Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313142
Bho Matthiesen, Eduard Axel Jorswieck
This paper studies the maximization of the weighted sum rate in multi-way relay channels with simultaneous non-unique decoding at the receivers. We state the resource allocation problem as a global optimization problem of the transmit powers and achievable rates, and transform it into a monotonic optimization problem. The computational complexity of monotonic optimization problems is exponential in the number of variables. We observe that for fixed powers the problem is a linear program with much lower complexity and exploit this structural property by decomposing the optimization problem into an inner linear and an outer monotonic program. This reduces the computational complexity significantly and allows computing the global solution. We compare the achievable throughput with multi-user decoding and optimal power allocation numerically to state-of-the-art single-user decoding and to simply transmitting at maximum power. We observe that multi-user decoding performs much better than single-user decoding in terms of throughput and fairness for medium to high SNRs.
{"title":"Weighted sum rate maximization for non-regenerative multi-way relay channels with multi-user decoding","authors":"Bho Matthiesen, Eduard Axel Jorswieck","doi":"10.1109/CAMSAP.2017.8313142","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313142","url":null,"abstract":"This paper studies the maximization of the weighted sum rate in multi-way relay channels with simultaneous non-unique decoding at the receivers. We state the resource allocation problem as a global optimization problem of the transmit powers and achievable rates, and transform it into a monotonic optimization problem. The computational complexity of monotonic optimization problems is exponential in the number of variables. We observe that for fixed powers the problem is a linear program with much lower complexity and exploit this structural property by decomposing the optimization problem into an inner linear and an outer monotonic program. This reduces the computational complexity significantly and allows computing the global solution. We compare the achievable throughput with multi-user decoding and optimal power allocation numerically to state-of-the-art single-user decoding and to simply transmitting at maximum power. We observe that multi-user decoding performs much better than single-user decoding in terms of throughput and fairness for medium to high SNRs.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"81 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":"126222735","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.2017.8313154
Irene Santos, J. J. Murillo-Fuentes, P. Djurić
In this paper, we deal with the estimation of received signal strength (RSS) in a time-varying spatial field, where only low accuracy measurements and noisy locations of users are available. The spatial field is defined on a fixed grid of nodes with perfectly known locations. We employ a propagation model where the path loss exponent and the transmitter power are unknown, and where the locations of the reporting users are estimates and thereby with errors. We propose to estimate time-varying RSS fields by a recursive Bayesian approach that operates on data of low accuracy and obtained by crowdsourcing. The method is based on Gaussian processes, and it produces as a result the complete joint distribution of the unknowns. We also inject a forgetting factor that reduces the effect of old information on current estimates. Our method summarizes all the acquired information, keeping the memory size needed for estimation fixed, i.e., making it independent from the number of sensing users. We also present the Cramér-Rao bound (CRB) of the estimated parameters. Finally, we illustrate the performance of our method with some experimental results.
{"title":"Recursive estimation of time-varying RSS fields based on crowdsourcing and Gaussian processes","authors":"Irene Santos, J. J. Murillo-Fuentes, P. Djurić","doi":"10.1109/CAMSAP.2017.8313154","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313154","url":null,"abstract":"In this paper, we deal with the estimation of received signal strength (RSS) in a time-varying spatial field, where only low accuracy measurements and noisy locations of users are available. The spatial field is defined on a fixed grid of nodes with perfectly known locations. We employ a propagation model where the path loss exponent and the transmitter power are unknown, and where the locations of the reporting users are estimates and thereby with errors. We propose to estimate time-varying RSS fields by a recursive Bayesian approach that operates on data of low accuracy and obtained by crowdsourcing. The method is based on Gaussian processes, and it produces as a result the complete joint distribution of the unknowns. We also inject a forgetting factor that reduces the effect of old information on current estimates. Our method summarizes all the acquired information, keeping the memory size needed for estimation fixed, i.e., making it independent from the number of sensing users. We also present the Cramér-Rao bound (CRB) of the estimated parameters. Finally, we illustrate the performance of our method with some experimental results.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"160 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":"130090862","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.2017.8313211
Bakht Zaman, L. M. Lopez-Ramos, Daniel Romero, B. Beferull-Lozano
An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.
