Lattice-reduction-aided MMSE equalization and the successive estimation of correlated data

Fischer, Robert F. H.; Windpassinger, C.; Stierstorfer, Clemens; Siegl, Christian; Schenk, Andreas; Abay, Umit

doi:10.1016/j.aeue.2010.12.004

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Hence, in summary, using augmented matrices where the lower part reflects the correlations of the symbols, the MMSE estimator and the correlation matrix of the estimation error are simply given by the respective (pseudo)inverse (cf. also [40]).…”

Section: Induction Stepmentioning

confidence: 87%

Lattice-Reduction-Aided and Integer-Forcing Equalization

Fischer

Stern

Huber

2019

FNT in Communications and Information Theory

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Section: Induction Stepmentioning

confidence: 87%

Lattice-Reduction-Aided and Integer-Forcing Equalization

Fischer

Stern

Huber

2019

FNT in Communications and Information Theory

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“…Interestingly, a relation between PRS and lattice-reductionaided (LRA) equalization has been established in [12], [13], [14]. LRA techniques have been applied to flat fading MIMO channels with only spatial ISI [15], [16], [17], [18]. More precisely, a lattice reduction algorithm, e.g., the LLL algorithm [19] or the element-based reduction algorithm [18], first computes a reduced channel matrix that is better conditioned than the original channel matrix, i.e., closer to being orthogonal.…”

mentioning

confidence: 99%

Optimized Precoded Spatio-Temporal Partial-Response Signaling Over Frequency-Selective MIMO Channels

Bailleul

Jacobs

Guenach

et al. 2020

IEEE Trans. Wireless Commun.

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Due to the continuous demand for higher bit rates, the management of the spatio-temporal intersymbol interference in frequency-selective multiple-input multiple-output (MIMO) channels becomes increasingly important. For single-input singleoutput channels, equalized precoded partial-response signaling is capable of handling a large amount of intersymbol interference, but, to date, no equalization scheme with general partial-response signaling has been presented for the frequency-selective MIMO channel. Not only does this contribution extend partial-response signaling to the MIMO channel by proposing a general spatiotemporal partial-response precoder, but it also develops a minimum mean-squared-error optimization framework in which the equalization coefficients and the spatio-temporal target response are jointly optimized. Three iterative optimization algorithms are discussed, which update (part of) a row of the target impulse response matrix in each iteration. In particular, the third algorithm reformulates this row optimization as a lattice decoding problem. Numerical simulations confirm that the general partial-response signaling clearly outperforms the traditional full-response signaling in terms of the mean squared error and the bit error rate. The third optimization algorithm has a better performance but a higher complexity, compared to the first and the second algorithm.

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