Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313209
M. Mardani, E. Gong, Joseph Y. Cheng, J. Pauly, L. Xing
Recovering images from highly undersampled measurements has a wide range of applications across imaging sciences. State-of-the-art analytics however are not aware of the image perceptual quality, and demand iterative algorithms that incur significant computational overhead. To sidestep these hurdles, this paper brings forth a novel compressive imaging framework using deep neural networks that approximates a low-dimensional manifold of images using generative adversarial networks. To ensure the images are consistent with the measurements a recurrent GAN (RGAN) architecture is deployed that consists of multiple alternative blocks of generator networks and affine projection, which is then followed by a discriminator network to score the perceptual quality of the generated images. A deep residual network with skip connections is used for the generator, while the discriminator is a multilayer perceptron. Experiments performed with real-world contrast enhanced MRI data corroborate the diagnostic quality of the retrieved images relative to state-of-the-art CS schemes. In addition, it achieves about two-orders of magnitude faster reconstruction.
{"title":"Recurrent generative adversarial neural networks for compressive imaging","authors":"M. Mardani, E. Gong, Joseph Y. Cheng, J. Pauly, L. Xing","doi":"10.1109/CAMSAP.2017.8313209","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313209","url":null,"abstract":"Recovering images from highly undersampled measurements has a wide range of applications across imaging sciences. State-of-the-art analytics however are not aware of the image perceptual quality, and demand iterative algorithms that incur significant computational overhead. To sidestep these hurdles, this paper brings forth a novel compressive imaging framework using deep neural networks that approximates a low-dimensional manifold of images using generative adversarial networks. To ensure the images are consistent with the measurements a recurrent GAN (RGAN) architecture is deployed that consists of multiple alternative blocks of generator networks and affine projection, which is then followed by a discriminator network to score the perceptual quality of the generated images. A deep residual network with skip connections is used for the generator, while the discriminator is a multilayer perceptron. Experiments performed with real-world contrast enhanced MRI data corroborate the diagnostic quality of the retrieved images relative to state-of-the-art CS schemes. In addition, it achieves about two-orders of magnitude faster reconstruction.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124939960","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313122
Jorge Bacca, Héctor Vargas, H. Arguello
Recent hyperspectral imaging systems are constructed on the idea of compressive sensing for efficient acquisition. However, the traditional reconstruction model in compressive hyperspectral imaging has a high computational complexity. In this work, compressive hyperspectral imaging and unmixing are combined for hyperspectral reconstruction in a low-complexity scheme. The compressed hyperspectral measurements are acquired with a single pixel spectrometer. The reconstruction model is represented in a space of lower dimension named linear mixture model. Hyperspectral reconstruction is then formulated as a nonnegative matrix factorization problem with respect to the endmembers and abundances, bypassing high-complexity tasks involving the hyperspectral data cube itself. The nonnegative matrix factorization problem is solved by combining an alternating least-squares based estimation strategy with the alternating direction method of multipliers. The estimated performance of the proposed scheme is illustrated in experiments conducted on a simulated acquisition in real data outperforming in 3dB the state-of-the-art reconstruction algorithms.
