Second-order LMS based wireless channel tracking: implementation under imperfect carrier synchronization

Xue, Yali; Zhu, Xiaohua

doi:10.1016/s0165-1684(02)00392-4

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…However, the author does not specify how to tune the two constant coefficients of the model. Xue and Zhu [18] proposed a method to obtain optimal coefficients, and Ros et al [15,19] presented the CA tracking algorithm and its optimisation from a second-order phase-locked-loop (PLL) point of view. Indeed, as it has been already shown by Driessen [20] and Christiansen [21] in the case of the phase estimation problem, a proportional-integral (second-order) PLL has the same structure as the KF when considering the second-order integrated RW model, and thereafter the closed-form Kalman gain expression is given in [22].…”

Section: Introductionmentioning

confidence: 99%

Third‐order complex amplitudes tracking loop for slow flat fading channel online estimation

2014

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This paper deals with channel estimation in tracking mode over a flat Rayleigh fading channel with Jakes' Doppler Spectrum. Many estimation algorithms exploit the time-domain correlation of the channel by employing a Kalman filter based on a first-order (or sometimes second-order) approximation model of the time-varying channel. However, the nature of the approximation model itself degrades the estimation performance for slow to moderate varying channel scenarios. Furthermore, the Kalman-based algorithms exhibit a certain complexity. Hence, a different model and approach has been investigated in this work to tackle all of these issues. A novel PLL-structured thirdorder tracking loop estimator with a low complexity is proposed. The connection between a steady-state Kalman filter based on a random walk approximation model and the proposed estimator is first established. Then, a sub-optimal mean-squared-error (MSE) is given in a closed-form expression as a function of the tracking loop parameters. The parameters that minimize this sub-optimal MSE are also given in a closed-form expression. The asymptotic MSE and Bit-Error-Ratio (BER) simulation results demonstrate that the proposed estimator outperforms the first and second order Kalman-based filters reported in literature. The robustness of the proposed estimator is also verified by a mismatch simulation.

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Section: Introductionmentioning

confidence: 99%

Third‐order complex amplitudes tracking loop for slow flat fading channel online estimation

2014

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“…However, in this case, the model will become nonlinear, leading to an Extended Kalman Predictor (EKP) [4, pp.37-41]. In [5], joint channel estimation and carrier synchronization of STTCs is formulated in an EKP framework. Another approach to deal with unknown parameters is to use a bank of KPs, each designed for a possible value of the unknown parameters [6].…”

Section: Introductionmentioning

confidence: 99%

MAP-PSP for Space-Time Trellis Codes Over Unknown Doppler Channels

Vilaipornsawai

Leib

2006

IEEE Vehicular Technology Conference

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This work considers joint channel estimation and data detection of Space-Time Trellis Codes (STTCs) over timevarying flat fading channels with unknown normalized Doppler shift, f d T . An algorithm based on an improved Per Survivor Processing (PSP) technique, where the survivor branch decision is formulated in the symbol by symbol Maximum A Posteriori Probability (MAP) framework, is presented. An Adaptive Kalman Predictor (AKP) is employed to track the fading channel and f d T . In the AKP, the sequence of channel estimates produced by the Kalman Predictor (KP) is utilized for f d T estimation. In turn, the estimated f d T is used in the KP for channel tracking. This coupled process adaptively estimates both the channel coefficients and Doppler shift. The performance of STTC using the proposed receiver is robust against f d T mismatch, and it is within 0.3 dB from the known f d T case.

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Tracking of time-varying channels using two-step LMS-type adaptive algorithm

Kohli

Mehra

2006

IEEE Trans. Signal Process.

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Second-order LMS based wireless channel tracking: implementation under imperfect carrier synchronization

Cited by 4 publications

References 22 publications

Third‐order complex amplitudes tracking loop for slow flat fading channel online estimation

Third‐order complex amplitudes tracking loop for slow flat fading channel online estimation

MAP-PSP for Space-Time Trellis Codes Over Unknown Doppler Channels

Tracking of time-varying channels using two-step LMS-type adaptive algorithm

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