on second order batch updates, where the curvature matrix is obtained by assuming random design and by applying the celebrated Stein's lemma together with subsampling techniques. The proposed algorithm enjoys fast convergence rates similar to the Newton method, yet the per-iteration cost has the same order of magnitude as the gradient descent. We demonstrate its performance on well-known optimization problems where Stein's lemma is not directly applicable, such as M-estimation for robust statistics, and inequality form linear/quadratic programming etc. Under certain assumptions, we show that the constrained optimization algorithm attains a composite convergence rate that is initially quadratic and asymptotically linear. We validate its performance through widely encountered optimization tasks on several real and synthetic datasets by comparing it to classical optimization algorithms.
{"title":"Generalized Hessian approximations via Stein's lemma for constrained minimization","authors":"Murat A. Erdogdu","doi":"10.1109/ITA.2017.8023450","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023450","url":null,"abstract":"We consider the problem of convex constrained minimization of an average of n functions, where the parameter and the features are related through inner products. We focus on second order batch updates, where the curvature matrix is obtained by assuming random design and by applying the celebrated Stein's lemma together with subsampling techniques. The proposed algorithm enjoys fast convergence rates similar to the Newton method, yet the per-iteration cost has the same order of magnitude as the gradient descent. We demonstrate its performance on well-known optimization problems where Stein's lemma is not directly applicable, such as M-estimation for robust statistics, and inequality form linear/quadratic programming etc. Under certain assumptions, we show that the constrained optimization algorithm attains a composite convergence rate that is initially quadratic and asymptotically linear. We validate its performance through widely encountered optimization tasks on several real and synthetic datasets by comparing it to classical optimization algorithms.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134557706","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023463
Arun Padakandla
A new coding technique, based on fixed block-length codes, is proposed for the problems of communicating a pair of correlated sources over (i) a 2-user multiple access channel (MAC) and (ii) a 2-user interference channel. Its performance is analyzed to derive a new set of sufficient conditions for both problems. The derived conditions are proven to be strictly less binding than the current known best. Our findings are inspired by Dueck's example [1].
{"title":"Communicating correlated sources over multi-user channels","authors":"Arun Padakandla","doi":"10.1109/ITA.2017.8023463","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023463","url":null,"abstract":"A new coding technique, based on fixed block-length codes, is proposed for the problems of communicating a pair of correlated sources over (i) a 2-user multiple access channel (MAC) and (ii) a 2-user interference channel. Its performance is analyzed to derive a new set of sufficient conditions for both problems. The derived conditions are proven to be strictly less binding than the current known best. Our findings are inspired by Dueck's example [1].","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127366492","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023458
Hsien-Ping Lin, Shu Lin, K. Abdel-Ghaffar
This paper presents a design of LDPC codes for a joint source-channel (JSC) coding system. In the construction of such an LDPC code, the source compression matrix and the channel code parity-check matrix are designed jointly. The integrated matrix is used for both JSC encoding and decoding. Experimental results show that the codes constructed not only perform well in the waterfall region but also achieve low error-floors.
{"title":"Integrated code design for a joint source and channel LDPC coding scheme","authors":"Hsien-Ping Lin, Shu Lin, K. Abdel-Ghaffar","doi":"10.1109/ITA.2017.8023458","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023458","url":null,"abstract":"This paper presents a design of LDPC codes for a joint source-channel (JSC) coding system. In the construction of such an LDPC code, the source compression matrix and the channel code parity-check matrix are designed jointly. The integrated matrix is used for both JSC encoding and decoding. Experimental results show that the codes constructed not only perform well in the waterfall region but also achieve low error-floors.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128813382","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023459
G. Alirezaei, R. Mathar
In this paper, we consider one-bit output quantization of an amplitude bounded input-signal subject to arbitrary additive noise. Capacity is then represented in various ways, each demonstrating that finding the optimum quantization threshold q is an extremely difficult problem. For a class of noise distributions, of which a typical representative is the uniform distribution, it is shown that the optimum quantizer is asymmetric. This contradicts intuition, which for symmetric noise expects the optimum threshold to be the average of the input distribution.
