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Likelihood-Based Modulation Classification for MU-MIMO Systems

Citation Author(s):
Prof. Mohammad Mansour, Dr. Louay Jalloul, Prof. Ali Chehab
Submitted by:
Hadi Sarieddeen
Last updated:
23 February 2016 - 1:44pm
Document Type:
Presentation Slides
Document Year:
Presenters Name:
Hadi Sarieddeen



The problem of optimal likelihood based modulation classification (MC) for optimal detection in 2x2 multiuser MIMO (MU-MIMO) receivers is considered. The optimal Log-MAP classifier is computationally exhaustive, and its sub-optimal Max-Log-MAP version poses remarkable degradation in performance. Between these two extremes, we propose four computationally simplified methods for MC, by taking special subsets of Euclidean distance computations that constitute the decision metric. Compared to the Max-Log-MAP classifier, the proposed schemes achieved a frame error rate (FER) gain of 0.5 dB with uncorrelated channels, while the gains reached 2 dB under high channel correlation.

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Dataset Files

GlobalSIP 2015 - MU-MIMO - Hadi Sarieddeen.pdf