{"title":"A constrained formulation for compressive spectral image reconstruction using linear mixture models","authors":"Jorge Bacca, Héctor Vargas, H. Arguello","doi":"10.1109/CAMSAP.2017.8313122","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313122","url":null,"abstract":"Recent hyperspectral imaging systems are constructed on the idea of compressive sensing for efficient acquisition. However, the traditional reconstruction model in compressive hyperspectral imaging has a high computational complexity. In this work, compressive hyperspectral imaging and unmixing are combined for hyperspectral reconstruction in a low-complexity scheme. The compressed hyperspectral measurements are acquired with a single pixel spectrometer. The reconstruction model is represented in a space of lower dimension named linear mixture model. Hyperspectral reconstruction is then formulated as a nonnegative matrix factorization problem with respect to the endmembers and abundances, bypassing high-complexity tasks involving the hyperspectral data cube itself. The nonnegative matrix factorization problem is solved by combining an alternating least-squares based estimation strategy with the alternating direction method of multipliers. The estimated performance of the proposed scheme is illustrated in experiments conducted on a simulated acquisition in real data outperforming in 3dB the state-of-the-art reconstruction algorithms.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114283716","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313191
A. Koochakzadeh, P. Pal
Decomposition of tensors into summation of rank one components, known as Canonical Polyadic (CP) decomposition, has long been studied in the literature. Although the CP-rank of tensors can far exceed their dimensions (in which case they are called overcomplete tensors), there are only a handful of algorithms which consider CP-decomposition of such overcomplete tensors, and most of the CP-decomposition algorithms proposed in literature deal with simpler cases where the rank is of the same order as the dimensions of the tensor. In this paper, we consider symmetric tensors of arbitrary even order whose eigenvalues are assumed to be positive. We show that for a 2dth order tensor with dimension N, under some mild conditions, the problem of CP-decomposition is equivalent to solving a system of quadratic equations, even when the rank is as large as O(Nd). We will develop two different algorithms (one convex, and one nonconvex) to solve this system of quadratic equations. Our simulations show that successful recovery of eigenvectors is possible even if the rank is much larger than the dimension of the tensor.1
{"title":"On canonical polyadic decomposition of overcomplete tensors of arbitrary even order","authors":"A. Koochakzadeh, P. Pal","doi":"10.1109/CAMSAP.2017.8313191","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313191","url":null,"abstract":"Decomposition of tensors into summation of rank one components, known as Canonical Polyadic (CP) decomposition, has long been studied in the literature. Although the CP-rank of tensors can far exceed their dimensions (in which case they are called overcomplete tensors), there are only a handful of algorithms which consider CP-decomposition of such overcomplete tensors, and most of the CP-decomposition algorithms proposed in literature deal with simpler cases where the rank is of the same order as the dimensions of the tensor. In this paper, we consider symmetric tensors of arbitrary even order whose eigenvalues are assumed to be positive. We show that for a 2dth order tensor with dimension N, under some mild conditions, the problem of CP-decomposition is equivalent to solving a system of quadratic equations, even when the rank is as large as O(Nd). We will develop two different algorithms (one convex, and one nonconvex) to solve this system of quadratic equations. Our simulations show that successful recovery of eigenvectors is possible even if the rank is much larger than the dimension of the tensor.1","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131496883","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313206
Xu Han, L. Albera, A. Kachenoura, H. Shu, L. Senhadji
In this paper, we propose a new rank-(L, L, 1) Block Term Decomposition (BTD) method. Contrarily to classical techniques, the proposed method estimates also the number of terms and the rank-(L, L, 1) of each term from an overestimated initialization of them. This is achieved by using Group Sparsity of the Loading (GSL) matrices. Numerical experiments with noisy tensors show the good behavior of GSL-BTD and its robustness with respect to the presence of noise in comparison with classical methods. Experiments on epileptic signals confirm its efficiency in practical contexts.
{"title":"Block term decomposition with rank estimation using group sparsity","authors":"Xu Han, L. Albera, A. Kachenoura, H. Shu, L. Senhadji","doi":"10.1109/CAMSAP.2017.8313206","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313206","url":null,"abstract":"In this paper, we propose a new rank-(L, L, 1) Block Term Decomposition (BTD) method. Contrarily to classical techniques, the proposed method estimates also the number of terms and the rank-(L, L, 1) of each term from an overestimated initialization of them. This is achieved by using Group Sparsity of the Loading (GSL) matrices. Numerical experiments with noisy tensors show the good behavior of GSL-BTD and its robustness with respect to the presence of noise in comparison with classical methods. Experiments on epileptic signals confirm its efficiency in practical contexts.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127805112","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313183
M. Kowalski, P. Willett
This paper provides an analysis of several scenarios of target tracking state estimation when additionally estimating the biases of the measuring sensors in the state. Line of Sight (LOS) sensors are used with noisy data and angle biases that are unknown to the estimator. The addition of new state components can potentially be a drawback to the estimator and this is addressed by comparing the accuracy of estimation with 2, 3, and 4 sensors. Of particular interest to us is whether “more” is worth it: More sensors? Is bias estimation even worth doing? The answers are a qualified “yes” and a definite “sometimes.”.
