Blind channel estimation and data detection using hidden Markov models

Antón-Haro, Carles; Fonollosa, José A. R.; Fonollosa, J.R.

doi:10.1109/78.552223

Cited by 47 publications

(37 citation statements)

References 12 publications

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“…Before proceeding with this somewhat involved modeling paradigm, it is important to explore other Markov-based techniques which might limit the overall model complexity while yielding appropriate performance. One such technique, which has been successfully applied to other fading channels, is hidden Markov modeling [10], [11], [12]. In the following section we develop a suitable HMM for the 802.11b bit error process.…”

Section: Discussionmentioning

confidence: 99%

“…Let the current state, in a process with memory length of 4-bits, be (0110) 2 = (6) 10 . As the window slides, the 0 in the most significant bit position will be dropped and a bit will be added to the least significant bit position, that is, the chain can transit to either (1100) 2 = (12) 10 or (1101) 2 = (13) 10 . Thus from any current state, the process can jump to only two possible next states (current state inclusive).…”

Section: Performance Evaluation Of the Full-state Markov Chainmentioning

confidence: 99%

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Markov-based modeling of wireless local area networks

Khayam

Radha

2003

Proceedings of the 8th International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems - MSWIM '03

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Errors introduced by a wireless medium are more frequent and profound than contemporary wired media. Some of these errors, which are not corrected by the physical layer, result in Medium Access Control (MAC) layer bit errors and packet losses. Design of wireless protocols and applications can benefit substantially from a thorough understanding of these MAC layer impairments. This paper evaluates and proposes Markov-based stochastic chains to model the 802.11b MAC-to-MAC channel behavior for both bit errors and packet losses. We introduce an Entropy Normalized Kullback-Leibler measure to evaluate the performance of existing and new bit error and packet loss models. Based on the proposed measure, and contrary to recent results for mobile networks, we demonstrate that the traditional two-state Markov chain provides a very suitable model for the 802.11b MAC-to-MAC packet loss process. However, this simple model is not adequate for bit errors observed at the MAC layer of wireless local area networks. Consequently, we evaluate three other Markov-based chains for modeling these errors: full-state, hidden, and hierarchical Markov chains. Among these chains, we illustrate that the fullstate Markov bit error model, evaluated under a wide range of order values, renders the best performance.

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Section: Discussionmentioning

confidence: 99%

Section: Performance Evaluation Of the Full-state Markov Chainmentioning

confidence: 99%

Markov-based modeling of wireless local area networks

Khayam

Radha

2003

Proceedings of the 8th International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems - MSWIM '03

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“…Anton-Haro, Fonollosa and Fonollosa [AHFF97] proposed a channel EM algorithm for doubly selective channels that used a polynomial BEM, assuming CM signaling. More recently, Yan and Rao [YR03] proposed a channel EMB method for AR-1 time-selective channels and CM signaling that employs a Kalman filter.…”

Section: Iterative Noncoherent Equalization For Single-carrier Schemesmentioning

confidence: 99%

Equalization of Time-Varying Channels

Schniter

Hwang

Das

et al. 2011

Wireless Communications Over Rapidly Time-Varying Channels

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“…The first use of EM with soft symbol estimates (as produced by BCJR) was proposed in [2]. It is a direct application of EM (as are [3] and [4]), and hence is guaranteed to converge to a local maximum of the joint channel-sequence likelihood function. An adaptive version [5] of EM was applied to the identification problem in [6].…”

Section: Introductionmentioning

confidence: 99%

“…1 [1][2][3][4][5][6][7][8][9]. In these algorithms, an initial channel estimate is used by a symbol estimator to provide tentative soft estimates of the transmitted symbol sequence.…”

Section: Introductionmentioning

confidence: 99%

Blind iterative channel identification and equalization

Lopes

Barry²

ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240)

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-We propose an iterative solution to the problem of blindly and jointly identifying the channel response and transmitted symbols in a digital communications system. The proposed algorithm iterates between a symbol estimator, which uses tentative channel estimates to provide soft symbol estimates, and a channel estimator, which uses the symbol estimates to improve the channel estimates. The proposed algorithm shares some similarities with the Expectation-Maximization (EM) algorithm but with lower complexity and better convergence properties. Specifically, the complexity of the proposed scheme is linear in the memory of the equalizer, and it avoids most of the local maxima that trap the EM algorithm.

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Blind channel estimation and data detection using hidden Markov models

Cited by 47 publications

References 12 publications

Markov-based modeling of wireless local area networks

Markov-based modeling of wireless local area networks

Equalization of Time-Varying Channels

Blind iterative channel identification and equalization

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