Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646483
M. Mahdavi, O. Edfors, V. Öwall, Liang Liu
Signal processing complexity and required memory become problematic in massive MIMO systems as the dimension of channel state information (CSI) matrix grows significantly with the large number of antennas and users. To address these challenges, we propose the first angular-domain massive MIMO detection scheme, which is based on three concepts: transferring the baseband processing from the spatial domain to the angular domain; exploiting the sparsity of received beams to reduce the dimension of CSI matrix; and performing the whole detection and precoding in the angular domain using the reduced CSI matrix. We have measured the massive MIMO channel at 2.6 GHz with a 128-antenna linear array communicating with 16 users to evaluate our scheme. Complexity analysis and simulations show that proposed idea leads to 40% – 70% reduction in the processing complexity and memory without significant performance loss, which significantly outperforms the antenna-domain schemes.
{"title":"A Low Complexity Massive MIMO Detection Scheme Using Angular-Domain Processing","authors":"M. Mahdavi, O. Edfors, V. Öwall, Liang Liu","doi":"10.1109/GlobalSIP.2018.8646483","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646483","url":null,"abstract":"Signal processing complexity and required memory become problematic in massive MIMO systems as the dimension of channel state information (CSI) matrix grows significantly with the large number of antennas and users. To address these challenges, we propose the first angular-domain massive MIMO detection scheme, which is based on three concepts: transferring the baseband processing from the spatial domain to the angular domain; exploiting the sparsity of received beams to reduce the dimension of CSI matrix; and performing the whole detection and precoding in the angular domain using the reduced CSI matrix. We have measured the massive MIMO channel at 2.6 GHz with a 128-antenna linear array communicating with 16 users to evaluate our scheme. Complexity analysis and simulations show that proposed idea leads to 40% – 70% reduction in the processing complexity and memory without significant performance loss, which significantly outperforms the antenna-domain schemes.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123344970","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646376
Fuqian Yang, Zhaowei Zhu, Shangshu Zhao, Yang Yang, Xiliang Luo
Fog computing has been considered to be a potential solution to enable computation-intensive and latency-critical application at the battery-empowered mobile devices. Task offloading could take advantages of available neighboring computational resources to achieve low latency. By exploiting the temporal correlation of the states at fog nodes, a sequential decision-making problem that aims to optimize the long-term task offloading performance is considered in this paper. Such a problem is a partially observed Markov decision process with an exploitation-exploration tradeoff that is difficult to analyze. We address this tradeoff in task offloading under the framework of restless multi-armed bandits (RMAB). The indexability analysis of this task offloading problem is then provided. Meanwhile, an index policy, which is asymptotically optimal and has remarkably low computation complexity, is established based on the Whittle’s index to solve the task offloading problem. Numerical results show the superiority of the proposed task offloading method.
{"title":"OPTIMAL TASK OFFLOADING IN FOG-ENABLED NETWORKS VIA INDEX POLICIES","authors":"Fuqian Yang, Zhaowei Zhu, Shangshu Zhao, Yang Yang, Xiliang Luo","doi":"10.1109/GlobalSIP.2018.8646376","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646376","url":null,"abstract":"Fog computing has been considered to be a potential solution to enable computation-intensive and latency-critical application at the battery-empowered mobile devices. Task offloading could take advantages of available neighboring computational resources to achieve low latency. By exploiting the temporal correlation of the states at fog nodes, a sequential decision-making problem that aims to optimize the long-term task offloading performance is considered in this paper. Such a problem is a partially observed Markov decision process with an exploitation-exploration tradeoff that is difficult to analyze. We address this tradeoff in task offloading under the framework of restless multi-armed bandits (RMAB). The indexability analysis of this task offloading problem is then provided. Meanwhile, an index policy, which is asymptotically optimal and has remarkably low computation complexity, is established based on the Whittle’s index to solve the task offloading problem. Numerical results show the superiority of the proposed task offloading method.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114703318","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646520
Sharif A. Matin, L. Milstein
An important indicator commonly used to evaluate the quality of a communication link is Signal-to-Interference ratio (SIR). However, there are relatively few papers which deal with SIR of OFDM systems in the literature. This paper analytically investigates OFDM system performance by including major channel impediments such as excess delay spread over cyclic prefix, frequency offset, and time varying fading. We derive a closed-form expression for SIR accounting for details of the interferences. Variations of ICI and ISI with the number of subcarriers, Doppler spread are also investigated. A condition relating peak ISI and peak ICI is derived. We also analyze the leakage of power (unwanted power distributions across subcarriers) for desired subcarrier demodulation.