{"title":"Optimal one-bit quantizers are asymmetric for additive uniform noise","authors":"G. Alirezaei, R. Mathar","doi":"10.1109/ITA.2017.8023459","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023459","url":null,"abstract":"In this paper, we consider one-bit output quantization of an amplitude bounded input-signal subject to arbitrary additive noise. Capacity is then represented in various ways, each demonstrating that finding the optimum quantization threshold q is an extremely difficult problem. For a class of noise distributions, of which a typical representative is the uniform distribution, it is shown that the optimum quantizer is asymmetric. This contradicts intuition, which for symmetric noise expects the optimum threshold to be the average of the input distribution.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126342652","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023472
Satya Kumar V, V. Sharma
We consider optimal/efficient power allocation policies in a single/multihop wireless network in the presence of hard end-to-end deadline delay constraints on the transmitted packets. Such constraints can be useful for real time voice and video. Power is consumed in only transmission of the data. We consider the case when the power used in transmission is a convex function of the data transmitted. We develop a computationally efficient online algorithm, which minimizes the average power for the single hop. We model this problem as dynamic program (DP) and obtain the optimal solution. Next, we generalize it to the multiuser, multihop scenario when there are multiple real time streams with different hard deadline constraints.
{"title":"Joint routing, scheduling and power control providing hard deadline in wireless multihop networks","authors":"Satya Kumar V, V. Sharma","doi":"10.1109/ITA.2017.8023472","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023472","url":null,"abstract":"We consider optimal/efficient power allocation policies in a single/multihop wireless network in the presence of hard end-to-end deadline delay constraints on the transmitted packets. Such constraints can be useful for real time voice and video. Power is consumed in only transmission of the data. We consider the case when the power used in transmission is a convex function of the data transmitted. We develop a computationally efficient online algorithm, which minimizes the average power for the single hop. We model this problem as dynamic program (DP) and obtain the optimal solution. Next, we generalize it to the multiuser, multihop scenario when there are multiple real time streams with different hard deadline constraints.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"50 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123395671","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023466
M. Reyes, D. Neuhoff
This paper proposes Local Conditioning as a truly distributed exact version of Belief Propagation for cyclic undirected graphical models. It is shown how to derive explicit recursive updates for messages and beliefs that are truly distributed in the sense that messages are passed between individual nodes of the graph rather than between clustered nodes. Such a distributed algorithm is especially relevant for problems that require a distributed implementation, for example sensor networks. In order to compare its complexity and ease of implementation with a clustered version of Belief Propagation, we illustrate both in a Min-Sum block interpolation problem within the context of an Ising model.
{"title":"Local Conditioning on undirected graphs","authors":"M. Reyes, D. Neuhoff","doi":"10.1109/ITA.2017.8023466","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023466","url":null,"abstract":"This paper proposes Local Conditioning as a truly distributed exact version of Belief Propagation for cyclic undirected graphical models. It is shown how to derive explicit recursive updates for messages and beliefs that are truly distributed in the sense that messages are passed between individual nodes of the graph rather than between clustered nodes. Such a distributed algorithm is especially relevant for problems that require a distributed implementation, for example sensor networks. In order to compare its complexity and ease of implementation with a clustered version of Belief Propagation, we illustrate both in a Min-Sum block interpolation problem within the context of an Ising model.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115552674","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023474
Nikos Kargas, N. Sidiropoulos
There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries. Related approaches have found considerable success in the area of recommender systems, under machine learning. From a statistical estimation point of view, the gold standard is to have access to the joint probability distribution of all pertinent random variables, from which any desired optimal estimator can be readily derived. In practice high-dimensional joint distributions are very hard to estimate, and only estimates of low-dimensional projections may be available. We show that it is possible to identify higher-order joint PMFs from lower-order marginalized PMFs using coupled low-rank tensor factorization. Our approach features guaranteed identifiability when the full joint PMF is of low-enough rank, and effective approximation otherwise. We provide an algorithmic approach to compute the sought factors, and illustrate the merits of our approach using rating prediction as an example.
