Smart grids have become susceptible to cyber-attacks, being one of the most diversified cyber-physical systems. Measurements collected by the supervisory control and data acquisition system can be compromised by a smart hacker, who can cheat a bad-data detector during state estimation by injecting biased values into the sensor-collected measurements. This may result in false control decisions, compromising the security of the smart grid, and leading to financial losses, power network disruptions, or a combination of both. To overcome these problems, we propose a novel approach to cyber-attacks detection, based on an extremely randomized trees algorithm and kernel principal component analysis for dimensionality reduction. A performance evaluation of the proposed scheme is done by using the standard IEEE 57-bus and 118-bus systems. Numerical results show that the proposed scheme outperforms state-of-art approaches while improving the accuracy in detection of stealth cyber-attacks in smart-grid measurements.

Rate-splitting multiple access (RSMA) and the intelligent reflecting surface (IRS) are novel technologies to improve spectrum and energy efficiencies in multi-antenna networks. In this paper, we investigate a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted with an IRS and using RSMA. In particular, the IRS assists with data and energy transmission from the base station (BS) to power-splitting (PS) users under a non-linear energy harvesting (EH) model. Our objective is to minimize the transmission power subject to rate and EH requirements at the users. To solve the non-convex problem, we propose a bilevel programming problem in which a genetic algorithm is used to solve the outer optimization problem, and the inner optimization problem is addressed with the semidefinite relaxation (SDR) method. In addition, to study a practical scenario of imperfect channel state information (CSI), we consider a robust beamforming design based on imperfect cascade channels at the BS. Then, we propose a solution based on the S-procedure with the SDR technique. Simulation results show that our proposed RSMA-based scheme provides a significant reduction in transmission power compared to the space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) methods under perfect and imperfect CSI.

In this article, we investigate the rate-splitting multiple access (RSMA) framework in a multiuser multiple-input single-output (MISO) underlay cognitive radio system with power-splitting secondary users and information secondary users by using simultaneous wireless information and power transfer. The RSMA framework is a novel multiple access method based on linearly precoded rate splitting at the secondary base station, and successive interference cancellation at the secondary receivers, which has shown better performance compared with the traditional space-division multiple access (SDMA) method. In particular, the objective is to minimize the transmit power at the secondary base station subject to the constraints of minimum energy harvesting, minimum data rate, and a maximum power level for allowable interference with the primary users. The challenging problem under consideration is nonconvex, and the solution relies on dividing it into outer and inner optimization problems. Then, we propose a particle-swarm-optimization-based algorithm to solve the outer problem, with the inner problem managed by the semidefinite relaxation method. In addition, we present a comparative scheme based on the successive convex approximation method and a scheme based on SDMA. Finally, simulation results confirm the superior performance of the proposed approach in comparison with the other schemes.

Cooperative networks integrating non-orthogonal multiple access (NOMA) and simultaneous wireless information power transfer (SWIPT) are emerging technologies that have been investigated as potential techniques to support the proliferation of the Internet of Things (IoT) and to obtain greener communications. In this sense, the optimization of resource allocation schemes is crucial to improve the performance of future cooperative wireless networks. However, conventional optimization methods that attempt to find the optimal solution may entail high computational complexity. Therefore, we propose a low-complexity particle swarm optimization (PSO)-based scheme to solve the resource allocation problem in a cooperative non-linear SWIPT-enabled NOMA system with a non-linear energy harvesting (EH) user. Specifically, we consider two optimization problems. First, we minimize transmission power, and second, we maximize energy efficiency subject to meeting quality-of-service (QoS) constraints. The problems are non-convex and challenging to solve. Furthermore, we develop the optimal solution based on convex optimization and the exhaustive search (ES) method to validate the results of the proposed PSO-based framework. Afterward, we investigate the performance of five swarm intelligence-based baseline schemes and evaluate an additional low-complexity solution based on the cuckoo search (CS) technique. For comparison purposes, we use orthogonal multiple access (OMA), equal power splitting (EPS), and time fixed (TF) baseline schemes. To our satisfaction, the proposed SWIPT NOMA network outperforms the benchmark schemes, and the proposed PSO-based framework achieves the nearest performance to the optimal scheme with lower complexity than obtained by the comparative swarm intelligence techniques and from convex optimization with the ES method.

Cognitive radio (CR), non-orthogonal multiple access (NOMA), and full-duplex (FD) communications have been considered key technologies for providing spectrum utilization improvement and higher energy efficiency on the Internet of Things (IoT) networks and next-generation communication. However, security concerns are still an issue to be addressed because confidential information is exposed in wireless systems. To solve this problem, we design a novel artificial intelligence (AI)-based framework for maximizing the secrecy energy efficiency (SEE) in FD cooperative relay underlay CR-NOMA systems that are exposed to multiple eavesdroppers. First, we formulate the non-convex SEE optimization problem as bi-level optimization, subject to constraints that satisfy the quality-of-service requirements of secondary users. In particular, the outer problem is solved with ensemble learning (EL) to select the optimal relay. Regarding the inner problem, we propose a quantum particle swarm optimization (QPSO)-based technique to optimize power allocation. In addition, for comparison purposes, we describe a cooperative relay CR network with orthogonal multiple access (OMA), rate-splitting multiple access (RSMA), and half-duplex technologies. Moreover, we evaluate comparative schemes based on machine learning algorithms and swarm intelligence baseline schemes. Furthermore, the proposed EL-aided QPSO-based framework achieves performance close to the optimal solutions, with a meaningful reduction in computation complexity.