{"title":"Imaging with Distributed Lensless Line Sensors","authors":"Yucheng Zheng, M. Salman Asif","doi":"10.1109/IEEECONF44664.2019.9048751","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048751","url":null,"abstract":"Recently, mask-based camera has been proposed by replacing the lens with a coded-mask that modulates light as it reaches the sensor. Such mask-based cameras can reliably capture both direction and depth information about the scene. However, current designs of lensless camera contain only one sensor array. In contrast to that, using multiple distributed sensor arrays to image the scene may offer us many advantages, such as more flexible placing strategy and different views of the scene. In this paper, we present the distributed mask-based line cameras, in which we replace a rectangular mask-based sensor with multiple distributed thin line sensors. To retrieve the information from the measurements, we propose an algorithm that jointly estimate the depth and light intensity of the scene voxels. Simulation results are also presented for different placement strategies and widths of the sensor arrays.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"2 1","pages":"1289-1293"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82132765","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}
{"title":"On the Role of Sampling in Underdetermined Tensor Decomposition with Kronecker and Khatri-Rao Structured Factors","authors":"Mehmet Can Hücümenoğlu, P. Pal","doi":"10.1109/IEEECONF44664.2019.9048911","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048911","url":null,"abstract":"This paper introduces the problem of learning Khatri- Rao structured dictionaries for tensor data, which is inspired from the CANDECOMP/PARAFAC decomposition of tensors. Unlike Kronecker-structured dictionaries which have recently been shown to be locally identifiable under a separable sparsity assumption on coefficient vectors, we show that Khatri-Rao dic tionaries are globally identifiable for arbitrary sparsity patterns. We provide the expected sample complexity to learn Khatri-Rao structured dictionaries and conduct numerical experiments which agree with the theoretical results.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"53 1","pages":"442-446"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86866472","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}
{"title":"Empirical and Simulated Performance Evaluation of Distributed Massive MIMO","authors":"David Löschenbrand, M. Hofer, B. Rainer, T. Zemen","doi":"10.1109/IEEECONF44664.2019.9049062","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9049062","url":null,"abstract":"We describe a distributed massive MIMO measurement campaign in an urban vehicular setting, where wireless channel measurements are acquired with two mobile stations equipped with a single antenna each and 32 antennas at the base station partitioned in two groups of 16 antennas each. We fit and assess various statistical distributions to characterize the observed propagation conditions and their dependency on time and space. Key parameters from the measurement are compared to simulations with a ray-tracing tool considering the geometry of the scenario. We evaluate distributed and collocated base station antenna setups in terms of singular value spread and achievable spectral efficiency for both measured and simulated channels and draw conclusions for optimal base station antenna placement.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"33 1","pages":"952-956"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76041126","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}
{"title":"Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning","authors":"Shixiang Chen, Zengde Deng, Shiqian Ma, A. M. So","doi":"10.1109/IEEECONF44664.2019.9048840","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048840","url":null,"abstract":"Dual principal component pursuit and orthogonal dictionary learning are two fundamental tools in data analysis, and both of them can be formulated as a manifold optimization problem with nonsmooth objective. Algorithms with convergence guarantees for solving this kind of problems have been very limited in the literature. In this paper, we propose a novel manifold proximal point algorithm for solving this nonsmooth manifold optimization problem. Numerical results are reported to demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"204 1","pages":"259-263"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76173260","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}
Samet Oymak, Zalan Fabian, Mingchen Li, M. Soltanolkotabi
{"title":"Generalization, Adaptation and Low-Rank Representation in Neural Networks","authors":"Samet Oymak, Zalan Fabian, Mingchen Li, M. Soltanolkotabi","doi":"10.1109/IEEECONF44664.2019.9048845","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048845","url":null,"abstract":"We develop a data-dependent optimization and generalization theory for neural networks which leverages the lowrankness of the Jacobian matrix associated with the network. Our results help demystify why training and generalization is easier on clean and structured datasets and harder on noisy and unstructured datasets. Specifically, we show that over the principal eigendirections of the Jacobian matrix space learning is fast and one can quickly train a model with zero training loss that can also generalize well. Over the smaller eigendirections, training is slower and early stopping can help with generalization at the expense of some bias. We also discuss how neural networks can learn better representations over time in terms of the Jacobian mapping. We conduct various numerical experiments on deep networks that corroborate our theoretical findings and demonstrate that: (i) the Jacobian of typical neural networks exhibit low-rank structure with a few large singular values and many small ones, (ii) most of the useful label information lies on the principal eigendirections where learning is fast, and (iii) Jacobian adapts over time and learn better representations.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"9 1","pages":"581-585"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80109109","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}
{"title":"Control and data channel combining in Ultra-Reliable Low-Latency Communication","authors":"Trung-Kien Le, U. Salim, F. Kaltenberger","doi":"10.1109/IEEECONF44664.2019.9048795","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048795","url":null,"abstract":"5G will be supporting new services that have remarkably higher requirements than LTE 4G and Ultra-reliable and low-latency communication (URLLC) is one of those emerged categories. Although various techniques have been proposed to improve the data reliability, there has been a gap in how to improve the reliability of control/scheduling information pointing to the scheduled data. In this paper, we propose an intelligent combining of retransmissions of physical downlink control channel (PDCCH) and the physical downlink data channel (PDSCH). In the proposed scheme, the downlink control information (DCI) on PDCCH already indicates the location of a potential retransmission of the corresponding PDSCH. Moreover, the retransmitted DCI can be combined with the first transmission so that resource consumption and latency are reduced compared to the conventional scheme. Theoretical calculations and simulation results show a decrease of resource consumption.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"32 1","pages":"1982-1986"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77663374","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}
{"title":"Decentralized Massive MIMO Uplink Signal Estimation by Binary Multistep Synthesis","authors":"Pascal Seidel, S. Paul, Jochen Rust","doi":"10.1109/IEEECONF44664.2019.9048772","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048772","url":null,"abstract":"While linear equalization schemes like zero forcing or minimum mean-square error achieve a near optimal uplink signal estimation performance in large-scale multi-user multiple-input multiple-output systems, the corresponding algorithms lean on centralized processing. To avoid disproportionate interconnect data rates due to the centralized signal estimation, performing a decentralized equalization can mitigate these effects. In this paper, we present a decentralized signal estimation architecture, which combines the ideas of existing decentralized architectures to (i) reduce the overall latency of the signal estimation and (ii) maintain a high data detection performance.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"10 1","pages":"1967-1971"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88231875","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}
{"title":"Lp Quasi-norm Minimization","authors":"M. Ashour, C. Lagoa, N. S. Aybat","doi":"10.1109/IEEECONF44664.2019.9048923","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048923","url":null,"abstract":"The ℓp (0 < p < 1) quasi-norm is used as a sparsity-inducing function, and has applications in diverse areas, e.g., statistics, machine learning, and signal processing. This paper proposes a heuristic based on a two-block ADMM algorithm for tackling ℓp quasi-norm minimization problems. For p = s/q < 1, s, q ∈ ℤ +, the proposed algorithm requires solving for the roots of a scalar degree 2q polynomial as opposed to applying a soft thresholding operator in the case of ℓ1. We show numerical results for two example applications, sparse signal reconstruction from few noisy measurements and spam email classification using support vector machines. Our method obtains significantly sparser solutions than those obtained by ℓ1 minimization while achieving similar level of measurement fitting in signal reconstruction, and training and test set accuracy in classification.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"604 1","pages":"726-730"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77439685","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}