{"title":"Simultaneous target state and sensor bias estimation: Is more better?","authors":"M. Kowalski, P. Willett","doi":"10.1109/CAMSAP.2017.8313183","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313183","url":null,"abstract":"This paper provides an analysis of several scenarios of target tracking state estimation when additionally estimating the biases of the measuring sensors in the state. Line of Sight (LOS) sensors are used with noisy data and angle biases that are unknown to the estimator. The addition of new state components can potentially be a drawback to the estimator and this is addressed by comparing the accuracy of estimation with 2, 3, and 4 sensors. Of particular interest to us is whether “more” is worth it: More sensors? Is bias estimation even worth doing? The answers are a qualified “yes” and a definite “sometimes.”.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128094044","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313185
Han Yan, S. Chaudhari, D. Cabric
Millimeter-wave (mmWave) systems require a large number of antennas at both base station (BS) and user equipment (UE) for a desirable link budget. Due to time varying channel under UE mobility, up-to-date channel state information (CSI) is important to obtain the beamforming gain. The overhead cost of frequent channel estimation becomes a bottleneck to achieve high throughput. In this paper, we propose the first mmWave frequency selective channel tracking technique for hybrid analog and digital beamforming architecture. During tracking, this technique exploits mmWave channel sparsity and uses only one training symbol to update the CSI. Our simulation study utilizes a dynamic channel simulator that builds on top of recently proposed geometric stochastic approach from mmMAGIC project at 28 GHz. Assuming 10m/s moving speed and 200 deg/s rotation speed at UE, the proposed algorithm maintains the 80% of the spectral efficiency as compared to static environment over a time window of 100 ms. The proposed tracking algorithm reduces the overhead by 3 times as compared to existing channel estimation technique.
{"title":"Wideband channel tracking for mmWave MIMO system with hybrid beamforming architecture: (Invited Paper)","authors":"Han Yan, S. Chaudhari, D. Cabric","doi":"10.1109/CAMSAP.2017.8313185","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313185","url":null,"abstract":"Millimeter-wave (mmWave) systems require a large number of antennas at both base station (BS) and user equipment (UE) for a desirable link budget. Due to time varying channel under UE mobility, up-to-date channel state information (CSI) is important to obtain the beamforming gain. The overhead cost of frequent channel estimation becomes a bottleneck to achieve high throughput. In this paper, we propose the first mmWave frequency selective channel tracking technique for hybrid analog and digital beamforming architecture. During tracking, this technique exploits mmWave channel sparsity and uses only one training symbol to update the CSI. Our simulation study utilizes a dynamic channel simulator that builds on top of recently proposed geometric stochastic approach from mmMAGIC project at 28 GHz. Assuming 10m/s moving speed and 200 deg/s rotation speed at UE, the proposed algorithm maintains the 80% of the spectral efficiency as compared to static environment over a time window of 100 ms. The proposed tracking algorithm reduces the overhead by 3 times as compared to existing channel estimation technique.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134630196","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313098
Daniel Valle de Lima, J. Costa, F. Antreich, R. K. Miranda, G. D. Galdo
Safety-critical applications (SCA), such as autonomous driving, and liability critical applications (LCA), such as fisheries management, require a robust positioning system in demanding signal environments with coherent multipath while ensuring reasonably low complexity. In this context, antenna array-based Global Navigation Satellite Systems (GNSS) receivers with array signal processing schemes allow the spatial separation of line-of-sight (LOS) from multipath components. In real-world scenarios array imperfections alter the expected array response, resulting in parameter estimation and filtering errors. In this paper, we propose an approach to time-delay estimation for a tensor-based GNSS receiver that mitigates the effect of multipath components while also being robust against array imperfections. This approach is based on the Canonical Polyadic Decomposition by a Generalized Eigenvalue Decomposition (GPD-GEVD) to recover the signal for each impinging component. Our scheme outperforms both the Higher-Order Singular Value Decomposition (HOSVD) eigenfilter and Direction of Arrival and Khatri-Rao factorization (DoA/KRF) approaches, which are state-of-the-art tensor-based schemes for time-delay estimation, particularly when array imperfections are present.
