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 characteristic (ROC) area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification. Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio, including the worst case (i.e., ), where the fading distribution follows a one-sided Gaussian distribution. We also carry out a comparative study between our proposal using MultiBoostAB classifier with the decision tree (J48) classifier. Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier. In addition, we study the impact of the symbols number, path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.
Highlights
A lot of attention has been paid to the two-way relaying (TWR) scheme, which consists of the exchange information between two users via a commonly shared relay in the absence of a direct link between them [1,2,3,4,5,6]
To guarantee an accurate data reception at the stations, a correct detection of the stations modulations from the superimposed symbols is demanded. It is well-known that multiple-input multiple-output (MIMO) system can offer a considerable gain compared to single-input single-output (SISO) system, especially in environments presenting rich-scattering
To the best of the authors’ knowledge, there is no previous work, which focused on the problem of superimposed modulations classification for MIMO two-way cognitive relay (TWCR) network under realistic channel modeled by Nakagami-m
Summary
A lot of attention has been paid to the two-way relaying (TWR) scheme, which consists of the exchange information between two users via a commonly shared relay in the absence of a direct link between them [1,2,3,4,5,6]. The PNC can double the throughput of a TWRC compared to the conventional one-way relay channel by decreasing the time slots for the exchange of one packet from four to two [7,8] It can acheive 1/2 bit of the capacity using a single-input single-output (SISO) Gaussian TWRC and it is assymptotically optimal in the presence of high signal-to-noise ratio (SNR) levels [9]. To the best of the authors’ knowledge, there is no previous work, which focused on the problem of superimposed modulations classification for MIMO two-way cognitive relay (TWCR) network under realistic channel modeled by Nakagami-m. We propose an algorithm dedicated to the classification of the superimposed users modulations for MIMO TWCR network under Nakagami-m channels. CL×C represents the set of L × C matrices over complex field
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