The Experts below are selected from a list of 58656 Experts worldwide ranked by ideXlab platform
Nuria González Prelcic - One of the best experts on this subject based on the ideXlab platform.
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Time-domain channel estimation for wideband millimeter wave systems with Hybrid Architecture
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Nuria González PrelcicAbstract:Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO Architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the Hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for Hybrid Architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.
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Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems
IEEE Journal on Selected Areas in Communications, 2017Co-Authors: Venugopal Kiran, Ahmed Alkhateeb, Nuria González Prelcic, Robert W. HeathAbstract:Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the Hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with Hybrid Architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for Hybrid Architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for Hybrid Architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the Hybrid Architecture at the transceivers gives further improvement in estimation error performance and achievable rates.
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channel estimation for Hybrid Architecture based wideband millimeter wave systems
arXiv: Information Theory, 2016Co-Authors: Kiran Venugopal, Ahmed Alkhateeb, Nuria González Prelcic, Robert W. HeathAbstract:Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the Hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with Hybrid Architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for Hybrid Architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for Hybrid Architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the Hybrid Architecture at the transceivers gives further improvement in estimation error performance and achievable rates.
Robert W. Heath - One of the best experts on this subject based on the ideXlab platform.
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Time-domain channel estimation for wideband millimeter wave systems with Hybrid Architecture
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Nuria González PrelcicAbstract:Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO Architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the Hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for Hybrid Architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.
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Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems
IEEE Journal on Selected Areas in Communications, 2017Co-Authors: Venugopal Kiran, Ahmed Alkhateeb, Nuria González Prelcic, Robert W. HeathAbstract:Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the Hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with Hybrid Architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for Hybrid Architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for Hybrid Architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the Hybrid Architecture at the transceivers gives further improvement in estimation error performance and achievable rates.
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channel estimation for Hybrid Architecture based wideband millimeter wave systems
arXiv: Information Theory, 2016Co-Authors: Kiran Venugopal, Ahmed Alkhateeb, Nuria González Prelcic, Robert W. HeathAbstract:Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the Hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with Hybrid Architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for Hybrid Architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for Hybrid Architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the Hybrid Architecture at the transceivers gives further improvement in estimation error performance and achievable rates.
Kiran Venugopal - One of the best experts on this subject based on the ideXlab platform.
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Time-domain channel estimation for wideband millimeter wave systems with Hybrid Architecture
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Nuria González PrelcicAbstract:Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO Architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the Hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for Hybrid Architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.
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channel estimation for Hybrid Architecture based wideband millimeter wave systems
arXiv: Information Theory, 2016Co-Authors: Kiran Venugopal, Ahmed Alkhateeb, Nuria González Prelcic, Robert W. HeathAbstract:Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the Hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with Hybrid Architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for Hybrid Architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for Hybrid Architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the Hybrid Architecture at the transceivers gives further improvement in estimation error performance and achievable rates.
Ahmed Alkhateeb - One of the best experts on this subject based on the ideXlab platform.
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Time-domain channel estimation for wideband millimeter wave systems with Hybrid Architecture
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Nuria González PrelcicAbstract:Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO Architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the Hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for Hybrid Architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.
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Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems
IEEE Journal on Selected Areas in Communications, 2017Co-Authors: Venugopal Kiran, Ahmed Alkhateeb, Nuria González Prelcic, Robert W. HeathAbstract:Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the Hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with Hybrid Architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for Hybrid Architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for Hybrid Architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the Hybrid Architecture at the transceivers gives further improvement in estimation error performance and achievable rates.
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channel estimation for Hybrid Architecture based wideband millimeter wave systems
arXiv: Information Theory, 2016Co-Authors: Kiran Venugopal, Ahmed Alkhateeb, Nuria González Prelcic, Robert W. HeathAbstract:Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the Hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with Hybrid Architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for Hybrid Architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for Hybrid Architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the Hybrid Architecture at the transceivers gives further improvement in estimation error performance and achievable rates.
Jean-pierre Briot - One of the best experts on this subject based on the ideXlab platform.
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A Hybrid Architecture for Multi-Party Conversational Systems.
arXiv: Computation and Language, 2017Co-Authors: Maíra Gatti De Bayser, Paulo R. Cavalin, Renan Souza, Alan Braz, Heloisa Candello, Claudio S. Pinhanez, Jean-pierre BriotAbstract:Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed Hybrid Architecture using both rules and machine learning and some insights after implementing and evaluating one on the finance domain.
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A Hybrid Architecture for Multi-Party Conversational Systems
2017Co-Authors: Maira Gatti De Bayser, Paulo R. Cavalin, Renan Souza, Alan Braz, Heloisa Candello, Claudio S. Pinhanez, Jean-pierre BriotAbstract:Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed Hybrid Architecture using both norms and machine learning and some insights after implementing and evaluating one on the finance domain.