{"title":"Time-Delay estimation via CPD-GEVD applied to tensor-based GNSS arrays with errors","authors":"Daniel Valle de Lima, J. Costa, F. Antreich, R. K. Miranda, G. D. Galdo","doi":"10.1109/CAMSAP.2017.8313098","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313098","url":null,"abstract":"Safety-critical applications (SCA), such as autonomous driving, and liability critical applications (LCA), such as fisheries management, require a robust positioning system in demanding signal environments with coherent multipath while ensuring reasonably low complexity. In this context, antenna array-based Global Navigation Satellite Systems (GNSS) receivers with array signal processing schemes allow the spatial separation of line-of-sight (LOS) from multipath components. In real-world scenarios array imperfections alter the expected array response, resulting in parameter estimation and filtering errors. In this paper, we propose an approach to time-delay estimation for a tensor-based GNSS receiver that mitigates the effect of multipath components while also being robust against array imperfections. This approach is based on the Canonical Polyadic Decomposition by a Generalized Eigenvalue Decomposition (GPD-GEVD) to recover the signal for each impinging component. Our scheme outperforms both the Higher-Order Singular Value Decomposition (HOSVD) eigenfilter and Direction of Arrival and Khatri-Rao factorization (DoA/KRF) approaches, which are state-of-the-art tensor-based schemes for time-delay estimation, particularly when array imperfections are present.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133693453","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313121
Dimitris G. Chachlakis, Panos P. Markopoulos, F. Ahmad
Standard direction-of-arrival estimation using coprime arrays samples the entries of the estimated physical-array autocorrelation matrix, organizes them in a matrix structure, and conducts multiple-signal classification (MUSIC) with singular vectors of the resulting matrix. A majority of the existing literature samples the physical-array autocorrelations by selection, retaining only one of the samples that correspond to each element of the difference coarray. Other more recent works conduct averaging of all samples that relate to each coarray element. Even though the two methods coincide when applied on the nominal/true physical-array autocorrelations, their performance differs significantly when applied on finite-snapshot estimates. In this paper, we present for the first time in closed form the mean-squared-error of both selection and averaging autocorrelation sampling and clarify/establish the superiority of the latter.
{"title":"The Mean-Squared-Error of autocorrelation sampling in coprime arrays","authors":"Dimitris G. Chachlakis, Panos P. Markopoulos, F. Ahmad","doi":"10.1109/CAMSAP.2017.8313121","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313121","url":null,"abstract":"Standard direction-of-arrival estimation using coprime arrays samples the entries of the estimated physical-array autocorrelation matrix, organizes them in a matrix structure, and conducts multiple-signal classification (MUSIC) with singular vectors of the resulting matrix. A majority of the existing literature samples the physical-array autocorrelations by selection, retaining only one of the samples that correspond to each element of the difference coarray. Other more recent works conduct averaging of all samples that relate to each coarray element. Even though the two methods coincide when applied on the nominal/true physical-array autocorrelations, their performance differs significantly when applied on finite-snapshot estimates. In this paper, we present for the first time in closed form the mean-squared-error of both selection and averaging autocorrelation sampling and clarify/establish the superiority of the latter.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"501 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131900695","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313102
Robin Rajamäki, V. Koivunen
Co-array based processing enables sparse arrays to achieve the resolution of uniform arrays in array imaging applications. In particular, a desired point spread function may be synthesized by coherently adding together several component images obtained using different complex-valued physical element weights. However, ambiguities in the weight assignment arise when the co-array of a given array configuration contains redundancies. A suboptimal assignment leads to using more component images that necessary, which may increase the acquisition time of the final image. This paper shows that the number of component images in active transmit-receive imaging can be minimized by formulating a low-rank matrix recovery problem that is solved uniquely and efficiently using convex optimization. The suggested method may also be applied to passive sensing with minor modifications. The performance of the proposed method is compared to uniformly distributing co-array weights among physical array elements, which is typically used for simplicity. Numerical simulations show that the suggested method uses up to 60% fewer component images than uniform assignment.
