The Experts below are selected from a list of 79905 Experts worldwide ranked by ideXlab platform
Robert W. Heath - One of the best experts on this subject based on the ideXlab platform.
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Channel Estimation for orthogonal time frequency space otfs massive mimo
IEEE Transactions on Signal Processing, 2019Co-Authors: Wenqian Shen, Linglong Dai, Pingzhi Fan, Robert W. HeathAbstract:Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink Channel Estimation due to the large number of base station antennas. In this paper, we propose a 3D-structured orthogonal matching pursuit algorithm based Channel Estimation technique to solve this problem. First, we show that the OTFS MIMO Channel exhibits 3D-structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D-structured Channel sparsity, we then formulate the downlink Channel Estimation problem as a sparse signal recovery problem. Simulation results show that the proposed algorithm can achieve accurate Channel state information with low pilot overhead.
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Channel Estimation for orthogonal time frequency space otfs massive mimo
International Conference on Communications, 2019Co-Authors: Wenqian Shen, Linglong Dai, Shuangfeng Han, I Chihlin, Robert W. HeathAbstract:Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink Channel Estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based Channel Estimation technique. First, we show that the OTFS MIMO Channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured Channel sparsity, we then formulate the downlink Channel Estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate Channel state information with low pilot 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, Nuria González Prelcic, Ahmed Alkhateeb, 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.
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Blind Channel Estimation for MIMO-OFDM Systems
IEEE Transactions on Vehicular Technology, 2007Co-Authors: Cheolkyu Shin, Robert W. Heath, Edward J. PowersAbstract:By combining multiple-input multiple-output (MIMO) communication with the orthogonal frequency division multiplexing (OFDM) modulation scheme, MIMO-OFDM systems can achieve high data rates over broadband wireless Channels. In this paper, to provide a bandwidth-efficient solution for MIMO-OFDM Channel Estimation, we establish conditions for Channel identifiability and present a blind Channel Estimation technique based on a subspace approach. The proposed method unifies and generalizes the existing subspace-based methods for blind Channel Estimation in single-input single-output OFDM systems to blind Channel Estimation for two different MIMO-OFDM systems distinguished according to the number of transmit and receive antennas. In particular, the proposed method obtains accurate Channel Estimation and fast convergence with insensitivity to overestimates of the true Channel order. If virtual carriers (VCs) are available, the proposed method can work with no or insufficient cyclic prefix (CP), thereby potentially increasing Channel utilization. Furthermore, it is shown under specific system conditions that the proposed method can be applied to MIMO-OFDM systems without CPs, regardless of the presence of VCs, and obtains an accurate Channel estimate with a small number of OFDM symbols. Thus, this method improves the transmission bandwidth efficiency. Simulation results illustrate the mean-square error performance of the proposed method via numerical experiments
Rui Zhang - One of the best experts on this subject based on the ideXlab platform.
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wireless communication via double irs Channel Estimation and passive beamforming designs
IEEE Wireless Communications Letters, 2021Co-Authors: Changsheng You, Beixiong Zheng, Rui ZhangAbstract:In this letter, we study efficient Channel Estimation and passive beamforming designs for a double-intelligent reflecting surface (IRS) aided single-user communication system, where a user communicates with an access point (AP) via the cascaded user-IRS 1-IRS 2-AP double-reflection link. First, a general Channel Estimation scheme is proposed for the system under any arbitrary inter-IRS Channel, where all coefficients of the cascaded Channel are estimated. Next, for the typical scenario with a line-of-sight (LoS)-dominant inter-IRS Channel, we propose another customized scheme to estimate two signature vectors of the rank-one cascaded Channel with significantly less Channel training time than the first scheme. For the two proposed Channel Estimation schemes, we further optimize their corresponding cooperative passive beamforming for data transmission to maximize the achievable rate with the training overhead and Channel Estimation error taken into account. Numerical results show that deploying two cooperative IRSs with the proposed Channel Estimation and passive beamforming designs achieves significant rate enhancement as compared to the conventional case of single IRS deployment.
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intelligent reflecting surface assisted multi user ofdma Channel Estimation and training design
IEEE Transactions on Wireless Communications, 2020Co-Authors: Beixiong Zheng, Changsheng You, Rui ZhangAbstract:To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate Channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of Channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient Channel Estimation schemes for different Channel setups in an IRS-assisted multiuser broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first Channel Estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading Channels. In contrast, the second Channel Estimation scheme, which exploits a key property that all users share the same (common) IRS-AP Channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS Channels. For the two proposed Channel Estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the Channel Estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed Channel Estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.
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intelligent reflecting surface enhanced ofdm Channel Estimation and reflection optimization
arXiv: Information Theory, 2019Co-Authors: Beixiong Zheng, Rui ZhangAbstract:In the intelligent reflecting surface (IRS)-enhanced wireless communication system, Channel state information (CSI) is of paramount importance for achieving the passive beamforming gain of IRS, which, however, is a practically challenging task due to its massive number of passive elements without transmitting/receiving capabilities. In this letter, we propose a practical transmission protocol to execute Channel Estimation and reflection optimization successively for an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system. Under the unit-modulus constraint, a novel reflection pattern at the IRS is designed to aid the Channel Estimation at the access point (AP) based on the received pilot signals from the user, for which the Channel Estimation error is derived in closed-form. With the estimated CSI, the reflection coefficients are then optimized by a low-complexity algorithm based on the resolved strongest signal path in the time domain. Simulation results corroborate the effectiveness of the proposed Channel Estimation and reflection optimization methods.
