On the utility of MIMO multi-relay networks for modulation identification over spatially-correlated channels

The necessity to perfectly monitor the intercepted signals for spatially-correlated multiple-input multiple-output (MIMO) systems, involves modulation identification algorithms. In this paper, we present an algorithm dedicated to the modulation identification for correlated MIMO relaying broadcast channels with direct link using multi-relay nodes. By modeling spatially-correlated MIMO channels as Kronecker-structured and the imperfect channel state information of both the source-to-destination and the relay-to-destination errors as independent complex Gaussian random variables, we firstly derive the ergodic capacity of the proposed transmission system. It turns out that the ergodic capacities improve with the number of relay nodes. Based on a pattern recognition approach using the higher order statistics features and the Bagging classifier, we show that the probability to distinguish among M-ary shift keying linear modulation types without any priori modulation information is enhanced compared to the decision tree (J48), the tree augmented naive Bayes, the naive Bayes using discretization and the multilayer perceptron classifiers. We also study the effect of increasing the number of relay nodes. Numerical simulations show that the proposed algorithm using the cooperation of multi-relay nodes with the source node can avoid the performance deterioration in modulation identification caused by both spatial correlation and imperfect CSI.