The Experts below are selected from a list of 120084 Experts worldwide ranked by ideXlab platform
Dmitry Yarotsky - One of the best experts on this subject based on the ideXlab platform.
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Lower Performance Bound for Beamspace Channel Estimation in Massive MIMO
IEEE Wireless Communications Letters, 2021Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry Lakontsev, Dmitry YarotskyAbstract:In this letter, we present a lower Performance Bound for discrete Fourier transform (DFT)-based beamspace channel estimation (CE) in 64 antennas Massive Multiple-Input Multiple-Output (MIMO) receiver. The beamspace CE is implemented after antennas digital signal transformation to a priori estimated DFT sub-space resulting in less complexity. To estimate the Performance, we calculate noise power after the beamspace CE unit by decomposing an ideal channel response into separate taps and transforming them into the beam domain. Then the minimum mean squared error (MMSE) method is employed with taps to estimate the CE error power. An artificial CE is calculated as a sum of the ideal beamspace channel and additive Gaussian noise with the same error power. The artificial CE is utilized in the MIMO detector and decoder to achieve Performance Bound. Simulation results are presented for the non-line-of-sight model of the 5G QuaDRiGa 2.0 channel.
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theoretical Performance Bound of uplink channel estimation accuracy in massive mimo
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry YarotskyAbstract:In this paper, we present a new Performance Bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power prediction after the CE unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower Bound (CRLB) due to considering more statistics since Performance strongly depends on a number of channel taps and power ratio between them. Simulation results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.
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ICASSP - Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO
ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry YarotskyAbstract:In this paper, we present a new Performance Bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power prediction after the CE unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower Bound (CRLB) due to considering more statistics since Performance strongly depends on a number of channel taps and power ratio between them. Simulation results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.
Alexander Osinsky - One of the best experts on this subject based on the ideXlab platform.
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Lower Performance Bound for Beamspace Channel Estimation in Massive MIMO
IEEE Wireless Communications Letters, 2021Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry Lakontsev, Dmitry YarotskyAbstract:In this letter, we present a lower Performance Bound for discrete Fourier transform (DFT)-based beamspace channel estimation (CE) in 64 antennas Massive Multiple-Input Multiple-Output (MIMO) receiver. The beamspace CE is implemented after antennas digital signal transformation to a priori estimated DFT sub-space resulting in less complexity. To estimate the Performance, we calculate noise power after the beamspace CE unit by decomposing an ideal channel response into separate taps and transforming them into the beam domain. Then the minimum mean squared error (MMSE) method is employed with taps to estimate the CE error power. An artificial CE is calculated as a sum of the ideal beamspace channel and additive Gaussian noise with the same error power. The artificial CE is utilized in the MIMO detector and decoder to achieve Performance Bound. Simulation results are presented for the non-line-of-sight model of the 5G QuaDRiGa 2.0 channel.
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theoretical Performance Bound of uplink channel estimation accuracy in massive mimo
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry YarotskyAbstract:In this paper, we present a new Performance Bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power prediction after the CE unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower Bound (CRLB) due to considering more statistics since Performance strongly depends on a number of channel taps and power ratio between them. Simulation results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.
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ICASSP - Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO
ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry YarotskyAbstract:In this paper, we present a new Performance Bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power prediction after the CE unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower Bound (CRLB) due to considering more statistics since Performance strongly depends on a number of channel taps and power ratio between them. Simulation results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.
Weihua Zhuang - One of the best experts on this subject based on the ideXlab platform.
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Variance of the turbo code Performance Bound over the interleavers
IEEE Transactions on Information Theory, 2002Co-Authors: A.h.s. Mohammadi, Weihua ZhuangAbstract:We evaluate the variance of the union Performance Bound for a rate-1/3 turbo code over all possible interleavers of length N, under the assumption of a maximum-likelihood (ML) decoder. Theoretical and simulation results for turbo codes with two-memory component codes indicate that the coefficient of variation of the Bound increases with the signal-to-noise ratio and decreases with the interleaver length. Theoretical analysis for large interleaver lengths shows that the coefficient of variation asymptotically approaches a constant value. The results also demonstrate that the majority of the interleavers have Performance Bounds very close to the average value of the Bound. This phenomenon is more palpable for larger interleaver lengths.
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Variance of the turbo-code Performance Bound over the interleavers
1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363), 1Co-Authors: A.h.s. Mohammadi, Weihua ZhuangAbstract:We evaluate the variance of the union Performance Bound for a turbo-code over all possible interleavers of length N. The asymptotic analysis (for large N) shows that the ratio of the standard deviation over the mean value of the Bound remains constant with respect to N. Numerical results corresponding to turbo-codes with two-memory component codes indicate that this ratio increases with the signal to noise ratio. Theoretical and simulation results demonstrate that the majority of the interleavers result in Performance Bounds very close to the average value of the Bound. This effect is stronger for larger interleaver lengths.
Guoyong Shi - One of the best experts on this subject based on the ideXlab platform.
