Modified Kalman Filters for Channel Estimation in Orthogonal Space-Time Coded Systems

Loiola, Murilo Bellezoni; Lopes, Renato da Rocha; Romano, João Marcos Travassos

doi:10.1109/tsp.2011.2170682

Cited by 24 publications

(19 citation statements)

References 13 publications

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“…Hence, following [16], [22], [23], we herein approximate the time evolution of channel coefficients by first order auto-regressive, AR(1), processes due to their simplicity. This is possible since the first few correlation terms of (23), for small lags, capture most of the channel dynamics [23].…”

Section: A Time Varying and Spatially Correlated Channel Modelmentioning

confidence: 99%

“…Then, by defining h k = vec(H k ) ∈ C M R M T ×1 as the vector resulting from stacking the columns of H k on top of each other, we can express the time-varying AR(1) correlated channel model as [16] h corr…”

Section: A Time Varying and Spatially Correlated Channel Modelmentioning

confidence: 99%

“…Moreover, we assess the system performance under more realistic conditions, where a timevarying and spatially-correlated channel model [16] is adopted, and two channel estimation methods are considered [3], [17]. Additionally, the BER performance of the proposed scheme is compared with other closed-loop codebook-based schemes with four transmit antennas and unitary spatial transmission rate, which allows a more comprehensive performance evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Codebook Design and Performance Analysis of Quantized Beamforming under Perfect and Imperfect Channel State Information

Valduga¹,

Almeida²,

Machado

et al. 2017

JCIS

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Abstract-This paper presents a codebook design to multiple transmit and multiple receive antenna system with quantized feedback channel. The codebook design is based on trigonometrical expressions that give a quasi-orthogonal structure to the codes. The codebook is designed to a specific number of transmit antennas and can easily be adapted to different number of feedback bits. An instantaneous signalto-noise ratio analysis is performed which is used to optimize the codebooks. An upper bound on the bit error rate (BER) performance for the proposed scheme is provided as well. We also investigate the system performance under more realistic conditions, and for this purpose we consider a time-varying and spatially-correlated channel model. We compare the BER performance of the proposed scheme with other closed-loop codebook-based schemes. Results show that the proposed scheme outperforms other good schemes in terms of array gain.Index Terms-MIMO, beamforming, quantized feedback, BER and channel estimation.

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Section: A Time Varying and Spatially Correlated Channel Modelmentioning

confidence: 99%

Section: A Time Varying and Spatially Correlated Channel Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Codebook Design and Performance Analysis of Quantized Beamforming under Perfect and Imperfect Channel State Information

Valduga¹,

Almeida²,

Machado

et al. 2017

JCIS

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“…To estimate the channel vector h n , recursive least square (RLS) or Kalman channel estimators have been popularly used [28], [29]. …”

Section: B Single Carrier Systemsmentioning

confidence: 99%

Statistical Recovery of Simultaneously Sparse Time-Varying Signals From Multiple Measurement Vectors

Choi¹,

Shim²

2015

IEEE Trans. Signal Process.

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In this paper, we propose a new sparse signal recovery algorithm, referred to as sparse Kalman tree search (sKTS), that provides a robust reconstruction of the sparse vector when the sequence of correlated observation vectors are available. The proposed sKTS algorithm builds on expectation-maximization (EM) algorithm and consists of two main operations: 1) Kalman smoothing to obtain the a posteriori statistics of the source signal vectors and 2) greedy tree search to estimate the support of the signal vectors. Through numerical experiments, we demonstrate that the proposed sKTS algorithm is effective in recovering the sparse signals and performs close to the Oracle (genie-based) Kalman estimator. Index TermsCompressed sensing, simultaneously sparse signal, multiple measurement vector, expectation-maximization (EM) algorithm, maximum likelihood estimation Jun Won Choi is with

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“…To extend KF to the nonlinear system with Gaussian noise, modified KFs such as extended KF (EKF) and unscented KF (UKF), have been proposed [7][8][9]. However, for the system with high nonlinearity, the poor state estimation results will be obtained [5,6].…”

Section: Introductionmentioning

confidence: 99%

An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems

Havangi

2018

Automatika

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To cite this article: Ramazan Havangi (2018) An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems, Automatika, 59:1, 94-103, DOI: 10.1080/00051144.2018 ABSTRACTParticle filters have been widely used in nonlinear/non-Gaussian Bayesian state estimation problems. However, the particle filter (PF) is inconsistent over time. The inconsistency of PF mainly results from the particle depletion in resampling step and an incorrect priori knowledge of process and measurement noise. To cope with this problem and enhance the accuracy and consistency of the state estimation, an adaptive particle filter(APF) is proposed in this paper. In APF, an adaptive fuzzy square-root unscented Kalman filter (AFSRUKF) is used to generate the proposal distribution. This causes that beside the merit of reducing the computational cost, APF has some other advantages such as increasing consistency that leads to more numerical stability and better performance. Moreover,APF can work in unknown statistical noise behaviour and is more robust. This is why the fuzzy inference system (FIS) supervises the performance of square-root unscented particle filter (SRUPF) using tuning statistical noises. In APF, to increase the diversity of particles, the resampling process is done based on the particle swarm optimization (PSO). With this resampling strategy, the small-weight particles are modified to the large-weight ones without duplication and elimination of particles. The effectiveness of APF is demonstrated by using two experiment examples through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method. ARTICLE HISTORY

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Modified Kalman Filters for Channel Estimation in Orthogonal Space-Time Coded Systems

Cited by 24 publications

References 13 publications

Codebook Design and Performance Analysis of Quantized Beamforming under Perfect and Imperfect Channel State Information

Codebook Design and Performance Analysis of Quantized Beamforming under Perfect and Imperfect Channel State Information

Statistical Recovery of Simultaneously Sparse Time-Varying Signals From Multiple Measurement Vectors

An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems

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