On the Capacity of Iteratively Estimated Channels Using LMMSE Estimators

Movahedian, Alireza; McGuire, Michael

doi:10.1109/tvt.2014.2320928

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…Further, several techniques can be used to improve the original channel estimation result, such as iterative linear estimation [16] [17]. SinceĤ is the final channel estimation result after improvement, (3) is obtained.…”

Section: A Channel Estimation and Pilot Contaminationmentioning

confidence: 99%

Robust Equalizer for Multicell Massive MIMO Uplink With Channel Model Uncertainty

Gao

You

2016

IEEE Trans. Veh. Technol.

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We study the robust linear equalizer for the uplink of massive multiple-input-multiple-output (MIMO) systems with multi-cell pilot reuse. We use the worstcase approach for robust design in order to combat pilot contamination. When the number of the base station (BS) antennas is large, we build a novel model to indicate the relationship between the instantaneous channel matrix and the imperfect channel estimation in the presence of channel model uncertainty. Based on this model, we formulate the robust equalizer design problem into a minmax problem. Further, we transform the min-max problem to an unconstrained one, and the optimality conditions are derived. With the resulting optimality conditions, two iterative algorithms and a simple approximation method are proposed to compute the optimal robust equalizers. Simulations are adopted to evaluate the performance of the proposed algorithms and approximation method. Compared to the conventional equalizers, the proposed robust equalizers achieve better bit-error-rate (BER) performance, especially in the regime of high signal-to-noise ratio (SNR), where pilot contamination is significant.

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Section: A Channel Estimation and Pilot Contaminationmentioning

confidence: 99%

Robust Equalizer for Multicell Massive MIMO Uplink With Channel Model Uncertainty

Gao

You

2016

IEEE Trans. Veh. Technol.

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“…The Kalman filter channel impulse response predictor can be employed for vehicular communications [2] [3] to effectively predict the fast multipath fading channels. By employing the channel estimation of the AR-1 channel, we can track the complex-exponential basis expansion models (CE-BEMs) channel state parameters [4] [5]. The Kalman filter channel impulse response predictor is an effective and low-cost channel response predictor.…”

Section: Introductionmentioning

confidence: 99%

Joint Kalman Channel Estimation and Turbo Equalization for MIMO OFDM Systems over Fast Fading Channels

2019

KSII TIIS

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The paper investigates a novel detector receiver with Kalman channel information estimator and iterative channel response equalization for MIMO (multi-input multi-output) OFDM (orthogonal frequency division multiplexing) communication systems in fast multipath fading environments. The performances of the existing linear equalizers (LE) are not good enough over most fast fading multipath channels. The existing adaptive equalizer with decision feedback structure (ADFE) can improve the performance of LE. But error-propagation effect seriously degrades the system performance of the ADFE, especially when operated in fast multipath fading environments. By considering the Kalman channel impulse response estimation for the fast fading multipath channels based on CE-BEM (complex exponential basis expansion) model, the paper proposes the iterative receiver with soft decision feedback equalization (SDFE) structure in the fast multipath fading environments. The proposed SDFE detector receiver combats the error-propagation effect for fast multipath fading channels and outperform the existing LE and ADFE. We demonstrate several simulations to confirm the ability of the proposed iterative receiver over the existing receivers.

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Improved scheme for the problem of anti‐fading of DSRC systems in IEEE 802.11p environments

2016

IET Communications

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On the Capacity of Iteratively Estimated Channels Using LMMSE Estimators

Cited by 6 publications

References 48 publications

Robust Equalizer for Multicell Massive MIMO Uplink With Channel Model Uncertainty

Robust Equalizer for Multicell Massive MIMO Uplink With Channel Model Uncertainty

Joint Kalman Channel Estimation and Turbo Equalization for MIMO OFDM Systems over Fast Fading Channels

Improved scheme for the problem of anti‐fading of DSRC systems in IEEE 802.11p environments

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