Modeling Fading Channels With Binary Erasure Finite-State Markov Channels

Moreira, Igor; Pimentel, Cecílio; Barros, Felipe P.; Chaves, Daniel P. B.

doi:10.1109/tvt.2016.2597743

Cited by 19 publications

(11 citation statements)

References 21 publications

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“…where • represents the Hadamard product, and the Doppler shift pre-compensation vector P (n) in (12), as shown at the bottom of this page,…”

Section: A Orthogonal Spatial Domain Subspace Projectionmentioning

confidence: 99%

“…And the channel states are defined based on the signal to noise ratios (SNRs) via the equal stepsize partitioning method. In [12], the ternary Markov channel (TMC) was proposed to characterize the ternary discrete channels with memory and soft-information, which generalizes the Gilbert-Elliott channel (GEC) in the sense that each binary symmetric channel (BSC) is replaced by a discrete memoryless channel with binary input and ternary output. The basis expansion model (BEM) is widely used to characterize the time-varying channel recently by utilizing the basis functions scaled with corresponding coefficients.…”

Section: Introductionmentioning

confidence: 99%

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Orthogonal Spatial Domain Subspace Projection Based Time-Varying Channel Estimation

2020

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Time-varying channel estimation is a well-known challenge for the wireless communications in the high mobility environments. In this paper, we propose a basis expansion model (BEM) based channel estimation algorithm utilizing orthogonal spatial domain subspace projection. The channel estimation is taken on each subspace and the estimation results of all subspaces are combined to obtain the final timevarying channel parameters. In our proposed scheme, the wave number spectrum shifting is utilized to suppress the time-varying level on each subspace and the wave number spectrum recovery is adopted in the combination process. Simulation results are provided to verify our proposed scheme. INDEX TERMS Time-varying channel estimation, high mobility environments, orthogonal spatial domain subspace projection.

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“…where • represents the Hadamard product, and the Doppler shift pre-compensation vector P (n) in (12), as shown at the bottom of this page,…”

Section: A Orthogonal Spatial Domain Subspace Projectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Orthogonal Spatial Domain Subspace Projection Based Time-Varying Channel Estimation

2020

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“…The Gilbert‐Elliott model has been widely used in characterizing burst‐error in wireless communication channel, where the condition of the wireless communication channel is characterized by a Markov chain with two modes: “Good” and “Bad.” In the “Good” mode, the channel is free of packet dropouts, while, in the “Bad” mode, the channel may be subjected to packet dropouts with a given probability. Compared with the traditional Bernoulli‐type packet dropout model, the Gilbert‐Elliott model is clearly more realistic since the packet dropout probability is not required to be a constant over different time periods.…”

Section: Introductionmentioning

confidence: 99%

Nonfragile H_∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models

Shen

Wang

Shen

et al. 2020

Intl J Robust & Nonlinear

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In this article, the nonfragile H ∞ filtering problem is investigated for a class of discrete multirate time-delayed systems over sensor networks. The probabilistic packet dropout occurs during the information transmissions among the sensor nodes in the sensor network characterized by the Gilbert-Elliott model. In order to take the multirate sampling into account, the state updating period of the system and the sampling period of the sensors are allowed to be different. The variation of the filter gain is considered to reflect the physical errors with the filter implementation. The aim of this article is to design a set of nonfragile filters such that, in the presence of multirate sampling, time-delays, and packet dropouts, the filtering error dynamics is exponentially mean-square stable and also satisfies the H ∞ performance requirement. By using the Lyapunov-Krasovskii functional approach, a sufficient condition is derived, which ensures the exponential mean-square stability and the H ∞ performance requirement of the filtering error dynamics. Then, the filter gains are characterized in terms of the solution to a set of matrix inequalities. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed filtering scheme. K E Y W O R D SGilbert-Elliott model, multirate sampling, nonfragile H ∞ filtering, packet dropouts, sensor networks, time-delays

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“…Furthermore, the application in deep neural networks can be found [18, 19, 20], where the precision of weights has been constrained to ternary values. Moreover, the use of soft‐decision with a three‐level quantiser in the finite‐state Markov channels has been considered in [21].…”

Section: Introductionmentioning

confidence: 99%

Gaussian source coding based on variance‐mismatched three‐level scalar quantisation using Q ‐function approximations

2020

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Modeling Fading Channels With Binary Erasure Finite-State Markov Channels

Cited by 19 publications

References 21 publications

Orthogonal Spatial Domain Subspace Projection Based Time-Varying Channel Estimation

Orthogonal Spatial Domain Subspace Projection Based Time-Varying Channel Estimation

Nonfragile H_∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models

Gaussian source coding based on variance‐mismatched three‐level scalar quantisation using Q ‐function approximations

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Modeling Fading Channels With Binary Erasure Finite-State Markov Channels

Cited by 19 publications

References 21 publications

Orthogonal Spatial Domain Subspace Projection Based Time-Varying Channel Estimation

Orthogonal Spatial Domain Subspace Projection Based Time-Varying Channel Estimation

Nonfragile H∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models

Gaussian source coding based on variance‐mismatched three‐level scalar quantisation using Q ‐function approximations

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Nonfragile H_∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models