Adaptive Channel Estimation and Equalization for Rapidly Mobile Communication Channels

El-Mahdy, Ahmed

doi:10.1109/tcomm.2004.831391

Cited by 25 publications

(17 citation statements)

References 24 publications

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“…DFE and trellis methods for both polynomial and Karhunen-Loève BEMs [BH99b] were studied by Borah and Hart in [BH99b] for doubly selective channels. Trellis-based PSP using a complex-exponential BEM [TG96] was discussed by El-Mahdy [EM04].…”

Section: Reduced-complexity Trellis-psp Equalization For Single-carrimentioning

confidence: 99%

Equalization of Time-Varying Channels

Schniter

Hwang

Das

et al. 2011

Wireless Communications Over Rapidly Time-Varying Channels

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Section: Reduced-complexity Trellis-psp Equalization For Single-carrimentioning

confidence: 99%

Equalization of Time-Varying Channels

Schniter

Hwang

Das

et al. 2011

Wireless Communications Over Rapidly Time-Varying Channels

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“…In this paper, such rapidly fading channels are considered, specifically, the channel model described in [9,10,11,12] is used. In this model, the time varying channel taps are modeled by a finite linear combination of complex exponentials.…”

Section: Introductionmentioning

confidence: 99%

“…In this model, the time varying channel taps are modeled by a finite linear combination of complex exponentials. The basis expansion approach of [9,11,12] is used in this paper and the time varying channel coefficients are expanded into a set of basis sequences and expansion parameters. These basis sequences are assumed to be known while the expansion parameters are unknown and needs to be identified.…”

Section: Introductionmentioning

confidence: 99%

Recursive Detection of M-ary Signals over Fast Varying Mobile Communication Channel

El-Mahdy¹

2013

TELKOMNIKA

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Abstrak IntroductionMultipath fading has a major effect on the performance of mobile communication systems. The time varying nature of the mobile wireless channel causes a limitation in the performance of the designed for non-fading channel even at high signal to noise ratio. Then, the development of mobile cellular resulted in increasing interest in the study of signal detection in the presence of this rapidly time varying environment. If the channel exhibits intersymbol interference (ISI) in the received data, adaptive equalizers [1][2][3], that use adaptive algorithms [4], are used to recover the received signal and to remove the ISI. When the channel is rapidly varying, tracking these variations becomes a challenging problem and hence the equalizer may not be able to track the channel.Several articles discuss the problem of detection of M-ary signals [5][6][7][8]. In [5] the authors derived an exact expression for the symbol error probability (SEP) for coherent detection of M-ary PSK signals using array of antennas with optimum combining. They consider a Rayleigh flat fading channel. In [6], a low complexity breadth first tree detector, termed improved M-algorithm (IMA) is proposed. The authors showed that IMA works well without an energy compacting front end prefilter (FEP) even in frequency-selective channels. Moreover, the authors propose to make use of the cepstrum to compute the FEP via a minimum phase target impulse response. In [7], the effect of power and rate adaptation on the spectral efficiency of orthogonal frequency division multiplexing (OFDM) systems using M-ary quadrature

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“…Hence, we use it as a baseline for comparison. The estimation of BEM coefficients, in place of the time-varying impulse response, was proposed in [9] in the context of PSP-VA. But BEM estimation does not appear to work well in conjunction with the path-pruning of PSP-VA; our experiments, as well as those in [9], show an early error floor.…”

Section: Introductionmentioning

confidence: 99%

“…The estimation of BEM coefficients, in place of the time-varying impulse response, was proposed in [9] in the context of PSP-VA. But BEM estimation does not appear to work well in conjunction with the path-pruning of PSP-VA; our experiments, as well as those in [9], show an early error floor. Though the suggestion to use generic tree-search algorithms for joint channel/data estimation can be found, e.g., in [3], we are not aware of existing strategies that are particularly well-suited to the doubly dispersive environment.…”

Section: Introductionmentioning

confidence: 99%

Near-Optimal Noncoherent Sequence Detection for Doubly Dispersive Channels

Hwang

Schniter

2006

2006 Fortieth Asilomar Conference on Signals, Systems and Computers

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Abstract-We propose a scheme for near-optimal sequence detection (SD) of uncoded block transmissions over unknown doubly dispersive (DD) channels. Starting with a noncoherent maximum likelihood (ML) metric that leverages a basis expansion model (BEM) for the channel's time-variation, we propose an efficient noncoherent SD strategy based on suboptimal tree search with a fast metric update. Our scheme yields performance within a fraction-of-a-dB from ML sequence detection with genieaided channel estimates, and maintains complexity that is only quadratic in the block length.

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Adaptive Channel Estimation and Equalization for Rapidly Mobile Communication Channels

Cited by 25 publications

References 24 publications

Equalization of Time-Varying Channels

Equalization of Time-Varying Channels

Recursive Detection of M-ary Signals over Fast Varying Mobile Communication Channel

Near-Optimal Noncoherent Sequence Detection for Doubly Dispersive Channels

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