A Low-Complexity Kalman Approach for Channel Estimation in Doubly-Selective OFDM Systems

Muralidhar, Karthik; Li, Kwok Hung

doi:10.1109/lsp.2009.2022145

Cited by 24 publications

(8 citation statements)

References 9 publications

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“…Maximum of the Doppler shift is set to 178 Hz (correspond to the subway train speed of 80 km / h). The pilot spacing meets the sampling law as follow: (8) The worst BER performance of the LS algorithm can be seen from Figure 4, while the BER performance of the LMMSE algorithm is followed. When the pilot interval is 4(in accordance with the sampling law), its BER performance of the L-ELM algorithm is little difference from the LMMSE algorithm, but when pilot interval is 8 (violation sampling law), its performance was significantly better than the previous two algorithms especially with the SNR increase.…”

Section: Simulation Results Analysismentioning

confidence: 99%

“…The channel estimation algorithm utilizes the SVM algorithm, which has heightened computation complexity [6][7]. In addition, the optimal channel estimation for OFDM system is using MMSE criterion [8].While following the guidelines of the estimation algorithm, the biggest drawback is the heightened computation complexity, so the low rank estimation attracts much attention because it can obtain higher performance in the case of reduced computation complexity. Through the determination of the signal subspace dimension and then obtaining the compromise between the computational complexity and estimated performance, that in the case of the estimation performance unchanged, the computational complexity has effectively reducing by reducing the rank of the matrix.…”

Section: Introductionmentioning

confidence: 99%

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The Novel Channel Estimation Algorithm Relying on ELM

Ding¹

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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An Extreme Learning Machine (ELM)-based novel channel estimation algorithm is proposed for orthogonal frequency division multiplexing (OFDM) communication system with multi-path propagation characteristics. In addition, a low-pass filter is utilized to filter the noise components which are separated from the information component of the channel characteristics. The simulation results show that the proposed novel algorithm significantly outdo the traditional least squares (LS) algorithm, linear minimum mean square error (LMMSE) estimation algorithm sometime even better than the Maximum Likelihood (ML) algorithm.

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Section: Simulation Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Novel Channel Estimation Algorithm Relying on ELM

Ding¹

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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“…4 and 5, the average bit error rates (BERs) of the IEKFS with N S = 5 are compared with those of the simple KF-based equalizer, LMMSE equalizer, LMMSE-ISDIC [9], combined Kalman receiver [23], and QRD-M [25], as well as with the MFB. The simple KF-based equalizer executes only (26) with the initial s k = 0 Ni×1 and v k = 1 Ni×1 . The performance of the LMMSE equalizer is based on the system model in (43).…”

Section: A Ber Comparison With Other Schemesmentioning

confidence: 99%

Iterative Equalizer Based on Kalman Filtering and Smoothing for MIMO-ISI Channels

Park

Choi

2015

IEEE Trans. Signal Process.

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This paper proposes an iterative equalizer based on Kalman filtering and smoothing (IEKFS) for multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels. A state-space model with a priori information and the corresponding Kalman filtering (KF) and Kalman smoothing (KS) operations are developed. The KF operations perform a linear minimum mean-square error (MMSE) equalization procedure with soft interference cancellation. In addition, the KF and KS operations produce and exchange the updated extrinsic information. During this IEKFS process, the soft estimate of a desired symbol does not participate in the equalization procedures for the desired symbol; only the feedback information of the other transmit symbols is used. Therefore, the proposed IEKFS performs iterative linear MMSE equalization based on the Kalman framework and turbo principle. The complexity of the IEKFS is linear with respect to the number of transmit signal vectors in a transmission block, and simulation results show that the IEKFS can achieve near-optimum bit error rate performances approaching the matched filter bound (MFB) of the channel in various environments. Index TermsMIMO systems, frequency-selective fading, ISI, Kalman filtering, Kalman smoothing, iterative equalizer, a priori information nulling.

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“…The per-path KF applied in this condition has been analyzed in [21]- [23] (called VSSO KF associate to a DTE channel channel). But in practice, the physical multi-path delays are not ensured to be multiples of T s , thus F H p F p = N p I L , or equivalently the DTE channel is correlated.…”

Section: ) Comparison With the Joint Multi-path Kfmentioning

confidence: 99%

“…We will show through simulations that the proposed estimator provides as good a performance as the highdimensional KF, with reduced complexity in case the number of multi-path components is small compared to the number of pilot subcarriers. This condition is generally true and necessary to the VSSO method [21], [22]. Another interesting aspect of this study, in addition to being a comprehensive study, is that the expression of the asymptotic variance performance of the proposed estimator is provided for the first to third orders of the RW model.…”

Section: Introductionmentioning

confidence: 96%

Simplified Random-Walk-Model-Based Kalman Filter for Slow to Moderate Fading Channel Estimation in OFDM Systems

Shu

Ros

Simon³

2014

IEEE Trans. Signal Process.

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Abstract-This study deals with multi-path channel estimation for orthogonal frequency division multiplexing systems under slow to moderate fading conditions. Advanced algorithms exploit the channel time-domain correlation by using Kalman Filters (KFs) based on an approximation of the time-varying channel. Recently, it was shown that under slow to moderate fading, near optimal channel multi-path complex amplitude estimation can be obtained by using the integrated Random Walk (RW) model as the channel approximation. To reduce the complexity of the high-dimensional RW-KF for joint estimation of the multi-path complex amplitudes, we propose using a lower dimensional RW-KF that estimates the complex amplitude of each path separately. We demonstrate that this amounts to a simplification of the joint multi-path Kalman gain formulation through the Woodbury's identities. Hence, this new algorithm consists of a superposition of independent single-path single-carrier KFs, which were optimized in our previous studies. This observation allows us to adapt the optimization to the actual multi-path multi-carrier scenario, to provide analytic formulae for the mean-square error performance and the optimal tuning of the proposed estimator directly as a function of the physical parameters of the channel (Doppler frequency, Signal-to-Noise-Ratio, Power Delay Profile). These analytic formulae are given for the first-, second-, and thirdorder RW models used in the KF. The proposed per-path KF is shown to be as efficient as the exact KF (i.e., the joint multipath KF), and outperforms the autoregressive-model-based KFs proposed in the literature.

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A Low-Complexity Kalman Approach for Channel Estimation in Doubly-Selective OFDM Systems

Cited by 24 publications

References 9 publications

The Novel Channel Estimation Algorithm Relying on ELM

The Novel Channel Estimation Algorithm Relying on ELM

Iterative Equalizer Based on Kalman Filtering and Smoothing for MIMO-ISI Channels

Simplified Random-Walk-Model-Based Kalman Filter for Slow to Moderate Fading Channel Estimation in OFDM Systems

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