On the robustness of the fractionally-spaced constant modulus criterion to channel order undermodeling .II

Endres, T.J.; Anderson, Brian D. O.; Johnson, C.R.; Green, M.

doi:10.1109/icassp.1997.604646

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…As the only difference between the RCM and RDDM criteria is the reference radius R, if we consider the derivative ofâ(n) with respect to w equal to zero, the RDDMA can be developed in the same way as the RCMA. Thus, the RDDMA is described by the same equations (27)(28)(29), with e 2 (n) given in Table 3. For PSK modulations, the RDDMA is identical to the RCMA.…”

Section: A Family Of Rls Algorithmsmentioning

confidence: 98%

“…In a multichannel system, it is also possible, under certain conditions, to choose the length of the equalizer in such a way that the nullspace of H is trivial [21]. Other related works about the convergence of adaptive blind algorithms can be found in [22][23][24][25][26][27][28].…”

Section: Additional Comments On Convergencementioning

confidence: 99%

“…That leads to the recursive modified constant modulus algorithm (RMCMA), the cost function of which is given in Table 3. The RMCMA adaptation equations are proved in Appendix C. They are summarized by (27)(28)(29), with e 2 (n) given in Table 3. As it will be illustrated by computer simulations, the RMCMA provides very good performances, notably improving the convergence speed of the original CMA.…”

Section: A Family Of Rls Algorithmsmentioning

confidence: 99%

“…That leads to the recursive modified decision directed modulus algorithm (RMDDMA), the cost function of which is also given in Table 3. This algorithm is described by (27)(28)(29), with e 2 (n) given in Table 3. It can be viewed as a generalization of the RMCMA with variable reference radii.…”

Section: A Family Of Rls Algorithmsmentioning

confidence: 99%

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Decision directed adaptive blind equalization based on the constant modulus algorithm

Fernandes

Favier

Mota

2007

SIViP

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International audienceIn this paper, new decision directed algorithms for blind equalization of communication channels are presented. These algorithms use informations about the last decided symbol to improve the performance of the constant modulus algorithm (CMA). The main proposed technique, the so called decision directed modulus algorithm (DDMA), extends the CMA to non-CM modulations. Assuming correct decisions, it is proved that the decision directed modulus (DDM) cost function has no local minima in the combined channel-equalizer system impulse response. Additionally, a relationship between the Wiener and DDM minima is established. The other proposed algorithms can be viewed as modifications of the DDMA. They are divided into two families: stochastic gradient algorithms and recursive least squares (RLS) algorithms. Simulation results allow to compare the performance of the proposed algorithms and to conclude that they outperform well-known methods

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Section: A Family Of Rls Algorithmsmentioning

confidence: 98%

Section: Additional Comments On Convergencementioning

confidence: 99%

Section: A Family Of Rls Algorithmsmentioning

confidence: 99%

Section: A Family Of Rls Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Decision directed adaptive blind equalization based on the constant modulus algorithm

Fernandes

Favier

Mota

2007

SIViP

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“…The CMA-type algorithms are very robust against channelorder estimation errors and other mismatches [19]. One possible reason is that these algorithms estimate zero-forcing equalizers directly from the observations without channel estimation.…”

Section: Introductionmentioning

confidence: 99%

Direct estimation of blind zero-forcing equalizers based on second-order statistics

Fan²

2000

IEEE Trans. Signal Process.

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Most existing zero-forcing equalization algorithms rely either on higher than second-order statistics or on partial or complete channel identification. We describe methods for computing fractionally spaced zero-forcing blind equalizers with arbitrary delay directly from second-order statistics of the observations without channel identification. We first develop a batch-type algorithm; then, adaptive algorithms are obtained by linear prediction and gradient descent optimization. Our adaptive algorithms do not require channel order estimation, nor rank estimation. Compared with other second-order statistics-based approaches, ours do not require channel identification at all. On the other hand, compared with the CMA-type algorithms, ours use only second-order statistics; thus, no local convergence problem exists, and faster convergence can be achieved. Simulations show that our algorithms outperform most typical existing algorithms.

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On the surface characteristics of a mixed constant modulus and cross-correlation criterion for the blind equalization of a MIMO channel

Lambotharan

Chambers

1999

Signal Processing

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On the robustness of the fractionally-spaced constant modulus criterion to channel order undermodeling .II

Cited by 7 publications

References 8 publications

Decision directed adaptive blind equalization based on the constant modulus algorithm

Decision directed adaptive blind equalization based on the constant modulus algorithm

Direct estimation of blind zero-forcing equalizers based on second-order statistics

On the surface characteristics of a mixed constant modulus and cross-correlation criterion for the blind equalization of a MIMO channel

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