{"title":"Counting Lattice Points in the Sphere using Deep Neural Networks","authors":"Aymen Askri, G. R. Othman, H. Ghauch","doi":"10.1109/IEEECONF44664.2019.9048858","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048858","url":null,"abstract":"This paper presents a deep learning model for regression to predict the number of lattice points inside the n-dimensional hypersphere. The number of points depends primarily on the lattice generator matrix and the sphere radius, which are used as inputs for the proposed deep neural network (DNN). To see the accuracy of the DNN model, we use some known lattices. Obtained results are compared to mathematical existing bounds in the literature. Our numerical results reveal that our model gives an accurate prediction, of around 80% percent, on the number of lattice points in the sphere.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"48 1","pages":"2053-2057"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84065399","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":"Randomized Asynchronous Recursions with a Sinusoidal Input","authors":"Oguzhan Teke, P. Vaidyanathan","doi":"10.1109/IEEECONF44664.2019.9048998","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048998","url":null,"abstract":"This study considers a randomized asynchronous form of the discrete time-invariant state-space models, in which only a random subset of the state variables is updated in each iteration. When the system has a single input in the form of a complex exponential, it is shown that the output signal still behaves like an exponential in a statistical sense. The study presents the necessary and sufficient condition that ensures the stability of a randomized asynchronous system, which does not necessarily require the stability of the state transition matrix.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"99 1","pages":"1491-1495"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80283679","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}
Siyi Yang, M. Sarihan, Kai-Chi Chang, C. Wong, L. Dolecek
{"title":"Efficient Information Reconciliation for Energy-Time Entanglement Quantum Key Distribution","authors":"Siyi Yang, M. Sarihan, Kai-Chi Chang, C. Wong, L. Dolecek","doi":"10.1109/IEEECONF44664.2019.9048898","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048898","url":null,"abstract":"Graph based codes such as low density parity check (LDPC) codes have been shown promising for the information reconciliation phase in quantum key distribution (QKD). However, existing graph coding schemes have not fully utilized the properties of the QKD channel. In this work, we first investigate the channel statistics for discrete variable (DV) QKD based on energy-time entangled photons. We then establish a so-called balanced modulation scheme that is promising for this channel. Based on the modulation, we propose a joint local-global graph coding scheme that is expected to achieve good error-correction performance.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"73 1","pages":"1364-1368"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80783184","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}
Srikanth Bommaraveni, T. Vu, Satyanarayana Vuppala, S. Chatzinotas, B. Ottersten
{"title":"Active Content Popularity Learning via Query-by-Committee for Edge Caching","authors":"Srikanth Bommaraveni, T. Vu, Satyanarayana Vuppala, S. Chatzinotas, B. Ottersten","doi":"10.1109/IEEECONF44664.2019.9048947","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048947","url":null,"abstract":"Edge caching has received much attention as an effective solution to face the stringent latency requirements in 5G networks due to the proliferation of handset devices as well as data-hungry applications. One of the challenges in edge caching systems is to optimally cache strategic contents to maximize the percentage of total requests served by the edge caches. To enable the optimal caching strategy, we propose an Active Learning approach (AL) to learn and design an accurate content request prediction algorithm. Specifically, we use an AL based Query-by-committee (QBC) matrix completion algorithm with a strategy of querying the most informative missing entries of the content popularity matrix. The proposed AL framework leverage’s the trade-off between exploration and exploitation of the network, and learn the user’s preferences by posing queries or recommendations. Later, it exploits the known information to maximize the system performance. The effectiveness of proposed AL based QBC content learning algorithm is demonstrated via numerical results.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"14 1","pages":"301-305"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80842246","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":"Centralized and Distributed Power Allocation for Max-Min Fairness in Cell-Free Massive MIMO","authors":"S. Chakraborty, Emil Björnson, L. Sanguinetti","doi":"10.1109/IEEECONF44664.2019.9048903","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048903","url":null,"abstract":"Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve users by coherent joint transmission. Downlink power allocation is important in these systems, to determine which APs should transmit to which users and with what power. If the system is implemented correctly, it can deliver a more uniform user performance than conventional cellular networks. To this end, previous works have shown how to perform system-wide max-min fairness power allocation when using maximum ratio precoding. In this paper, we first generalize this method to arbitrary precoding, and then train a neural network to perform approximately the same power allocation but with reduced computational complexity. Finally, we train one neural network per AP to mimic system-wide max-min fairness power allocation, but using only local information. By learning the structure of the local propagation environment, this method outperforms the state-of-the-art distributed power allocation method from the Cell-free Massive MIMO literature.