Blind equalization based on pdf fitting and convergence analysis

Fki, Souhaila; Messai, Malek; Bey, Abdeldjalil Aïssa El; Chonavel, Thierry

doi:10.1016/j.sigpro.2014.02.009

Cited by 14 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These figures show that the MSQD-ℓ1 converges close to the MSE of the MMSE equalizer. This is in accordance with [10] where we have proved that the MMSE equalizer is the only stationary stable point of the MSQD-ℓ1 algorithm. In Fig.5 we show the performance of the proposed methods when using the channel of length L h2 = 10 with transfer function H 2 (z) =…”

Section: B Numerical Resultssupporting

confidence: 91%

New criteria for blind equalization based on PDF fitting

Fki

Messai

Bey

et al. 2014

2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Self Cite

View full text Add to dashboard Cite

In this paper, we address M-QAM blind equalization based on information theoretic criteria. We propose two new cost functions that force the probability density functions (pdf) at the equalizer output to match the known constellation pdf. They involve kernel pdf approximation. The kernel bandwidth of a Parzen estimator is updated during iterations to improve the convergence speed and to decrease the residual error of the algorithms. Unlike related existing techniques, the new algorithms measure the distance error between observed and assumed pdfs for the real and imaginary parts of the equalizer output separately. We show performance and complexity gain against the CMA, the most popular blind equalization technique, and classical pdf fitting approaches.

show abstract

Section: B Numerical Resultssupporting

confidence: 91%

New criteria for blind equalization based on PDF fitting

Fki

Messai

Bey

et al. 2014

2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The MSQD-ℓp algorithm [9] aims at minimizing the distance error between observed and assumed pdfs for the real and imaginary parts of the equalizer output. The MSQD-ℓp cost function is given by…”

Section: Msqd-ℓp Gen Equalizermentioning

confidence: 99%

“…The values of µ 2 and µ 1 were set to µ 2 = 4 × 10 −3 and µ 1 = 6 × 10 −3 for MSQD-ℓ2 gen and MSQD-ℓ1 gen respectively after testing the equalizers with a 16-QAM modulation. The kernel bandwidth, σ, of K σ (x) was updated to control the convergence speed of the equalizer and its residual ISI [9]. Since we have no prior information about the emitted constellation, we propose to update the kernel size by…”

Section: Output Constellations With Msqd-ℓp Gen (P=12)mentioning

confidence: 99%

“…In [8], the CMA was used as a generic equalizer with radius equal to 1 and the amplitude of the equalized signal Characteristic Function (CF) as a technique to recognize the transmitted modulation. In this paper, we propose to use a generic MSQD-ℓp equalizer that is more efficient than the CMA [9]. Once the signal is equalized, we identify the transmitted constellation via an ML approach or pdf distance based methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blind equalization and Automatic Modulation Classification based on pdf fitting

Fki

Bey

Chonavel

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

In this paper, a completely blind equalizer based on probability density function (pdf) fitting is proposed. It doesn't require any prior information about the transmission channel or the emitted constellation. We also investigate Automatic Modulation Classification (AMC) for Quadrature Amplitude Modulation (QAM) based on the pdf of the equalized signal. We propose three new approaches for AMC. The first employs maximum likelihood functions (ML) of the modulus of real and imaginary parts of the equalized signal. The second is based on the lowest quadratic or Bhattacharyya distance between the estimated pdf of the real and imaginary parts of the equalizer output and the theoretical pdfs of M-QAM modulations. The third approach is based on theoretical pdf dictionnary learning. The performance of the identification scheme is investigated through simulations.

show abstract

“…However, due to poor physical link quality, high latency, constant movement of waves and chemical properties of water, the underwater acoustic channel is considered as one of the most adverse communication mediums in use today [3][4][5]. These adverse properties of the underwater acoustic channel should be equalized by in order to provide reliable communication [2,3,[6][7][8][9][10][11][12][13]. Furthermore, due to rapidly changing and unpredictable nature of underwater environment, constant movement of waves and transmitter-receivers, such processing should be adaptive [2,7,9,11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive and efficient nonlinear channel equalization for underwater acoustic communication

Kari

Vanli

Kozat

2017

Physical Communication

View full text Add to dashboard Cite

We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear channel equalization algorithms that are highly efficient and provide significantly improved bit error rate (BER) performance. Due to the high complexity of nonlinear equalizers and poor performance of linear ones, to equalize highly difficult underwater acoustic channels, we employ piecewise linear equalizers. However, in order to achieve the performance of the best piecewise linear model, we use a tree structure to hierarchically partition the space of the received signal. Furthermore, the equalization algorithm should be completely adaptive, since due to the highly non-stationary nature of the underwater medium, the optimal MSE equalizer as well as the best piecewise linear equalizer changes in time. To this end, we introduce an adaptive piecewise linear equalization algorithm that not only adapts the linear equalizer at each region but also learns the complete hierarchical structure with a computational complexity only polynomial in the number of nodes of the tree. Furthermore, our algorithm is constructed to directly minimize the final squared error without introducing any ad-hoc parameters. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.

show abstract

Blind equalization based on pdf fitting and convergence analysis

Cited by 14 publications

References 14 publications

New criteria for blind equalization based on PDF fitting

New criteria for blind equalization based on PDF fitting

Blind equalization and Automatic Modulation Classification based on pdf fitting

Adaptive and efficient nonlinear channel equalization for underwater acoustic communication

Contact Info

Product

Resources

About