{"title":"Online topology estimation for vector autoregressive processes in data networks","authors":"Bakht Zaman, L. M. Lopez-Ramos, Daniel Romero, B. Beferull-Lozano","doi":"10.1109/CAMSAP.2017.8313211","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313211","url":null,"abstract":"An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"14 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":"128844493","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.2017.8313169
Michael Pauley, J. Manton
Optimisation geometry studies the geometry of a smooth class of optimisation problems on manifolds. A focus is placed on those classes that are fibre-wise Morse, i.e., such that in all specific problem instances, the objective function is Morse. If this condition holds, optimisation can be split into two parts: a (hard) preparation stage that computes certain lookup tables, and an (easy) optimisation stage that, given parameter values, uses the lookup tables to quickly find the global optimum for the particular problem instance. In this paper we show how the fibre-wise Morse condition can be automatically checked during the preparation stage. We also implement a version of the optimisation stage, thus providing a complete demonstration of the algorithm suggested by the theory. We discuss what goes wrong when the fibre-wise Morse condition fails and put forward some preliminary ideas on how these issues might be handled.
{"title":"Optimisation geometry and its implications for optimisation algorithms","authors":"Michael Pauley, J. Manton","doi":"10.1109/CAMSAP.2017.8313169","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313169","url":null,"abstract":"Optimisation geometry studies the geometry of a smooth class of optimisation problems on manifolds. A focus is placed on those classes that are fibre-wise Morse, i.e., such that in all specific problem instances, the objective function is Morse. If this condition holds, optimisation can be split into two parts: a (hard) preparation stage that computes certain lookup tables, and an (easy) optimisation stage that, given parameter values, uses the lookup tables to quickly find the global optimum for the particular problem instance. In this paper we show how the fibre-wise Morse condition can be automatically checked during the preparation stage. We also implement a version of the optimisation stage, thus providing a complete demonstration of the algorithm suggested by the theory. We discuss what goes wrong when the fibre-wise Morse condition fails and put forward some preliminary ideas on how these issues might be handled.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"17 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":"122387288","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.2017.8313171
M. Nokleby, W. Bajwa
We present and analyze two algorithms — termed distributed stochastic approximation mirror descent (D-SAMD) and accelerated distributed stochastic approximation mirror descent (AD-SAMD)—for distributed, stochastic optimization from high-rate data streams over rate-limited networks. Devices contend with fast streaming rates by mini-batching samples in the data stream, and they collaborate via distributed consensus to compute variance-reduced averages of distributed subgradients. This induces a trade-off: Mini-batching slows down the effective streaming rate, but may also slow down convergence. We present two theoretical contributions that characterize this trade-off: (i) bounds on the convergence rates of D-SAMD and AD-SAMD, and (ii) sufficient conditions for order-optimum convergence of D-SAMD and AD-SAMD, in terms of the network size/topology and the ratio of the data streaming and communication rates. We find that AD-SAMD achieves order-optimum convergence in a larger regime than D-SAMD. We demonstrate the effectiveness of the proposed algorithms using numerical experiments.
{"title":"Distributed mirror descent for stochastic learning over rate-limited networks","authors":"M. Nokleby, W. Bajwa","doi":"10.1109/CAMSAP.2017.8313171","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313171","url":null,"abstract":"We present and analyze two algorithms — termed distributed stochastic approximation mirror descent (D-SAMD) and accelerated distributed stochastic approximation mirror descent (AD-SAMD)—for distributed, stochastic optimization from high-rate data streams over rate-limited networks. Devices contend with fast streaming rates by mini-batching samples in the data stream, and they collaborate via distributed consensus to compute variance-reduced averages of distributed subgradients. This induces a trade-off: Mini-batching slows down the effective streaming rate, but may also slow down convergence. We present two theoretical contributions that characterize this trade-off: (i) bounds on the convergence rates of D-SAMD and AD-SAMD, and (ii) sufficient conditions for order-optimum convergence of D-SAMD and AD-SAMD, in terms of the network size/topology and the ratio of the data streaming and communication rates. We find that AD-SAMD achieves order-optimum convergence in a larger regime than D-SAMD. We demonstrate the effectiveness of the proposed algorithms using numerical experiments.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"241 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":"122468680","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.2017.8313161
Loris Cannelli, F. Facchinei, G. Scutari
We propose a novel algorithmic framework for the asynchronous and distributed optimization of multi-agent systems. We consider the constrained minimization of a nonconvex and nonsmooth partially separable sum-utility function, i.e., the cost function of each agent depends on the optimization variables of that agent and of its neighbors. This partitioned setting arises in several applications of practical interest. The proposed algorithmic framework is distributed and asynchronous: i) agents update their variables at arbitrary times, without any coordination with the others; and ii) agents may use outdated information from their neighbors. Convergence to stationary solutions is proved, and theoretical complexity results are provided, showing nearly ideal linear speedup with respect to the number of agents, when the delays are not too large.
