Ultra wideband OFDM channel estimation through a wavelet based EM‐MAP algorithm

Sadough, Seyed Mohammad Sajad; Ichir, Mahieddine; Duhamel, Pierre; Jaffrot, E.

doi:10.1002/ett.1324

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…A hidden semi Markov model is an extension of hidden Markov model using the semi Markov chain process with parameters such as variable duration or sojourn time for each state. In HSMM number of observation for each state is high than the number of observations present in the HMM (Sadough and Jaffrot, 2005;Sadough et al, 2009). This is the main dissimilarity between the HMM and HSMM.…”

Section: Methodology Hidden Semi Markov Modelmentioning

confidence: 99%

“…The HSMM based on HDP denoted by = { , , H, ˙{ where is an initial probability, a vector of mean observed signal strengths , a transition matrix H, a vector of observed signal strength variances ˙. The parameter estimation of hidden semi Markov model is more complicated than the hidden Markov model (Sadough, 2008). The EM algorithm is used for calculating the unknown parameters of the hidden semi Markov model from real data in the maximum likelihood sense.…”

Section: Parameter Estimationmentioning

confidence: 99%

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An Efficient Channel Sensing Algorithm Based on Hidden Semi Markov Model and Channel Quality Predicition

Kumar¹,

Srivatsa²

2014

RJASET

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In recent years, Cognitive radio has become one of the most important emerging technologies to handle the primary user channel utilization in next generation cellular networks. The major issues in the future generation cellular networks are channel sensing and allocation for secondary user. Several optimization algorithms have been proposed in the literature for sensing the channel in future generation networks. Although, the existing algorithms provide good results, it has certain limitations such as high computational complexity in real time implementation. In order to overcome the limitation in existing algorithms and to obtain the efficient results, this study proposed a probability based channel sensing algorithm. Hidden Markov model is used as the probability calculation of primary user state and the predicted channel is validated using the proposed quality estimation method. The estimated channel is predicted using the probability of detection and probability of false alarm is used for validating the algorithms. The performance metrics used to evaluate the proposed algorithm is mean square error value and the channel is estimated using different estimators. The comparison of the proposed algorithm with the existing algorithms and performance of the proposed algorithm is better than the other algorithms.

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Section: Methodology Hidden Semi Markov Modelmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

An Efficient Channel Sensing Algorithm Based on Hidden Semi Markov Model and Channel Quality Predicition

Kumar¹,

Srivatsa²

2014

RJASET

View full text Add to dashboard Cite

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“…A semi-blind joint channel estimation and data detection scheme based on EM algorithm, with the objective of minimizing the number of estimated parameters and enhancing the estimation accuracy is considered in [29,30]. This is achieved by expressing the unknown CIR in terms of its discrete wavelet series which has been shown to provide a parsimonious representation.…”

Section: Wavelet-based Channel Estimation (Wce)mentioning

confidence: 99%

A Comprehensive Study of Channel Estimation for WBAN-based Healthcare Systems: Feasibility of Using Multiband UWB

Islam

Kwak

2010

J Med Syst

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Wireless personal area network (WPAN) is an emerging in wireless technology for short range indoor and outdoor communication applications. A more specific category of WPAN is the wireless body area network (WBAN) used for health monitoring. On the other hand, multiband orthogonal frequency division multiplexing (MB-OFDM) ultra-wideband (UWB) comes with a number of desirable features at the physical layer for wireless communications, for example, very high data rate. One big challenge in adoption of multiband UWB in WBAN is the fact that channel estimation becomes difficult under the constraint of extremely low transmission power. Moreover, the heterogeneous environment of WBAN causes a dense multipath wireless channel. Therefore, effective channel estimation is required in the receiver of WBAN-based healthcare system that uses multiband UWB. In this paper, we first outline the MB-OFDM UWB system. Then, we present an overview of channel estimation techniques proposed/investigated for multiband UWB communications with emphasis on their strengths and weaknesses. Useful suggestions are given to overcome the weaknesses so that these methods can be particularly useful for WBAN channels. Also, we analyze the comparative performances of the techniques using computer simulation in order to find the energy-efficient channel estimation methods for WBAN-based healthcare systems.

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“…The HSMM based on HDP denoted by = ( , , , ) where is an initial probability, a vector of mean observed signal strengths , a transition matrix , a vector of observed signal strength variances . The parameter estimation of hidden semi markov model is more complicated than the hidden markov model (Sadough, 2008). The EM algorithm is used for calculating the unknown parameters of the hidden semi markov model from real data in the maximum likelihood sense.…”

Section: Parameter Estimationmentioning

confidence: 99%

Opportunistic Channel Access Algorithm Based on Hidden Semi Markov Model for Cognitive Radio Networks

Kumar¹,

Srivatsa²

2014

BIJRCE

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Future generation cellular networks highly depend on Cognitive radio due to its capability to handle the primary user channel utilization. However, channel sensing and allocation for secondary user are the major factors to be considered. Several research works have been carried out to deal with the sensing problem. But, most of the existing algorithms results in high computational complexity in real time implementation. In recent years, Hidden Markov Model(HMM) has been considered as a powerful statistical tool for modeling generative sequences that can be characterised by an underlying process generating an observable sequence algorithms. Thus, based on the motivation of the earlier works carried out in HMM, this paper proposed a probability based channel sensing algorithm. Hidden markov model is used for probability calculation of primary user state and the predicted channel is validated using the proposed quality estimation method. The performance metrics used to evaluate the proposed algorithm is linear mean square error value and the channel is estimated using different estimators. The comparison of the proposed algorithm with the existing algorithms and performance of the proposed algorithm is better than the other algorithms.

show abstract

Ultra wideband OFDM channel estimation through a wavelet based EM‐MAP algorithm

Cited by 7 publications

References 17 publications

An Efficient Channel Sensing Algorithm Based on Hidden Semi Markov Model and Channel Quality Predicition

An Efficient Channel Sensing Algorithm Based on Hidden Semi Markov Model and Channel Quality Predicition

A Comprehensive Study of Channel Estimation for WBAN-based Healthcare Systems: Feasibility of Using Multiband UWB

Opportunistic Channel Access Algorithm Based on Hidden Semi Markov Model for Cognitive Radio Networks

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