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Performance Bound analysis of analog circuits in frequency and time domain considering process variations
ACM Transactions on Design Automation of Electronic Systems, 2013Co-Authors: Xue-xin Liu, Sheldon X.-d. Tan, A A Palmarodriguez, Esteban Tlelocuautle, Guoyong ShiAbstract:In this article, we propose a new Performance Bound analysis of analog circuits considering process variations. We model the variations of component values as intervals measured from tested chips and manufacture processes. The new method first applies a graph-based analysis approach to generate the symbolic transfer function of a linear(ized) analog circuit. Then the frequency response Bounds (maximum and minimum) are obtained by performing nonlinear constrained optimization in which magnitude or phase of the transfer function is the objective function to be optimized subject to the ranges of process variational parameters. The response Bounds given by the optimization-based method are very accurate and do not have the over-conservativeness issues of existing methods. Based on the frequency-domain Bounds, we further develop a method to calculate the time-domain response Bounds for any arbitrary input stimulus. Experimental results from several analog benchmark circuits show that the proposed method gives the correct Bounds verified by Monte Carlo analysis while it delivers one order of magnitude speedup over Monte Carlo for both frequency-domain and time-domain Bound analyses. We also show analog circuit yield analysis as an application of the frequency-domain variational Bound analysis.
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ASP-DAC - Time-domain Performance Bound analysis of analog circuits considering process variations
17th Asia and South Pacific Design Automation Conference, 2012Co-Authors: Xue-xin Liu, Sheldon X.-d. Tan, Zhigang Hao, Guoyong ShiAbstract:In this paper, we propose a new time-domain Performance Bound analysis method for analog circuits considering process variations. The proposed method, called TIDBA, consists of several steps to compute the Bound Performances in time domain. First the Performance Bound in frequency domain is computed for a linearized analog circuits by an variational symbolic analysis method and the Kharitonov's functions. Then the time domain Performance Bound is computed via a new general-signal transient Bound analysis method. The new algorithm can give transient lower Bound and upper Bound of the Performance variations affected analog circuits accurately and reliably. Experimental results from two industry benchmark circuits show that TIDBA gives the correct Bounds for the Monte Carlo analysis while it delivers one order of magnitude speedup over the Monte Carlo method.
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Performance Bound analysis of analog circuits considering process variations
Design Automation Conference, 2011Co-Authors: Zhigang Hao, Sheldon X.-d. Tan, Ruijing Shen, Guoyong ShiAbstract:In this paper, we propose a new Performance Bound analysis of analog circuits considering process variations. We model the variations of component values as intervals measured from tested chip and manufacture processes. The new method applies a graph-based symbolic analysis and affine interval arithmetic to derive the variational transfer functions of analog circuits (linearized) with variational coefficients in forms of intervals. Then the frequency response Bounds (maximum and minimum) are obtained by performing analysis of a finite number of transfer functions given by the Kharitonov's polynomial functions. We show that symbolic de-cancellation is critical for the affine interval analysis. The response Bound given by the Kharitonov's functions are conservative given the correlations among coefficient intervals in transfer functions. Experimental results demonstrate the effectiveness of the proposed compared to the Monte Carlo method.
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DAC - Performance Bound analysis of analog circuits considering process variations
Proceedings of the 48th Design Automation Conference on - DAC '11, 2011Co-Authors: Zhigang Hao, Sheldon X.-d. Tan, Ruijing Shen, Guoyong ShiAbstract:In this paper, we propose a new Performance Bound analysis of analog circuits considering process variations. We model the variations of component values as intervals measured from tested chip and manufacture processes. The new method applies a graph-based symbolic analysis and affine interval arithmetic to derive the variational transfer functions of analog circuits (linearized) with variational coefficients in forms of intervals. Then the frequency response Bounds (maximum and minimum) are obtained by performing analysis of a finite number of transfer functions given by the Kharitonov's polynomial functions. We show that symbolic de-cancellation is critical for the affine interval analysis. The response Bound given by the Kharitonov's functions are conservative given the correlations among coefficient intervals in transfer functions. Experimental results demonstrate the effectiveness of the proposed compared to the Monte Carlo method.
Andrey Ivanov - One of the best experts on this subject based on the ideXlab platform.
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Lower Performance Bound for Beamspace Channel Estimation in Massive MIMO
IEEE Wireless Communications Letters, 2021Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry Lakontsev, Dmitry YarotskyAbstract:In this letter, we present a lower Performance Bound for discrete Fourier transform (DFT)-based beamspace channel estimation (CE) in 64 antennas Massive Multiple-Input Multiple-Output (MIMO) receiver. The beamspace CE is implemented after antennas digital signal transformation to a priori estimated DFT sub-space resulting in less complexity. To estimate the Performance, we calculate noise power after the beamspace CE unit by decomposing an ideal channel response into separate taps and transforming them into the beam domain. Then the minimum mean squared error (MMSE) method is employed with taps to estimate the CE error power. An artificial CE is calculated as a sum of the ideal beamspace channel and additive Gaussian noise with the same error power. The artificial CE is utilized in the MIMO detector and decoder to achieve Performance Bound. Simulation results are presented for the non-line-of-sight model of the 5G QuaDRiGa 2.0 channel.
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theoretical Performance Bound of uplink channel estimation accuracy in massive mimo
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry YarotskyAbstract:In this paper, we present a new Performance Bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power prediction after the CE unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower Bound (CRLB) due to considering more statistics since Performance strongly depends on a number of channel taps and power ratio between them. Simulation results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.
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ICASSP - Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO
ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020Co-Authors: Alexander Osinsky, Andrey Ivanov, Dmitry YarotskyAbstract:In this paper, we present a new Performance Bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power prediction after the CE unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower Bound (CRLB) due to considering more statistics since Performance strongly depends on a number of channel taps and power ratio between them. Simulation results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.
