Low-Complexity Decoder Based on Conditional ML for QOSTBC

Nassaji, Ehsan; Almasi, Mojtaba Ahmadi; Fardad, Mohammad

doi:10.1109/lcomm.2018.2828825

Cited by 1 publication

(2 citation statements)

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“…The pairwise-ML method requires more computational complexity, as it has to perform joint ML decoding to recover two pairs of data signals separately, unlike OSTBC which only requires the linear ML method. Therefore, various methods have been proposed to simplify the pairwise-ML decoding scheme [17]- [20]. A linear ML decoding algorithm based on QR decomposition of the channel matrix was proposed in [17].…”

Section: Introductionmentioning

confidence: 99%

“…A linear ML decoding algorithm based on QR decomposition of the channel matrix was proposed in [17]. In [20], a conditional maximum-likelihood (ML) based detection scheme was proposed to reduce the decoding complexity of the QOSTBC system. Most of the existing QOSTBC schemes that have been developed rely on the assumption that the channel remains quasi-static during the length of the code word symbol periods.…”

Section: Introductionmentioning

confidence: 99%

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Improved Signal Detection Techniques for QOSTBC System in Fast Fading Channel

Patra¹,

Singh²

2020

JTIT

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Most existing quasi-orthogonal space time Block coding (QO-STBC) schemes have been developed relying on the assumption that the channel is at or remains static during the length of the code word symbol periods to achieve an optimal antenna diversity gain. However, in time-selective fading channels, this assumption does not hold and causes intertransmit-antenna-interferences (ITAI). Therefore, the simple pairwise maximum likelihood decoding scheme is not sufficient to recover original transmitted signals at the receiver side. To avoid the interferences, we have analyzed several signal detection schemes, namely zero forcing (ZF), two-step zero forcing (TS-ZF), minimum mean square error (MMSE), zero forcing - interference cancelation - decision feedback equalizer (ZF-IC-DFE) and minimum mean square error - interference cancelation { decision feedback equalizer (MMSE-IC-DFE). We have proposed two efficient iterative signal detection schemes, namely zero forcing - iterative interference cancelation - zero forcing { decision feedback equalization (ZF-IIC-ZF-DFE) and minimum mean square error - parallel interference cancelation - zero forcing – decision feedback equalization (MMSE-IIC-ZF-DFE). The simulation results show that these two proposed detection schemes significantly outperform all conventional methods for QOSTBC system over time selective channel.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%