Eigenvector algorithm for blind MA system identification

Boss, D.; Jelonnek, B.; Kammeyer, Karl‐Dirk

doi:10.1016/s0165-1684(97)00237-5

Cited by 39 publications

(10 citation statements)

References 34 publications

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“…The modulation we have used is BPSK. For our method we have selected ψ = E z 4 i (k) using 55 FIR filters in "Span Mode" and comparison was made with the EVI [9], [10] method.…”

Section: Simulations and Resultsmentioning

confidence: 99%

A reliable method for blind channel identification using burst data

Raphaeli

Suissa

2003

Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.

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Abstract-In this paper we present a new approach for blind identification of Single Input Single Output (SISO) and Multiple Inputs Single Output (MISO) FIR channels with nonminimum phase. The approach is based on minimizing a cost function built of the problem unknown parameters and a vector of measurements achieved by passing the received data through a known parallel set of FIR filters followed by samples averaging. Averaging is done according to certain functions that have Higher Order Statistics (HOS) content and that their assymptotical mean can be expressed in closed form. The main advantages of this approach are its high probability of identification success when considering statistical channels, its ability to obtain reliable channel estimates in low SNR using short records of samples and its unsensitivity to overestimation of the channel order.

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“…The modulation we have used is BPSK. For our method we have selected ψ = E z 4 i (k) using 55 FIR filters in "Span Mode" and comparison was made with the EVI [9], [10] method.…”

Section: Simulations and Resultsmentioning

confidence: 99%

A reliable method for blind channel identification using burst data

Raphaeli

Suissa

2003

Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.

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“…Since this knowledge usually is not available, the problem of channel estimation arises. From the point of view of systems theory, channel estimation is a particular form of (linear) system identification which, in our case, is complicated by three main properties of the radio channel: (i) it consists of multiple propagation paths and is therefore frequency selective, (ii) its discrete-time equivalent baseband impulse response may be mixed phase and (iii) in a mobile environment, it is time-variant [11]. Because of the movement of the mobile station and, hence, the changing channel characteristics, the channel has to be treated as a time-variant, multipath fading channel.…”

Section: Equalization Of Wide-sense Stationary Uncorrelated Scatterinmentioning

confidence: 99%

Performance of multilevel‐turbo codes with blind/non‐blind equalization over WSSUS multipath channels

Uçan

Buyukatak

Gose

et al. 2005

Int J Communication

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SUMMARYIn this paper, in order to improve error performance, we introduce a new type of turbo codes, called 'multilevel-turbo codes (ML-TC)' and we evaluate their performance over wide-sense stationary uncorrelated scattering (WSSUS) multipath channels. The basic idea of ML-TC scheme is to partition a signal set into several levels and to encode each level separately by a proper component of the turbo encoder. In the considered structure, the parallel input data sequences are encoded by our multilevel scheme and mapped to any modulation type such as MPSK, MQAM, etc. Since WSSUS channels are very severe fading environments, it is needed to pass the received noisy signals through non-blind or blind equalizers before turbo decoders. In ML-TC schemes, noisy WSSUS corrupted signal sequence is first processed in equalizer block, then fed into the first level of turbo decoder and the first sequence is estimated from this first Turbo decoder. Subsequently, the other following input sequences of the frame are computed by using the estimated input bit streams of previous levels. Here, as a ML-TC example, 4PSK 2 level-turbo codes (2L-TC) is chosen and its error performance is evaluated in WSSUS channel modelled by COST 207 (Cooperation in the field of Science & Technology, Project #207). It is shown that 2L-TC signals with equalizer blocks exhibit considerable performance gains even at lower SNR values compared to 8PSK-turbo trellis coded modulation (TTCM). The simulation results of the proposed scheme have up to 5.5 dB coding gain compared to 8PSK-TTCM for all cases. It is interesting that after a constant SNR value, 2L-TC with blind equalizer has better error performance than non-blind filtered schemes. We conclude that our proposed scheme has promising results compared to classical schemes for all SNR values in WSSUS channels.

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“…In [1], the authors have demonstrated that it is possible with an eigenvector algorithm based on higher order statistics (HOS), referred to as EVI, to derive good channel estimates from fourth order cumulants estimated on the basis of only few samples of the received signal. In feasibility studies based on the global system for mobile communications (GSM), we have shown that the blind EVI algorithm can even compete with conventional second order methods based on training sequences [2].…”

Section: Introductionmentioning

confidence: 99%

Blind turbo channel estimation in OFDM receivers

Petermann

Vogeler

Kammeyer

et al. 2001

Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256)

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Finite-Alphabet-based blind channel estimation in OFDM systems is known to be extremely complex due to an exhaustive search to be performed over a tremendous number of channel coefficient combinations. In this paper, we present a novel blind channel estimator which dramatically reduces this number of coefficient combinations to be checked without a significant deterioration in estimation quality. Hence, the new low complexity approach enables the application of blind channel estimators based on the finite alphabet set even if the transmitted data are high-rate modulated. Furthermore, we show that the performance of blind channel estimation can be improved by an iterative process based upon the capabilities of channel coding. Using bit error rates, the algorithm is tested with simulations and compared to other blind and nonblind channel estimators.

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Eigenvector algorithm for blind MA system identification

Cited by 39 publications

References 34 publications

A reliable method for blind channel identification using burst data

A reliable method for blind channel identification using burst data

Performance of multilevel‐turbo codes with blind/non‐blind equalization over WSSUS multipath channels

Blind turbo channel estimation in OFDM receivers

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