Soft constraint satisfaction multimodulus blind equalization algorithms

Abrar, Shafayat; Zerguine, Azzedine; Deriche, Mohamed

doi:10.1109/lsp.2005.853042

Cited by 17 publications

(16 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed technique was based on the principle of minimum disturbance. From Lin algorithm, a new algorithm termed soft-constraint satisfaction multimodulus algorithm (SCS-MMA) was derived by relaxing the constraints defined by Lin using principle of softconstraint satisfaction (SCS) [14]. The cost function for SCS-MMA is given as…”

Section: Scs-mmamentioning

confidence: 99%

“…There are two major operations involved in time domain equalization detailed in Section 3 above. They are linear correlations in the update equation of (17) and linear convolution embodied by the filtering operation in (14). In this section, we take advantage of DFT processing in implementing these two operations which lead to circular correlation and circular convolution, respectively.…”

Section: Frequency Domain Blind Algorithmsmentioning

confidence: 99%

“…The proposed frequency domain implementation is based on SCS-MMA [14] which was derived by applying the principle of soft-constraint satisfaction to relax the constraints in Lin's cost function [15]. This implementation avoids the use of reference symbols in order to improve the spectral efficiency and throughput.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, FDSCS-MMA convergence is greatly improved by normalization of each of the frequency bin in the weight update. We have used the square root of the spectral power of the equalizer input for our normalization rather than the spectral power considered in [14] as we found that this gives better performance. Specific contributions as presented in this paper include: (1) frequency domain implementation of SCS-MMA, (2) convergence improvement of FDSCS-MMA to realize normalized FDSCS-MMA, (3) adaptation and implementation of NFDSCS-MMA for the equalization of SC-FDMA, (4) reduced overhead and improved bandwidth efficiency compared to channel estimation algorithms, (5) superior phase recovery and intersymbol interference (ISI) optimization capability compared to other popular blind algorithms such as CMA and MMA.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Frequency domain soft-constraint multimodulus blind equalization for uplink SC-FDMA

Akande

Iqbal

Zerguine

et al. 2016

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Single-carrier frequency division multiple access (SC-FDMA) has been adopted and employed as the standard in the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) uplink multiple-access scheme. It offers comparable performance and complexity to orthogonal frequency multiple access scheme (OFDMA) with a lower peak to average power ratio (PAPR) offering power-efficient transmission and longer battery life to mobile terminals. However, due to its single-carrier nature, SC-FDMA performance degrades in channels with long impulse responses and becomes prohibitive to equalize when implemented in time domain (TD). Furthermore, of the seven SC-FDMA symbols in the LTE uplink slot, one full symbol is used for channel estimation leading to about 14 % throughput degradation. In this work, a novel frequency domain soft-constraint satisfaction multimodulus blind algorithm (FDSCS-MMA) is developed and proposed. The frequency domain approach results in computational complexity reduction while blind implementation ensured improved spectral efficiency and throughput. The algorithm convergence is further improved by normalization of each of the frequency bin in the weight update. Simulation results show superior performance of the developed algorithm over other blind algorithms.

show abstract

Section: Scs-mmamentioning

confidence: 99%

Section: Frequency Domain Blind Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Frequency domain soft-constraint multimodulus blind equalization for uplink SC-FDMA

Akande

Iqbal

Zerguine

et al. 2016

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…In [5], the authors relaxed the constraints in Lin's cost function by applying the principle of soft-constraints satisfaction (SCS) [6] to obtain two new algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Newton-like algorithm for adaptive multi-modulus blind equalization

Paracha

Zerguine

2011

International Workshop on Systems, Signal Processing and Their Applications, WOSSPA

View full text Add to dashboard Cite

This work concentrates on the development of a Newton-like algorithm for blind equalization. This algorithm is obtained by applying a complex Taylor series on a recently proposed soft constraint satisfaction multi-modulus blind equalization algorithm of order p (SCS-MMA-p). The proposed algorithm is a generalized version of the SCS-MMA-p. This algorithm is called stochastic Newton-like soft constraint satisfaction multi-modulus algorithm of order p (SNL-SCS-MMA-p).Simulation results carried out, on both baud spaced and fractionally-spaced equalizers, to assess the performance of the prosed algorithm reveal great improvement in performance of the proposed algorithm against that of the SCS-MMA for different values of the parameter p.

show abstract

Robust Constant Modulus Algorithms

2018

Simplified Robust Adaptive Detection and Beamforming for Wireless Communications

View full text Add to dashboard Cite

Soft constraint satisfaction multimodulus blind equalization algorithms

Cited by 17 publications

References 9 publications

Frequency domain soft-constraint multimodulus blind equalization for uplink SC-FDMA

Frequency domain soft-constraint multimodulus blind equalization for uplink SC-FDMA

A Newton-like algorithm for adaptive multi-modulus blind equalization

Robust Constant Modulus Algorithms

Contact Info

Product

Resources

About