Haithem Ben Chikha, Ahmad Almadhor*
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1799-1814, 2022, DOI:10.32604/cmc.2022.018819
Abstract To promote reliable and secure communications in the cognitive radio network, the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation. In this paper, we address the classification of superimposed modulations dedicated to 5G multiple-input multiple-output (MIMO) two-way cognitive relay network in realistic channels modeled with Nakagami- distribution. Our purpose consists of classifying pairs of users modulations from superimposed signals. To achieve this goal, we apply the higher-order statistics in conjunction with the MultiBoostAB classifier. We use several efficiency metrics including the true positive (TP) rate, false positive (FP) rate, precision, recall, F-Measure and receiver operating… More >
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