Iterative Minimum Mean Square Error Interference Alignment Scheme for the MIMO X Channel

Shen, Hui; Lin, Bin; Luo, Yi; Liu, Feng

doi:10.1587/transcom.e94.b.1348

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The closed‐form MMSE and the IMMSE algorithms for IA are presented in [3]. The IMMSE algorithm outperforms the closed‐form MMSE by relaxing the need for perfect alignment, while minimising the signal's sum mean square error (MSE), that is, it minimises the expected sum of the norms between each

{\hat{s}}_{k, l} and {bold-italics}_{k, l}

for all k , which can be formulated by

\min \sum_{k = 1}^{K} E \{{(∥∥, {\hat{s}}_{k} - {bold-italics}_{k})}^{2}\}, s . t . {∥W_{j}∥}_{F}^{2} = P_{normalt}, j \in falsefalse{1, \dots, K falsefalse}

…”

Section: System Modelmentioning

confidence: 99%

“…A closed‐form solution for constant channels is still unknown for more than three users and is not necessarily the best solution for low‐to‐moderate signal‐to‐noise ratio (SNR) values [1]. Owing to the difficulty to obtain a closed‐form solution for constant channels, some iterative algorithms were proposed using alternating minimisation in minimum mean squared error (MMSE)‐based or maximum signal and interference noise ratio‐based iterative algorithms [3–5]. In [6], a robust iterative MMSE (IMMSE)‐based algorithm was proposed, which takes in consideration the channel estimation errors, motivated by the fact that in the presence of imperfect channel the performance of the MMSE algorithm has some degradation in high SNR.…”

Section: Introductionmentioning

confidence: 99%

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