Beixiong Zheng - One of the best experts on this subject based on the ideXlab platform.
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wireless communication via double irs Channel Estimation and passive beamforming designs
IEEE Wireless Communications Letters, 2021Co-Authors: Changsheng You, Beixiong Zheng, Rui ZhangAbstract:In this letter, we study efficient Channel Estimation and passive beamforming designs for a double-intelligent reflecting surface (IRS) aided single-user communication system, where a user communicates with an access point (AP) via the cascaded user-IRS 1-IRS 2-AP double-reflection link. First, a general Channel Estimation scheme is proposed for the system under any arbitrary inter-IRS Channel, where all coefficients of the cascaded Channel are estimated. Next, for the typical scenario with a line-of-sight (LoS)-dominant inter-IRS Channel, we propose another customized scheme to estimate two signature vectors of the rank-one cascaded Channel with significantly less Channel training time than the first scheme. For the two proposed Channel Estimation schemes, we further optimize their corresponding cooperative passive beamforming for data transmission to maximize the achievable rate with the training overhead and Channel Estimation error taken into account. Numerical results show that deploying two cooperative IRSs with the proposed Channel Estimation and passive beamforming designs achieves significant rate enhancement as compared to the conventional case of single IRS deployment.
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intelligent reflecting surface assisted multi user ofdma Channel Estimation and training design
IEEE Transactions on Wireless Communications, 2020Co-Authors: Beixiong Zheng, Changsheng You, Rui ZhangAbstract:To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate Channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of Channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient Channel Estimation schemes for different Channel setups in an IRS-assisted multiuser broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first Channel Estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading Channels. In contrast, the second Channel Estimation scheme, which exploits a key property that all users share the same (common) IRS-AP Channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS Channels. For the two proposed Channel Estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the Channel Estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed Channel Estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.
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intelligent reflecting surface enhanced ofdm Channel Estimation and reflection optimization
arXiv: Information Theory, 2019Co-Authors: Beixiong Zheng, Rui ZhangAbstract:In the intelligent reflecting surface (IRS)-enhanced wireless communication system, Channel state information (CSI) is of paramount importance for achieving the passive beamforming gain of IRS, which, however, is a practically challenging task due to its massive number of passive elements without transmitting/receiving capabilities. In this letter, we propose a practical transmission protocol to execute Channel Estimation and reflection optimization successively for an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system. Under the unit-modulus constraint, a novel reflection pattern at the IRS is designed to aid the Channel Estimation at the access point (AP) based on the received pilot signals from the user, for which the Channel Estimation error is derived in closed-form. With the estimated CSI, the reflection coefficients are then optimized by a low-complexity algorithm based on the resolved strongest signal path in the time domain. Simulation results corroborate the effectiveness of the proposed Channel Estimation and reflection optimization methods.
Mark C Reed - One of the best experts on this subject based on the ideXlab platform.
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iterative turbo Channel Estimation for ofdm system over rapid dispersive fading Channel
IEEE Transactions on Wireless Communications, 2008Co-Authors: Ming Zhao, Zhenning Shi, Mark C ReedAbstract:Coherent OFDM detection requires accurate Channel state information (CSI). Mobile radio Channels are both time and frequency dispersive, especially at high vehicular speeds, which makes Channel Estimation a challenging problem in system design. Conventional preamble-based and pilot-aided Channel Estimation require numerous reference signals, which significantly compromises the system throughput. This paper proposes a novel low complexity iterative turbo Channel Estimation technique which makes use of preamble, pilots and soft decoded data information in an iterative fashion to improve the system performance over the time and frequency selective fading Channel while maintaining the system throughput. The numerical and analytical results show that the proposed technique can approach the performance of systems with perfect CSI with much fewer preamble and pilots symbols compared to existing Channel Estimation methods.
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iterative turbo Channel Estimation for ofdm system over rapid dispersive fading Channel
International Conference on Communications, 2007Co-Authors: Ming Zhao, Zhenning Shi, Mark C ReedAbstract:Current OFDM systems assume the Channel is not time varying within one OFDM frame, and use Channel estimates obtained from preamble or pilots for data symbols within the same frame. This performs poorly in rapid dispersive fading Channel with high mobility. This paper proposes a novel iterative turbo Channel Estimation to improve the performance of OFDM system over fading Channel with both time and frequency selectivity. Unlike the conventional Channel Estimation techniques only using preamble or pilots, the proposed Channel Estimation technique makes use of preamble, pilots and soft coded data information in a turbo iterative approach. Mean square error analysis and simulation results show that the proposed Channel Estimation technique can approach the performance of perfect Channel state information (CSI) with fewer pilots insertion.
Venugopal Kiran - One of the best experts on this subject based on the ideXlab platform.
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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, Nuria González Prelcic, Ahmed Alkhateeb, 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.