{"title":"Completing a joint PMF from projections: A low-rank coupled tensor factorization approach","authors":"Nikos Kargas, N. Sidiropoulos","doi":"10.1109/ITA.2017.8023474","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023474","url":null,"abstract":"There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries. Related approaches have found considerable success in the area of recommender systems, under machine learning. From a statistical estimation point of view, the gold standard is to have access to the joint probability distribution of all pertinent random variables, from which any desired optimal estimator can be readily derived. In practice high-dimensional joint distributions are very hard to estimate, and only estimates of low-dimensional projections may be available. We show that it is possible to identify higher-order joint PMFs from lower-order marginalized PMFs using coupled low-rank tensor factorization. Our approach features guaranteed identifiability when the full joint PMF is of low-enough rank, and effective approximation otherwise. We provide an algorithmic approach to compute the sought factors, and illustrate the merits of our approach using rating prediction as an example.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128368756","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023457
Igor Burago, M. Levorato, Sameer Singh
A novel semantic approach to data selection and compression is presented for the dynamic adaptation of IoT data processing and transmission within “wireless islands”, where a set of sensing devices (sensors) are interconnected through one-hop wireless links to a computational resource via a local access point. The core of the proposed technique is a cooperative framework where local classifiers at the mobile nodes are dynamically crafted and updated based on the current state of the observed system, the global processing objective and the characteristics of the sensors and data streams. The edge processor plays a key role by establishing a link between content and operations within the distributed system. The local classifiers are designed to filter the data streams and provide only the needed information to the global classifier at the edge processor, thus minimizing bandwidth usage. However, the better the accuracy of these local classifiers, the larger the energy necessary to run them at the individual sensors. A formulation of the optimization problem for the dynamic construction of the classifiers under bandwidth and energy constraints is proposed and demonstrated on a synthetic example.
{"title":"Semantic compression for edge-assisted systems","authors":"Igor Burago, M. Levorato, Sameer Singh","doi":"10.1109/ITA.2017.8023457","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023457","url":null,"abstract":"A novel semantic approach to data selection and compression is presented for the dynamic adaptation of IoT data processing and transmission within “wireless islands”, where a set of sensing devices (sensors) are interconnected through one-hop wireless links to a computational resource via a local access point. The core of the proposed technique is a cooperative framework where local classifiers at the mobile nodes are dynamically crafted and updated based on the current state of the observed system, the global processing objective and the characteristics of the sensors and data streams. The edge processor plays a key role by establishing a link between content and operations within the distributed system. The local classifiers are designed to filter the data streams and provide only the needed information to the global classifier at the edge processor, thus minimizing bandwidth usage. However, the better the accuracy of these local classifiers, the larger the energy necessary to run them at the individual sensors. A formulation of the optimization problem for the dynamic construction of the classifiers under bandwidth and energy constraints is proposed and demonstrated on a synthetic example.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125914135","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023452
Yang Liu, Kemal Davaslioglu, R. Gitlin
Network life time of Wireless Body Area Network (WBANs) is essential for its operation. An integral part of the network life time is the energy efficiency of the devices and enabling their transmissions with the optimal choice of Physical layer (PHY) and Medium Access Control (MAC) parameters. Particularly, in the next generation wireless networks, where devices and sensors are heterogeneous and coexist in the same geographical area creating possible collisions and interference to each other, the battery power needs to be efficiently used. This paper consists of four complementing parts. In the first two parts, we study the two optimization algorithms proposed in our prior work. The first algorithm, Cross-Layer Optimization for Energy Efficiency (CLOEE), enables us to carry out a cross-layer resource allocation that addresses the rate and reliability trade-off in the PHY as well as the frame size optimization and transmission efficiency for the MAC layer. The second algorithm, Energy Efficiency Optimization of Channel Access Probabilities (EECAP), studies the case where the nodes access the medium in a probabilistic manner and jointly determines the optimal access probability and payload frame size for each node. These two algorithms address the problem from an optimization perspective. In the third part of the paper, we study the same problem from a game theoretical point of view. The last part of the paper is devoted to summarizing the challenges ahead and future research directions to increase the energy efficiency and network life time of WBANs.
