It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid’s stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method.

Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI). At the destination node, we extract the higher-order statistics of the received signals as the discriminating features. After applying the principal component analysis technique, we carry out a comparative study between the random committee and the AdaBoost machine learning techniques (MLTs) at low signal-to-noise ratio. The efficiency metrics, including the true positive rate, false positive rate, precision, recall, F-Measure, and the time taken to build the model, are used for the performance comparison. The simulation results show that the use of the random committee MLT, compared to the AdaBoost MLT, provides gain in terms of both the modulation detection and complexity.

Reducing energy consumption is a key challenge in the design of any routing protocol dedicated to wireless sensor networks (WSNs). In this study, the authors introduce a new low energy consumption protocol based on a cooperative relaying technique. The proposed protocol is called distributed cooperative relaying low energy adaptive clustering hierarchy (DCR‐LEACH) protocol, and is a way of improving the LEACH protocol. The authors have exploited cooperative relaying transmission techniques, in order to enhance LEACH for large‐scale WSNs, whereby, a cluster head cooperates with the most energetic nodes in order to transmit collected data to the sink. To evaluate the energy efficiency of the proposed DCR‐LEACH, they derive the expressions of energy consumption by applying a cooperative relaying technique and simulate the DCR‐LEACH using NS‐2 for three different scenarios. To confirm the efficiency of the DCR‐LEACH protocol, they compare the proposed scheme with both LEACH and multi‐hop routing LEACH (MR‐LEACH) protocols. The experimental results prove that for large‐scale WSNs, the performance of DCR‐LEACH protocol is better than MR‐LEACH and conventional LEACH in terms of network lifetime, energy consumption, and number of data transmission.

Exploiting cognitive radio (CR) technology in intelligent transportations systems have recently attracted a significant attention from the research communities. In particular, we study cooperative vehicular ad hoc networks (VANET) which are considered as an important application of CR. Spectrum sensing is a crucial task in CR applications. However, fading propagation channels can significantly limit the sensing performance. Thus, great efforts are made to model the fading and shadowing channel characteristics in CR-VANET. Indeed, experimental measurements have shown that the 5.9 GHz dedicated short range communication narrow-bad allocated to VANET is characterized with Nakagami distribution. Thus, blind eigenvalues-based spectrum sensing methods are appropriate in high fading environments. In this paper, we investigate blind detection in cooperative CR-VANET over Nakagami-m fading channels. The results reveal the great impact of fading channel characteristics and their severity on spectrum sensing performance. Besides, several cases of interest are examined such as the impact of fading factor, CR-vehicles number, samples number, etc.

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.