{"title":"Multi-Agent asynchronous nonconvex large-scale optimization","authors":"Loris Cannelli, F. Facchinei, G. Scutari","doi":"10.1109/CAMSAP.2017.8313161","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313161","url":null,"abstract":"We propose a novel algorithmic framework for the asynchronous and distributed optimization of multi-agent systems. We consider the constrained minimization of a nonconvex and nonsmooth partially separable sum-utility function, i.e., the cost function of each agent depends on the optimization variables of that agent and of its neighbors. This partitioned setting arises in several applications of practical interest. The proposed algorithmic framework is distributed and asynchronous: i) agents update their variables at arbitrary times, without any coordination with the others; and ii) agents may use outdated information from their neighbors. Convergence to stationary solutions is proved, and theoretical complexity results are provided, showing nearly ideal linear speedup with respect to the number of agents, when the delays are not too large.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"41 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":"122702201","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.2017.8313133
Puyang Wang, He Zhang, Vishal M. Patel
Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Generative Adversarial Network (ID-GAN), for automatically removing speckle from the input noisy images. In particular, ID-GAN is trained in an end-to-end fashion using a combination of Euclidean loss, Perceptual loss and Adversarial loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.
{"title":"Generative adversarial network-based restoration of speckled SAR images","authors":"Puyang Wang, He Zhang, Vishal M. Patel","doi":"10.1109/CAMSAP.2017.8313133","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313133","url":null,"abstract":"Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Generative Adversarial Network (ID-GAN), for automatically removing speckle from the input noisy images. In particular, ID-GAN is trained in an end-to-end fashion using a combination of Euclidean loss, Perceptual loss and Adversarial loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"39 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":"126408571","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.2017.8313102
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.2017.8313174
Jianshu Zhang, M. Haardt
In this paper we study the channel estimation problem for a CP-OFDM based mmWave hybrid analog-digital MIMO system, where the analog processing is achieved using only phase shift networks. A two-stage three-dimensional (3-D) Unitary ESPRIT in DFT beamspace based channel estimation algorithm is proposed to estimate the angular-delay profile and subsequently the unknown frequency-selective channel. The required training protocol, analog precoding and decoding matrices, as well as pilot patterns are discussed. Simulation results show that the proposed multi-stage 3-D Unitary ESPRIT in DFT beamspace based channel estimation algorithm provides high resolution channel estimates.
{"title":"Channel estimation for hybrid multi-carrier mmwave MIMO systems using three-dimensional unitary esprit in DFT beamspace","authors":"Jianshu Zhang, M. Haardt","doi":"10.1109/CAMSAP.2017.8313174","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313174","url":null,"abstract":"In this paper we study the channel estimation problem for a CP-OFDM based mmWave hybrid analog-digital MIMO system, where the analog processing is achieved using only phase shift networks. A two-stage three-dimensional (3-D) Unitary ESPRIT in DFT beamspace based channel estimation algorithm is proposed to estimate the angular-delay profile and subsequently the unknown frequency-selective channel. The required training protocol, analog precoding and decoding matrices, as well as pilot patterns are discussed. Simulation results show that the proposed multi-stage 3-D Unitary ESPRIT in DFT beamspace based channel estimation algorithm provides high resolution channel estimates.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 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":"130458432","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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