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"28 1","pages":"576-580"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83338847","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":"Low Complexity Uplink Grant-Free NOMA Based on Boosted Approximate Message Passing","authors":"Takanori Hara, K. Ishibashi","doi":"10.1109/IEEECONF44664.2019.9048976","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048976","url":null,"abstract":"We propose a new framework of uplink grant-free non-orthogonal multiple access (NOMA) composed of user activity detection, channel estimation, and data detection. Since the original problem becomes the multiple-measurement vector recovery (MMVR) problem and requires high complexity to solve, we employ an approach based on reduce-MMV-and-boost (ReMBo) together with approximate message passing (AMP) to reduce the complexity significantly. Computer simulations confirm that the proposed framework exhibits the symbol error rate (SER) close to the conventional one while even lower complexity.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"17 1","pages":"1877-1880"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83021103","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":"A Brazilian Portuguese Real-Time Voice Recognition to deal with sensitive data","authors":"F. Pinna, João Carlos Néto, W. Ruggiero","doi":"10.1109/IEEECONF44664.2019.9048689","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048689","url":null,"abstract":"Speech recognition is generally performed by a few cloud providers centrally. Such an approach is not suitable for financial and medical institutions because sensitive data cannot be provided openly to third parties. This work proposes a simple chatbot for Brazilian Portuguese real-time speech recognition, well-defined purpose, specific vocabulary, and a secure way without exposing sensitive data for use on mobile devices. The proposed system got a word error rate of 2.38% for a speech recognition task.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"24 1","pages":"1872-1876"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83225457","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}
Brian J. Redman, Daniel Calzada, Jamie Wingo, T. Quach, Meghan A. Galiardi, Amber L. Dagel, C. LaCasse, G. Birch
{"title":"Optimizing a Compressive Imager for Machine Learning Tasks","authors":"Brian J. Redman, Daniel Calzada, Jamie Wingo, T. Quach, Meghan A. Galiardi, Amber L. Dagel, C. LaCasse, G. Birch","doi":"10.1109/IEEECONF44664.2019.9048763","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048763","url":null,"abstract":"Images are often not the optimal data form to perform machine learning tasks such as scene classification. Compressive classification can reduce the size, weight, and power of a system by selecting the minimum information while maximizing classification accuracy.In this work we present designs and simulations of prism arrays which realize sensing matrices using a monolithic element. The sensing matrix is optimized using a neural network architecture to maximize classification accuracy of the MNIST dataset while considering the blurring caused by the size of each prism. Simulated optical hardware performance for a range of prism sizes are reported.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"26 1","pages":"1000-1004"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90959491","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}
Tianyi Zhang, Jiaying Ren, Christopher Gianelli, Jian Li
{"title":"RFI Mitigation for One-Bit UWB Radar Systems","authors":"Tianyi Zhang, Jiaying Ren, Christopher Gianelli, Jian Li","doi":"10.1109/IEEECONF44664.2019.9048982","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048982","url":null,"abstract":"Radio frequency interference (RFI) mitigation is critical to the proper operation of ultra-wideband (UWB) radar systems. This paper considers RFI mitigation for a one-bit UWB radar system, with its measurements obtained via a low-cost and high-rate sampling strategy using a known threshold varying with slow-time. We first establish a data model for the RFI sources. Then we present a relaxation based algorithm to estimate the parameters of the RFI sources from the signed measurements and thresholds. Next, a sparse method is introduced to recover the desired UWB radar echoes using the estimated RFI parameters. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed method.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"45 1","pages":"1545-1549"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89430681","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":"Reduced Dimension Beamspace Design Incorporating Nested Array For Mmwave Channel Estimation","authors":"Rohan R. Pote, B. Rao","doi":"10.1109/IEEECONF44664.2019.9048702","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048702","url":null,"abstract":"Millimeter wave systems often have very few number of RF chains as compared to the number of antenna elements in order to reduce power consumption and complexity. In this work, we study the design of the mapping function QN×r from the antenna element space of size N to the reduced beamspace dimension r. In the first part of this paper, we restrict the mapping function to antenna selection matrix that chooses antenna elements in a large ULA to form a nested array. For $r leq 2sqrt N - 1$, we can identify such nested arrays using appropriate selection matrices. For $r > 2sqrt N - 1$, we select elements associated with each additional RF chain to maximally improve CRLB for a limited scattering environment, modeled using a single angle of arrival. The above design of the mapping function lacks beamforming (BF) gain, which is crucial for the channel estimation (CE) phase in mmWave communications. In the second part of the paper, we design a mapping function QN × r that provides BF gain while forming an approximate nested array, when focused in an appropriately narrow angular space.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"449 1","pages":"1212-1216"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86575327","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}