The Expectation-Maximization Viterbi Algorithm for Blind Adaptive Channel Equalization

Nguyen, Hoang; Levy, Bernard C.

doi:10.1109/tcomm.2005.857162

Cited by 19 publications

(6 citation statements)

References 20 publications

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“…In the simulations, the input bit sequence of length 512 (510 information bits and 2 tail bits set to 0 to end the trellis of the decoder) is encoded using the rate r c = 1/2 convolutional code with 4 states and generator polynomials (7,5). In Figure 5, the curves with solid lines show the BER obtained from one to five iterations of the turbo-detector, with respect to SNR in dB for T 0 = 10, when the TS is chosen according to [10] (optimum TS in terms of signal to estimation error ratio).…”

Section: Coded Transmissionmentioning

confidence: 99%

“…When the channel and the transmitted symbols are unknown, two applications of the EM algorithm can be considered. Indeed, it can be used to iteratively estimate the channel [4,5] or to iteratively estimate the transmitted symbols [6,7,8]. Here, we propose a new approach using the EM algorithm to give an approximate solution to the intractable problem introduced in [1].…”

Section: Introductionmentioning

confidence: 99%

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MAP sequence equalization for imperfect frequency selective channel knowledge

Sellami¹,

Siala

Roumy

et al. 2010

Trans Emerging Tel Tech

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This paper considers the problem of Maximum a Posteriori (MAP) sequence equalization over a frequency selective channel when the channel is not perfectly known at the receiver. The imperfect channel estimate is provided by a least squares estimator using a training sequence (TS). The derivation of the MAP criterion taking into account the channel estimation error exists in the literature but no direct practical implementation has been proposed. In this paper, we propose an approximate solution to the problem by using an iterative burst-by-burst equalizer based on the Expectation Maximization (EM) algorithm. Simulation results show that the performance of our iterative equalizer is significantly better than the performance of the non iterative equalizer which does not take into account the channel estimation error.

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Section: Coded Transmissionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MAP sequence equalization for imperfect frequency selective channel knowledge

Sellami¹,

Siala

Roumy

et al. 2010

Trans Emerging Tel Tech

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“…In order to improve spectral efficiency, considerable research effort has been devoted to the application of blind equalization techniques in wireless communication systems in recent decades [2][3][4][5][6][7][8]. A popular approach to blind equalization is to jointly estimate the unknown *Correspondence: jnelson@gmu.edu 2 Department of Electrical and Computer Engineering, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA Full list of author information is available at the end of the article channel and transmitted data based on the maximum likelihood (ML) criterion [9].…”

Section: Introductionmentioning

confidence: 99%

Blind sequential detection for sparse ISI channels

Zhou

Nelson

2018

EURASIP J. Adv. Signal Process.

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We present a computationally efficient blind sequential detection method for data transmitted over a sparse intersymbol interference channel. Unlike blind sequential detection methods designed for general channels, the proposed method exploits the channel sparsity by using estimated channel sparsity to assist in the detection of the transmitted sequence. A Gaussian mixture model is used to describe sparse channels, and two tree-search strategies are applied to estimate the channel sparsity and the transmitted sequence, respectively. To demonstrate the performance improvement achieved by the proposed blind detector, we compare it to conventional joint channel and sequence detection methods that use sparse channel estimation techniques. Simulation results show that the proposed detector not only reduces computational complexity compared to existing methods but also provides superior performance, particularly when the signal to noise ratio is low.

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“…The performance of the said technique is investigated through simulation of a realistic MIMO-OFDM scenario, modelled with the aid of Stanford University Interim (SUI) model. The motivation for this work comes from [39] and [40]. In [40], the authors replace the expectation step of conventional EM by an MMSE estimate, and show that the technique when applied independently over each subcarrier of a SISO-OFDM system, performs almost identical to EM as well as the case of ideal CSI, and involves lower computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Simplified Semi-Blind Channel Estimation for Space–Time Coded MIMO-OFDM Systems

Gupta

Mehra

2010

Wireless Pers Commun

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The combination of Space-Time Coded Multiple Input Multiple Output systems (STC-MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is recently being investigated as an effective means of providing high-speed data transmission over dispersive wireless channels. The strengths of the two techniques coalesce and render MIMO-OFDM systems robust to ISI and IBI. However, the decoding and demodulation of STC-OFDM needs reliable channel knowledge at the receiver, unless differential modulation techniques are used. Semi-blind methods for channel estimation are seen to provide the best trade-off in terms of bandwidth overhead, computational complexity and latency. The conventional Expectation-Maximization (EM) algorithm for semi-blind channel estimation improves a pilot-based estimate with a two step process; however, it is computationally complex to implement. In this paper, we propose a variant of the EM method, referred to as ML-EM, for semi-blind estimation of doubly dispersive channels in space-time coded MIMO-OFDM systems. Here, the conventional EM algorithm is coupled with the ML decoder for space time block codes (STBCs). The technique shows good performance, even in highly correlated antenna arrays, and is computationally simpler than conventional EM. The method incurs a training overhead of less than 1%, and performs close to exact CSI through iterative processing at the receiver.

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The Expectation-Maximization Viterbi Algorithm for Blind Adaptive Channel Equalization

Cited by 19 publications

References 20 publications

MAP sequence equalization for imperfect frequency selective channel knowledge

MAP sequence equalization for imperfect frequency selective channel knowledge

Blind sequential detection for sparse ISI channels

Simplified Semi-Blind Channel Estimation for Space–Time Coded MIMO-OFDM Systems

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