EM-Based Maximum-Likelihood Channel Estimation in Multicarrier Systems With Phase Distortion

Carvajal, Rodrigo; Agüero, Juan C.; Godoy, Boris I.; Goodwin, Graham C.

doi:10.1109/tvt.2012.2217361

Cited by 27 publications

(22 citation statements)

References 40 publications

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“…For example, at SN R = 20 dB the estimator stops only after 2 iterations, on average. This demonstrates the efficiency of our proposed estimator compared to [4], which require 150 iterations for convergence.…”

Section: Proposed Ecm Based Estimatormentioning

confidence: 70%

“…Many CFO and PHN estimators are proposed for mitigating the effects of CPE and ICI, e.g., [3] and [4]. However, the estimation approach in [3] is based on a small angle approximation that adversely affects estimation performance.…”

Section: Introductionmentioning

confidence: 99%

“…The approach in [3] is also computationally very complex because it requires large matrix inversions. Most recently, the authors in [4] proposed a new PHN and CFO estimation based on the expectation-maximization approach. Even though the estimator in [4] can track the PHN parameters, its performance is only verified at signal-to-noise ratio (SNR) of 10 dB, it requires a large number of iterations, and it is not in closedform.…”

Section: Introductionmentioning

confidence: 99%

“…Most recently, the authors in [4] proposed a new PHN and CFO estimation based on the expectation-maximization approach. Even though the estimator in [4] can track the PHN parameters, its performance is only verified at signal-to-noise ratio (SNR) of 10 dB, it requires a large number of iterations, and it is not in closedform. More importantly, both [3] and [4] do not provide the Cramér-Rao lower bound (HCRB) for joint estimation of PHN and CFO in OFDM systems.…”

Section: Introductionmentioning

confidence: 99%

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Phase noise and carrier frequency offset in OFDM systems: Joint estimation and hybrid Cramér-Rao lower bound

Salim

Nasir

Mehrpouyan

et al. 2013

2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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In this paper, a new iterative pilot-aided algorithm based on expectation conditional maximization (ECM) for joint estimation of Wiener phase noise (PHN) and carrier frequency offset (CFO) in orthogonal frequency division multiplexing (OFDM) systems is proposed. Next, a new expression for the hybrid Cramér-Rao lower bound (HCRB) for joint estimation of PHN and CFO in OFDM systems is derived. Numerical results show that the proposed estimator outperforms existing algorithms in terms of mean square error while performing close to the derived HCRB at moderate PHN variances. Moreover, the proposed estimator is found to be computationally more efficient than existing algorithms since it jointly estimates PHN and CFO in a few iterations.

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Section: Proposed Ecm Based Estimatormentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Phase noise and carrier frequency offset in OFDM systems: Joint estimation and hybrid Cramér-Rao lower bound

Salim

Nasir

Mehrpouyan

et al. 2013

2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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show abstract

“…The EM algorithm is an iterative method for obtaining the ML estimate of the parameter λ based on an observation r. This algorithm iterates between two steps: the expectation step and the maximization step until reaching the state of convergence. [30][31][32] It relies on the concept of the so-called unobserved (or missing) data symbols a, such that, if the unobserved data symbols were known, it would be easy to estimate λ. However, since the missing data symbols are unknown, starting with an initial estimate of λ, an iterative approach is deployed.…”

Section: Em Estimation Algorithmmentioning

confidence: 99%

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

El-Hamid

Hassan

Zekry

et al. 2019

Int J Communication

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Summary In realistic scenarios of cognitive radio (CR) systems, imperfect channel sensing may occur due to false alarms and miss detections. Channel estimation between the secondary user transmitter and another secondary user receiver is another challenge in CR systems, especially for frequency‐selective fading channels. In this context, this paper presents a study of the effects of imperfect channel sensing and channel estimation on the performance of CR systems. In particular, different methods of channel estimation are analyzed under channel sensing imperfections. Initially, a CR system model with channel sensing errors is described. Then, the expectation maximization (EM) algorithm is implemented in order to learn the channel fading coefficients. By exploiting the pilot symbols and the detected symbols at the secondary user receiver, we can estimate the channel coefficients. We further compare the proposed EM estimation algorithm with different estimation algorithms such as the least squares (LS) and linear minimum mean square error (LMMSE). The expressions of channel estimates and mean squared errors (MSE) are determined, and their dependencies on channel sensing uncertainty are investigated. Finally, to reduce the complexity of EM algorithm, a sub‐optimal algorithm is also proposed. The obtained results show that the proposed sub‐optimal algorithm provides a comparable bit error rate (BER) performance with that of the optimal one yet with less computational complexity.

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RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

Mohammadian

Tellambura

2021

IEEE Access

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Wireless transceivers for mass-market applications must be cost effective. We may achieve this goal by deploying non-ideal low-cost radio frequency (RF) analog components. However, their imperfections may result in RF impairments, including phase noise (PN), carrier frequency offset (CFO), and in-phase (I) and quadrature-phase (Q) imbalance. These impairments introduce in-band and out-of-band interference terms and degrade the performance of wireless systems. In this survey, we present RF-impairment signal models and discuss their impacts. Moreover, we review RF-impairment estimation and compensation in singlecarrier (SC) and multicarrier systems, especially orthogonal frequency division multiplexing (OFDM). Furthermore, we discuss the effects of the RF impairments in already-established wireless technologies, e.g., multiple-input multiple-output (MIMO), massive MIMO, full-duplex, and millimeter-wave communications and review existing estimation and compensation algorithms. Finally, future research directions investigate the RF impairments in emerging technologies, including cell-free massive MIMO communications, nonorthogonal multicarrier systems, non-orthogonal multiple access (NOMA), ambient backscatter communications, and intelligent reflecting surface (IRS)-assisted communications. Furthermore, we discuss artificial intelligence (AI) approaches for developing estimation and compensation algorithms for RF impairments.

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EM-Based Maximum-Likelihood Channel Estimation in Multicarrier Systems With Phase Distortion

Cited by 27 publications

References 40 publications

Phase noise and carrier frequency offset in OFDM systems: Joint estimation and hybrid Cramér-Rao lower bound

Phase noise and carrier frequency offset in OFDM systems: Joint estimation and hybrid Cramér-Rao lower bound

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

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EM-Based Maximum-Likelihood Channel Estimation in Multicarrier Systems With Phase Distortion

Cited by 27 publications

References 40 publications

Phase noise and carrier frequency offset in OFDM systems: Joint estimation and hybrid Cram&#x00E9;r-Rao lower bound

Phase noise and carrier frequency offset in OFDM systems: Joint estimation and hybrid Cram&#x00E9;r-Rao lower bound

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

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Product

Resources

About

Phase noise and carrier frequency offset in OFDM systems: Joint estimation and hybrid Cramér-Rao lower bound

Phase noise and carrier frequency offset in OFDM systems: Joint estimation and hybrid Cramér-Rao lower bound