{"title":"Impacts of Channel Impairments and Imperfections on OFDM System Performances","authors":"Sharif A. Matin, L. Milstein","doi":"10.1109/GlobalSIP.2018.8646520","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646520","url":null,"abstract":"An important indicator commonly used to evaluate the quality of a communication link is Signal-to-Interference ratio (SIR). However, there are relatively few papers which deal with SIR of OFDM systems in the literature. This paper analytically investigates OFDM system performance by including major channel impediments such as excess delay spread over cyclic prefix, frequency offset, and time varying fading. We derive a closed-form expression for SIR accounting for details of the interferences. Variations of ICI and ISI with the number of subcarriers, Doppler spread are also investigated. A condition relating peak ISI and peak ICI is derived. We also analyze the leakage of power (unwanted power distributions across subcarriers) for desired subcarrier demodulation.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114745073","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646587
Arvind Prasadan, R. Nadakuditi
We analyze the Dynamic Mode Decomposition (DMD) algorithm as applied to multivariate time-series data. Our analysis reveals the critical role played by the lag-one cross-correlation, or cross-covariance, terms. We show that when the rows of the multivariate time series matrix can be modeled as linear combinations of lag-one uncorrelated latent time series that have a non-zero lag-one autocorrelation, then in the large sample limit, DMD perfectly recovers, up to a column-wise scaling, the mixing matrix, and thus the latent time series. We validate our findings with numerical simulations, and demonstrate how DMD can be used to unmix mixed audio signals.
{"title":"THE FINITE SAMPLE PERFORMANCE OF DYNAMIC MODE DECOMPOSITION","authors":"Arvind Prasadan, R. Nadakuditi","doi":"10.1109/GlobalSIP.2018.8646587","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646587","url":null,"abstract":"We analyze the Dynamic Mode Decomposition (DMD) algorithm as applied to multivariate time-series data. Our analysis reveals the critical role played by the lag-one cross-correlation, or cross-covariance, terms. We show that when the rows of the multivariate time series matrix can be modeled as linear combinations of lag-one uncorrelated latent time series that have a non-zero lag-one autocorrelation, then in the large sample limit, DMD perfectly recovers, up to a column-wise scaling, the mixing matrix, and thus the latent time series. We validate our findings with numerical simulations, and demonstrate how DMD can be used to unmix mixed audio signals.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124268090","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 : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646498
A. Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, M. Bell, T. Tran
Conventional image discrimination tasks are performed on fully observed images. In challenging real imaging scenarios, where sensing systems are energy demanding or need to operate with limited bandwidth and exposure-time budgets, or defective pixels, where the data collected often suffers from missing information, and this makes the task extremely hard. In this paper, we leverage Convolutional Neural Networks (CNNs) to extract information from partially observed images. While pre-trained CNNs fail significantly even with such a small percentage of the input missing, our proposed framework demonstrates the ability to overcome it after training on fully-observed and partially-observed images at a few observation ratios. We demonstrate that our method is indeed reconstruction-free, retraining-free and generalizable to previously untrained-on observation ratios and it remains effective in two different visual tasks – image classification and object detection. Our framework performs well even for test images with only 10% of pixels available and outperforms the reconstruct-then-classify pipeline in these challenging scenarios for small observation fractions.
