In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. Generally, as network input of AMC convolutional neural networks (CNNs) images or complex signals are utilized in time domain or frequency domain. In terms of the image that contains RGB(Red, Green, Blue) levels the input size may be larger than the complex signal, which represents the increase of computational complexity. In terms of the complex signal it is normally used as 2 × N size for the input, which is divided into in-phase and quadrature-phase (IQ) components. In this paper, the input size is extended as 4 × N size by copying IQ components and concatenating in reverse order to improve the classification accuracy. Since the increase in the amount of computation complexity due to the extended input size, the proposed CNN archiecture is designed to reduce the size from 4 × N to 2 × N by an average pooling layer, which can enhence the classification accuracy as well. The simulation results show that the classification accuracy of the proposed model is higher than the conventional models in the almost signal-to-noise ratio (SNR) range. INDEX TERMSAutomatic modulation classification, Deep learning model, Convolution neural network, Frame extension, Cognitive radio.

Abstract-In this paper, an enhanced SDP-Dynamic bloom filters for a DDS node discovery scheme in real-time distributed systems is proposed. Since the previous works of the DDS focuses more on the usage of a Simple Discovery Protocol (SDP) for endpoint to endpoint information communication of industrialscale networks, attempts have now been made to enhance this approach into the Simple Discovery Protocol Dynamic Bloom Filters (SDP-Dynamic Bloom) focusing more on scalability in the amount of sent and stored message packets in the industrial network system. Simulation result show that the proposed scheme viciously reduce the overall computing and processing time of both stable and unstable industrial network environment which arises during the restructuring process of the existing SDP bloom filters approach.

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In competing multi-dimensional gaming drones, inherent inaccuracies of the precisepoint-positioning (PPP) measurement of the global navigation satellite system (GNSS) have become rampant, hence, necessitating this work. These inaccuracies, occasioned by system drawbacks such as sudden GPS lock, device misalignment constraints, poor detection and communication signals, all lead to PPP computational complexities. To mitigate the inherent PPP complexities, robust and hybrid accurate continuous-discrete (ACD) cubature-extended Kalman filter (C-EKF) methodology for next-generation GNSS integrated system is corroborated. More precisely, time updates to the state and parameter subvectors were accomplished using the third-degree spherical-radial cubature rule. A testbed deployment of the system was then conducted and investigated using tightly-coupled (i) ring laser gyroscope (RLG) and (ii) micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) based devices to ascertain the PPP cooperative tendencies. Optimized performance comparisons of the proposed hybrid C-EKF over the existing cubature Kalman filter (CKF) and the extended Kalman filter (EKF) schemes with-respect-to (w.r.t) their probabilistic outages, Yaw error-differences and ergodic capacities were demonstrated in situations of inaccurate PPP caused by GNSS distortions.INDEX TERMS Gaming drones, global navigation satellite system (GNSS), inertial measurement unit (IMU), hybrid cubature-extended Kalman filter (C-EKF), precise point positioning (PPP).