Estimation of OFDM Time-Varying Fading Channels Based on Two-Cross-Coupled Kalman Filters

Jamoos, Ali; Abdo, Ali; Nour, Hanna Abdel

doi:10.1007/978-1-4020-8737-0_52

Cited by 4 publications

(11 citation statements)

References 15 publications

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“…The Perfect Channel Estimation is obtained by performing LS estimation to the whole received signal. 9 This is practically impossible since it would mean that the receiver already knows the complete signal before decoding. Although this is not practical for communication between sensors, it is implemented to provide as a baseline for all other techniques.…”

Section: Data Based Channel Estimationmentioning

confidence: 99%

“…Preamble based channel estimation is the practical version of the perfect channel estimation and exploits the already known 8 Frequency offset correction and packet frame synchronization are performed in all other techniques as well. 9 Number M that is used in Equation 4 becomes the total number of samples in a packet Fig. 9.…”

Section: Data Based Channel Estimationmentioning

confidence: 99%

“…It is known that a fading wireless channel can be estimated with a Kalman filter as long as the channel is approximated by an Autoregressive (AR) process [9]. For a block fading channel with a p th order AR process, namely AR(p), h k l , the l t h tap of the CIR at the received block () k can be demonstrated as [9] [18]:…”

Section: Appendix Kalman Filter Implementationmentioning

confidence: 99%

“…In literature there exist several methods to estimate AR coefficients such as using the Jakes power spectrum by using the Jakes channel shaping filter [18]. However, Jakes approach requires the estimation of maximum Doppler frequency which is not guaranteed to be a trivial task [9]. In another approach, which is implemented in our work, the AR model coefficients are calculated by utilizing the auto-correlation coefficients of the perfect channel estimates with Yule-Walker equations [9].…”

Section: Appendix Kalman Filter Implementationmentioning

confidence: 99%

“…However, Jakes approach requires the estimation of maximum Doppler frequency which is not guaranteed to be a trivial task [9]. In another approach, which is implemented in our work, the AR model coefficients are calculated by utilizing the auto-correlation coefficients of the perfect channel estimates with Yule-Walker equations [9]. Relation of autocorrelation coefficients of the perfect channel estimate with ϕ p for one tap can be represented in Eqs.…”

Section: Appendix Kalman Filter Implementationmentioning

confidence: 99%

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Veni Vidi Dixi

Ayvasik

Gürsu

Kellerer

2019

Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies

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The upcoming industrial revolution requires deployment of critical wireless sensor networks for automation and monitoring purposes. However, the reliability of the wireless communication is rendered unpredictable by mobile elements in the communication environment such as humans or mobile robots which lead to dynamically changing radio environments. Changes in the wireless channel can be monitored with frequent pilot transmission. However, that would stress the battery life of sensors. In this work a new wireless channel estimation technique, Veni Vidi Dixi, VVD, is proposed. VVD leverages the redundant information in depth images obtained from the surveillance camera(s) in the communication environment and utilizes Convolutional Neural Networks (CNNs) to map the depth images of the communication environment to complex wireless channel estimations. VVD increases the wireless communication reliability without the need for frequent pilot transmission and with no additional complexity on the receiver. The proposed method is tested by conducting measurements in an indoor environment with a single mobile human. Up to authors' best knowledge our work is the first to obtain complex wireless channel estimation from only depth images without any pilot transmission. The collected wireless trace, depth images and codes are publicly available.

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Section: Data Based Channel Estimationmentioning

confidence: 99%

Section: Data Based Channel Estimationmentioning

confidence: 99%

Section: Appendix Kalman Filter Implementationmentioning

confidence: 99%

Section: Appendix Kalman Filter Implementationmentioning

confidence: 99%

Section: Appendix Kalman Filter Implementationmentioning

confidence: 99%

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Veni Vidi Dixi

Ayvasik

Gürsu

Kellerer

2019

Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies

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Estimation of UFMC Time-Varying Fading Channel Using Adaptive Filters

Jamoos¹,

Hussein²

2018

2018 International Conference on Promising Electronic Technologies (ICPET)

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Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

Aldababseh

Jamoos

2014

The Scientific World Journal

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We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.

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Estimation of OFDM Time-Varying Fading Channels Based on Two-Cross-Coupled Kalman Filters

Cited by 4 publications

References 15 publications

Veni Vidi Dixi

Veni Vidi Dixi

Estimation of UFMC Time-Varying Fading Channel Using Adaptive Filters

Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

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