{"title":"RECONSTRUCTION-FREE DEEP CONVOLUTIONAL NEURAL NETWORKS FOR PARTIALLY OBSERVED IMAGES","authors":"A. Nair, Luoluo Liu, Akshay Rangamani, Peter Chin, M. Bell, T. Tran","doi":"10.1109/GLOBALSIP.2018.8646498","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646498","url":null,"abstract":"Conventional image discrimination tasks are performed on fully observed images. In challenging real imaging scenarios, where sensing systems are energy demanding or need to operate with limited bandwidth and exposure-time budgets, or defective pixels, where the data collected often suffers from missing information, and this makes the task extremely hard. In this paper, we leverage Convolutional Neural Networks (CNNs) to extract information from partially observed images. While pre-trained CNNs fail significantly even with such a small percentage of the input missing, our proposed framework demonstrates the ability to overcome it after training on fully-observed and partially-observed images at a few observation ratios. We demonstrate that our method is indeed reconstruction-free, retraining-free and generalizable to previously untrained-on observation ratios and it remains effective in two different visual tasks – image classification and object detection. Our framework performs well even for test images with only 10% of pixels available and outperforms the reconstruct-then-classify pipeline in these challenging scenarios for small observation fractions.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128121083","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646667
I. A. Arriaga-Trejo, Arindam Bose, A. Orozco-Lugo, Mojtaba Soltanalian
The design of sequences with good correlation properties has been of long-standing interest in signal processing owing to their applications in diverse fields of technology. The computational construction of such sequences typically relies on the minimization of Integrated Sidelobe Level (ISL)-related metrics that effectively lower the correlation magnitude for out-of-phase lags. In this paper, we address the design of sets of unimodular sequences with good cross-correlation and complementary cross-correlation properties over a zero-correlation-zone. Numerical results show the potential of the generated sequences in identifying both strictly linear (SL) and widely linear (WL) systems.
{"title":"DESIGN OF UNIMODULAR SEQUENCE SETS WITH GOOD CORRELATION AND COMPLEMENTARY CORRELATION PROPERTIES","authors":"I. A. Arriaga-Trejo, Arindam Bose, A. Orozco-Lugo, Mojtaba Soltanalian","doi":"10.1109/GlobalSIP.2018.8646667","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646667","url":null,"abstract":"The design of sequences with good correlation properties has been of long-standing interest in signal processing owing to their applications in diverse fields of technology. The computational construction of such sequences typically relies on the minimization of Integrated Sidelobe Level (ISL)-related metrics that effectively lower the correlation magnitude for out-of-phase lags. In this paper, we address the design of sets of unimodular sequences with good cross-correlation and complementary cross-correlation properties over a zero-correlation-zone. Numerical results show the potential of the generated sequences in identifying both strictly linear (SL) and widely linear (WL) systems.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125603617","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646605
Sungwoo Park, Anum Ali, N. G. Prelcic, R. Heath
Spatial channel covariance information can replace instantaneous full channel state information for designing hybrid analog/digital precoders. Estimating the spatial channel covariance is challenging due to the inherent limitation of the hybrid architecture, i.e., much fewer radio frequency (RF) chains than antennas. In this paper, we propose a spatial channel covariance estimation method for spatially sparse time-varying frequency-selective channels. The proposed method leverages the fact that the channel can be represented as a low-rank higher-order tensor. Numerical results demonstrate that the proposed approach achieves higher estimation accuracy in comparison with existing covariance estimation methods.