无线体域网(wban)的网络寿命对其运行至关重要。网络寿命的一个组成部分是设备的能量效率，并使其传输具有物理层(PHY)和介质访问控制(MAC)参数的最佳选择。特别是，在下一代无线网络中，设备和传感器是异构的，并且共存于同一地理区域，可能会产生相互碰撞和干扰，因此需要有效地利用电池电量。本文由四个互补部分组成。在前两部分中，我们研究了之前工作中提出的两种优化算法。第一种算法，跨层优化能效(CLOEE)，使我们能够进行跨层资源分配，解决物理层的速率和可靠性权衡，以及MAC层的帧大小优化和传输效率。第二种算法是信道访问概率的能效优化算法(Energy Efficiency Optimization of Channel Access Probabilities, EECAP)，该算法研究节点以概率方式访问介质的情况，共同确定每个节点的最优访问概率和负载帧大小。这两种算法从优化的角度解决了这个问题。在论文的第三部分，我们从博弈论的角度研究了同样的问题。最后，总结了提高wban的能效和网络寿命所面临的挑战和未来的研究方向。
{"title":"Energy efficiency and resource allocation of IEEE 802.15.6 IR-UWB WBANs: Current state-of-the-art and future directions","authors":"Yang Liu, Kemal Davaslioglu, R. Gitlin","doi":"10.1109/ITA.2017.8023452","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023452","url":null,"abstract":"Network life time of Wireless Body Area Network (WBANs) is essential for its operation. An integral part of the network life time is the energy efficiency of the devices and enabling their transmissions with the optimal choice of Physical layer (PHY) and Medium Access Control (MAC) parameters. Particularly, in the next generation wireless networks, where devices and sensors are heterogeneous and coexist in the same geographical area creating possible collisions and interference to each other, the battery power needs to be efficiently used. This paper consists of four complementing parts. In the first two parts, we study the two optimization algorithms proposed in our prior work. The first algorithm, Cross-Layer Optimization for Energy Efficiency (CLOEE), enables us to carry out a cross-layer resource allocation that addresses the rate and reliability trade-off in the PHY as well as the frame size optimization and transmission efficiency for the MAC layer. The second algorithm, Energy Efficiency Optimization of Channel Access Probabilities (EECAP), studies the case where the nodes access the medium in a probabilistic manner and jointly determines the optimal access probability and payload frame size for each node. These two algorithms address the problem from an optimization perspective. In the third part of the paper, we study the same problem from a game theoretical point of view. The last part of the paper is devoted to summarizing the challenges ahead and future research directions to increase the energy efficiency and network life time of WBANs.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132459249","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 : 2017-02-01DOI: 10.1109/ITA.2017.8023447
M. H. M. Costa, Chandra Nair, David Ng
The optimality of the Han and Kobayashi achievable region (with Gaussian signaling) remains an open problem for Gaussian interference channels. In this paper we focus on the Gaussian Z-interference channel. We first show that using correlated (over time) Gaussian signals does not improve on the Han and Kobayashi achievable rate region. Secondly we compute the slope of the Han and Kobayashi achievable region with Gaussian signaling around the Sato's corner point.
{"title":"On the Gaussian Z-interference channel","authors":"M. H. M. Costa, Chandra Nair, David Ng","doi":"10.1109/ITA.2017.8023447","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023447","url":null,"abstract":"The optimality of the Han and Kobayashi achievable region (with Gaussian signaling) remains an open problem for Gaussian interference channels. In this paper we focus on the Gaussian Z-interference channel. We first show that using correlated (over time) Gaussian signals does not improve on the Han and Kobayashi achievable rate region. Secondly we compute the slope of the Han and Kobayashi achievable region with Gaussian signaling around the Sato's corner point.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123234075","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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