Signal estimation for interference cancellation and decision feedback equalization

Frey, Thomas L.; Reinhardt, M.

doi:10.1109/vetec.1997.596338

Cited by 22 publications

(3 citation statements)

References 6 publications

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“…Similar problems of soft-value calculation/soft feedback appear, e.g., in the fields of multiuser detection (successive interference cancellation, a.k.a. decision-feedback equalization (DFE)) [15][16][17].…”

Section: Omp With Soft Feedbackmentioning

confidence: 99%

Soft-feedback OMP for the recovery of discrete-valued sparse signals

Sparrer

Fischer

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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In Compressed Sensing, a real-valued sparse vector has to be reconstructed from an underdetermined system of linear equations. However, in many applications of digital communications the elements of the unknown sparse vector are drawn from a finite set. The standard reconstruction algorithms of Compressed Sensing do not take this knowledge into account, hence, enhanced algorithms are required to achieve optimum performance. In this paper, we propose a new approach for the reconstruction of discrete-valued sparse signals. On the one hand, the algorithm is tailored to the discrete nature of the signal. On the other hand, reliability information is utilized within the successive reconstruction procedure. Via numerical simulations it is shown that the proposed variant of the Orthogonal Matching Pursuit clearly outperforms the wellknown standard versions.

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Section: Omp With Soft Feedbackmentioning

confidence: 99%

Soft-feedback OMP for the recovery of discrete-valued sparse signals

Sparrer

Fischer

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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“…While the unbiasing is state of the art for some estimators such as linear MMSE estimation [2,3], for the so-called soft feedback [4], which is used in many different fields such as successive interference cancellation (SIC), a.k.a. decisionfeedback equalization (DFE) in multiuser detection [5,6,7], unbiasing is generally ignored. In the following, we derive a general rule for the unbiasing of soft feedback, i.e., nonlinear MMSE estimates, and apply it to iterative algorithms in Compressed Sensing.…”

Section: Introductionmentioning

confidence: 99%

Bias Compensation in Iterative Soft-Feedback Algorithms With Applicationto (Discrete) Compressed Sensing

Sparrer

Fischer

2018

2018 IEEE Statistical Signal Processing Workshop (SSP)

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In all applications in digital communications, it is crucial for an estimator to be unbiased. Although so-called soft feedback is widely employed in many different fields of engineering, typically the biased estimate is used. In this paper, we contrast the fundamental unbiasing principles, which can be directly applied whenever soft feedback is required. To this end, the problem is treated from a signal-based perspective, as well as from the approach of estimating the signal based on an estimate of the noise. Numerical results show that when employed in iterative reconstruction algorithms for Compressed Sensing, a gain of 1.2 dB due to proper unbiasing is possible.

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“…Receiver structure of the proposed method in Fig. 2 where f H (·) and f S (·) are the hard and soft decision device [7] for the quadratic phase shift keying(QPSK), respectively,…”

Section: Reduction For the Effect Of Error Propagationmentioning

confidence: 99%

Eigenmode SNR Increasing Method for ML Criterion Based Space-Time Linear Precoder

Narieda

Yamashita

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.

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In this paper, we propose an eigenmode SNR increasing method for a maximum likelihood(ML) criterion based spacetime linear precoder(ML-STLP). In the proposed method, several ML-STLPs are employed for one frame whereas only one ML-STLP is employed in the conventional method. When over two ML-STLPs are employed, the same MLSTLPs, which have short precoded symbol length, are arranged in parallel. Also, a length of precoded symbol for one frame and a data rate are same between the proposed method and the conventional method. We investigate the effect of the precoded symbol length on the resulting eigenmode SNR and show that the eigenmode SNR increasing can be achieved by selecting the precoded symbol length with higher eigenmode SNR depending on the channel condition. Also, the applicability of the proposed method is demonstrated by a computer simulation. INTRODUNCTIONIn wireless communication systems, multi-input multi-output (MIMO) systems, which exploits the spatial diversity, is known as a cost-effective solution for high data rate. In the MIMO systems, the space-time linear precoder and decoder, which is a joint processing at the transmitter and receiver sides and decomposes the MIMO channel to the parallel flat fading subchannels i.e., eigenmode, is introduced to achieve performance improvement[1]- [4].Under the assumption that the channel state information is known, various criteria for space-time linear precoder design have been proposed. In various criteria, reducing the probability of error using maximum likelihood (ML) decision rule based criterion minimizes a bit error rate (BER) by maximizing a minimum distance between symbol hypotheses for any modulation scheme [2]. The potential of the space-time linear precoder based on the criterion (ML criterion based space-time linear precoder, i.e., ML-STLP) is determined by a signal-to-noise ratio (SNR) at the output of the eigenmode, i.e., eigenmode SNR. If obtained eigenvalues are high, in proportion, the eigenmode SNRs and a good bit error rate(BER) performance are achieved. However, when only poor eigenvalues are obtained, also the eigenmode SNRs are low, and it is likely to cause poor BER performance.In this paper, we propose an eigenmode SNR increasing method for ML-STLP. In the proposed method, several MLSTLPs are employed for one frame whereas only one ML-STLP is employed in the conventional method. The number of the employed ML-STLP is determined by a channel condition. When over two ML-STLPs are employed, the same ML-STLPs, which have short precoded symbol length, are arranged in parallel. Also, a length of precoded symbol for one frame and a data rate are same between the proposed method and the conventional method. We investigate the effect of the precoded symbol length on the resulting eigenmode SNR by the interlacing inequalities [5], and show that the eigenmode SNR increasing can be achieved by selecting the precoded symbol length with higher eigenmode SNR depending on the channel condition.In the block transmission system, a guard interval is empl...

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Signal estimation for interference cancellation and decision feedback equalization

Cited by 22 publications

References 6 publications

Soft-feedback OMP for the recovery of discrete-valued sparse signals

Soft-feedback OMP for the recovery of discrete-valued sparse signals

Bias Compensation in Iterative Soft-Feedback Algorithms With Applicationto (Discrete) Compressed Sensing

Eigenmode SNR Increasing Method for ML Criterion Based Space-Time Linear Precoder

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