{"title":"SPATIAL CHANNEL COVARIANCE ESTIMATION FOR THE HYBRID ARCHITECTURE AT A BASE STATION: A TENSOR-DECOMPOSITION-BASED APPROACH","authors":"Sungwoo Park, Anum Ali, N. G. Prelcic, R. Heath","doi":"10.1109/GlobalSIP.2018.8646605","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646605","url":null,"abstract":"Spatial channel covariance information can replace instantaneous full channel state information for designing hybrid analog/digital precoders. Estimating the spatial channel covariance is challenging due to the inherent limitation of the hybrid architecture, i.e., much fewer radio frequency (RF) chains than antennas. In this paper, we propose a spatial channel covariance estimation method for spatially sparse time-varying frequency-selective channels. The proposed method leverages the fact that the channel can be represented as a low-rank higher-order tensor. Numerical results demonstrate that the proposed approach achieves higher estimation accuracy in comparison with existing covariance estimation methods.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122285065","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 : 2018-11-01DOI: 10.1109/globalsip.2018.8646555
{"title":"GlobalSIP 2018 Welcome","authors":"","doi":"10.1109/globalsip.2018.8646555","DOIUrl":"https://doi.org/10.1109/globalsip.2018.8646555","url":null,"abstract":"","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132057344","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646478
Sudarshan Adiga, M. Attia, Wei-Ting Chang, R. Tandon
Generative Adversarial Networks (GAN) are used to generate synthetic samples while closely following the underlying distribution of a real data set. While GANs have recently gained significant popularity, they often suffer from the mode collapse problem, where the generated samples lack diversity. Moreover, some approaches that attempt to resolve the model collapse problem do not necessarily yield high quality synthetic samples. In this paper, we propose two novel performance metrics, namely mode-collapse divergence (MCD) which quantifies the extent of mode collapse for a GAN architecture. Second, we propose the metric Generative Quality Score (GQS), which measures the quality of generated samples. We present a comprehensive study of the performance of various GAN architectures proposed in the literature through the lens of MCD and GQS, for three different data sets, namely MNIST, Fashion MNIST and CIFAR-10.
{"title":"ON THE TRADEOFF BETWEEN MODE COLLAPSE AND SAMPLE QUALITY IN GENERATIVE ADVERSARIAL NETWORKS","authors":"Sudarshan Adiga, M. Attia, Wei-Ting Chang, R. Tandon","doi":"10.1109/GlobalSIP.2018.8646478","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646478","url":null,"abstract":"Generative Adversarial Networks (GAN) are used to generate synthetic samples while closely following the underlying distribution of a real data set. While GANs have recently gained significant popularity, they often suffer from the mode collapse problem, where the generated samples lack diversity. Moreover, some approaches that attempt to resolve the model collapse problem do not necessarily yield high quality synthetic samples. In this paper, we propose two novel performance metrics, namely mode-collapse divergence (MCD) which quantifies the extent of mode collapse for a GAN architecture. Second, we propose the metric Generative Quality Score (GQS), which measures the quality of generated samples. We present a comprehensive study of the performance of various GAN architectures proposed in the literature through the lens of MCD and GQS, for three different data sets, namely MNIST, Fashion MNIST and CIFAR-10.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132534273","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646703
Wai-Ming Chan, Wing-Shan Tam, C. Kok, H. So
A hybrid analog-digital precoder equalizer system for MIMO communication is presented in this paper. A delay-line based precoder is proposed to achieve low hardware complexity for large-scale antenna array transmitter deployed in fifth generation mobile devices. Besides, the presented precoder equalizer system is free from intersymbol interference even with very short guard interval when channel state information is known a priori. Simulation results shows that the proposed system can achieve low BER with much lower transmitter hardware complexity.
{"title":"Delay-Line Precoder based Analog-Digital MIMO Systems with MMSE Equalizer","authors":"Wai-Ming Chan, Wing-Shan Tam, C. Kok, H. So","doi":"10.1109/GlobalSIP.2018.8646703","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646703","url":null,"abstract":"A hybrid analog-digital precoder equalizer system for MIMO communication is presented in this paper. A delay-line based precoder is proposed to achieve low hardware complexity for large-scale antenna array transmitter deployed in fifth generation mobile devices. Besides, the presented precoder equalizer system is free from intersymbol interference even with very short guard interval when channel state information is known a priori. Simulation results shows that the proposed system can achieve low BER with much lower transmitter hardware complexity.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130258211","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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