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Automatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning‐aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1‐score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs.
Automatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning‐aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1‐score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs.
Cognitive radio is a technology that allows the unlicensed users to access the licensed spectrum based on sensing information perceived by the users. Cognitive radio user generally attempts to access the unutilized band of licensed user. However, the emergence/reappearance of primary users during ongoing cognitive user's transmission creates the interference at the primary receiver and data loss at cognitive receiver. In this context, the spectrum monitoring is a vital process of the cognitive radio network during data transmission to continuously monitor the primary user's presence or absence on a licensed channel. In this manuscript, we have utilized the spectrum monitoring technique on earlier existing MAC protocol for CR network to detect the emergence/reappearance of primary users throughout the data transmission period of cognitive users. The spectrum monitoring is employed to reduce the interference at primary receiver, which is measured in terms of interference power (IP) and data loss at CU receiver. The closed form expressions of data loss and IP for the monitoring and without monitoring scenarios are provided. In addition, the probability of monitoring error's (which causes misinterpretation of channel's status by the cognitive users) impact on these performance parameters is also provided and simulated. Further, earlier MAC protocol has significant amount of bandwidth wastage in case primary user disappears or reappears during cognitive user's data transmission, which is shown mathematically and simulated as well. However, incorporation of the monitoring phenomenon even with significant amount of monitoring error has reduced bandwidth wastage, which is measured in bps. In addition, the throughput calculation of the proposed system and the effect of monitoring error on the throughput of the proposed system are also seen. As per the results, the maximum data loss and IP occurred at licensed channel's busy probability value of 0.5, which is 23.22 bps and 3.487 W, respectively, at maximum monitoring error value. However, minimum data loss and IP are achieved at licensed busy channel's probability values of zero and one. In addition, the resource wastage at licensed channel's busy probability value of 0.5 is varying from 2.322 to 23.22 bps for varying the monitoring error value from 0.1 to 1, respectively, giving highest resource wastage at maximum monitoring error value.
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