{"title":"Sparse array imaging using low-rank matrix recovery","authors":"Robin Rajamäki, V. Koivunen","doi":"10.1109/CAMSAP.2017.8313102","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313102","url":null,"abstract":"Co-array based processing enables sparse arrays to achieve the resolution of uniform arrays in array imaging applications. In particular, a desired point spread function may be synthesized by coherently adding together several component images obtained using different complex-valued physical element weights. However, ambiguities in the weight assignment arise when the co-array of a given array configuration contains redundancies. A suboptimal assignment leads to using more component images that necessary, which may increase the acquisition time of the final image. This paper shows that the number of component images in active transmit-receive imaging can be minimized by formulating a low-rank matrix recovery problem that is solved uniquely and efficiently using convex optimization. The suggested method may also be applied to passive sensing with minor modifications. The performance of the proposed method is compared to uniformly distributing co-array weights among physical array elements, which is typically used for simplicity. Numerical simulations show that the suggested method uses up to 60% fewer component images than uniform assignment.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133609986","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313157
Javier Rodríguez-Fernández, N. G. Prelcic, R. Heath
Obtaining accurate channel state information is crucial to configure the antenna arrays and the digital precoders and combiners in hybrid millimeter wave (mmWave) MIMO architectures. Most of prior work on channel estimation with hybrid MIMO architectures relies on the use of finite-resolution dictionaries to estimate angles of arrival (AoA) and angles of departure (AoD). When the AoAs or AoDs do not fall within the quantization grids used to generate these dictionaries, there is an unavoidable grid error in the estimation of the channel. In this paper, we propose a mixed compressed sensing-maximum likelihood algorithm that uses continuous dictionaries to estimate the channel. The quantization error due to using finite resolution dictionaries can be neglected with this approach, enhancing estimation performance without resorting to very large dictionaries. Simulation results show how the new algorithm outperforms approaches based on finite resolution dictionaries previously proposed for the estimation of mmWave channels.
{"title":"A compressive sensing-maximum likelihood approach for off-grid wideband channel estimation at mmWave","authors":"Javier Rodríguez-Fernández, N. G. Prelcic, R. Heath","doi":"10.1109/CAMSAP.2017.8313157","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313157","url":null,"abstract":"Obtaining accurate channel state information is crucial to configure the antenna arrays and the digital precoders and combiners in hybrid millimeter wave (mmWave) MIMO architectures. Most of prior work on channel estimation with hybrid MIMO architectures relies on the use of finite-resolution dictionaries to estimate angles of arrival (AoA) and angles of departure (AoD). When the AoAs or AoDs do not fall within the quantization grids used to generate these dictionaries, there is an unavoidable grid error in the estimation of the channel. In this paper, we propose a mixed compressed sensing-maximum likelihood algorithm that uses continuous dictionaries to estimate the channel. The quantization error due to using finite resolution dictionaries can be neglected with this approach, enhancing estimation performance without resorting to very large dictionaries. Simulation results show how the new algorithm outperforms approaches based on finite resolution dictionaries previously proposed for the estimation of mmWave channels.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"447 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116230388","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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