Wireless communication, especially that relying on 5G technology, plays a crucial role in modern networks. The use of massive multiple-input, multiple-output (MIMO) systems is one of the key advancements in this area, as it improves energy efficiency (EE) and spectral efficiency (SE), making such a technique critical for future communication networks. This article focuses on optimizing EE and SE using a new metaheuristic multiverse optimization algorithm (MVO), and compares the results obtained with those achieved with the use of the Pachycondyla Apicalis algorithm (API) and other methods. Furthermore, the study explores the best values for factors such as coherence time, power amplifier efficiency, and hardware power in each user, with all of them playing a critical role in maximizing EE. The authors also examine the correlation between EE and SE in the downlink direction. The results show that the MVO approach achieves better performance in fewer iterations compared to API and other methods, demonstrating its potential for improving wireless communication systems.

This paper aims to optimize the pathloss in 4G/LTE networks obtained by empirical Radio Frequency (RF) propagation models to enhance user access quality. The radio wave propagation models are mainly used to predict the pathloss which are necessary for planning and optimizing wireless communication systems. In this paper, we propose a parametric optimization for loss estimation in a 4G/LTE network leveraging the Particle Swarm Optimization (PSO) algorithm to enhance the performances of this type of networks and decrease their complexity. For this sake, comparison and performance analysis were conducted using different environments such as urban, sub-urban and rural areas. First, we provide an analysis of radio propagation models, namely: Okumura-Hata, Stanford University Interim (SUI) and Ericsson 9999 models that would be used for outdoor propagation in LTE. Then, we optimize these empirical models using the PSO algorithm to make them more appropriate to the desired coverage area. This is achieved by minimizing the Root Mean Square Error (RMSE) between the optimized predicted data and the measured data in the field. Specifically, the measurements are taken in an urban region, as a case study, the city of Tebessa located in Algeria was selected. The proposed PSO pathloss optimization method showed better prediction performance with lower RMSE values than the analytical method based on empirical pathloss models.

The increasing demand for higher bandwidth and reliable communication networks driven by next-generation mobile technologies (5G/6G) and the Internet of Things (IoT) poses challenges for existing communication infrastructures. Free Space Optics (FSO) offers high-speed wireless data transmission comparable to fiber optics but suffers from significant performance degradation due to environmental factors such as rain, fog, and atmospheric turbulence. This study investigates the potential of a hybrid Dense Wavelength Division Multiplexing (DWDM)-FSO system to overcome these challenges. By combining DWDM’s high capacity with the flexibility of FSO links, the system achieves a data rate of 5.4 Tbps across 90 users. Simulations conducted using OptiSystem software evaluate the impact of different weather conditions – clear skies, heavy rain, and dense fog – on performance. Key metrics, including bit error rate (BER), quality factor (Q-factor), and eye diagrams, are analyzed to assess signal integrity and reliability. A comparative study of Single Input Single Output (SISO) and MIMO configurations (2 × 2 and 3 × 3) shows that the 3 × 3 MIMO-FSO setup significantly enhances performance, maintaining signal quality and extending range under adverse weather. These findings highlight the hybrid DWDM-FSO system’s potential to deliver high-speed, resilient communication, ensuring seamless connectivity in dynamic environments.

Chaotic systems appear in a wide range of natural and engineering contexts, making the design of reliable and flexible control strategies a crucial challenge. This work proposes a robust control scheme based on the Fractional-Order Backstepping Control (FOBC) method for the stabilization of non-commensurate fractional-order chaotic systems subject to bounded uncertainties and external disturbances. The method is developed through a rigorous stability analysis grounded in the Mittag–Leffler function, enabling the step-by-step stabilization of each subsystem. By incorporating fractional-order derivatives into carefully selected Lyapunov candidate functions, the proposed controller ensures global system stability. The performance of the FOBC approach is validated on fractional-order versions of the Duffing–Holmes system and the Rayleigh oscillator, with the results compared against those of a fractional-order PID (FOPID) controller. Numerical evaluations demonstrate the superior performance of the proposed strategy: the error dynamics converge rapidly to zero, the system exhibits strong robustness by restoring state variables to equilibrium quickly after disturbances, and the method achieves low energy dissipation with a high error convergence speed. These quantitative indices confirm the efficiency of FOBC over existing methods. The integration of fractional-order dynamics within the backstepping framework offers a powerful, robust, and resilient approach to stabilizing complex chaotic systems in the presence of uncertainties and external perturbations.

The rapid growth of Internet of Things (IoT) devices and the emergence of 5G/6G networks have created major challenges in secure and reliable data transmission. Traditional cryptographic algorithms, while robust, often suffer from high computational complexity and latency, making them less suitable for large-scale, real-time applications. This paper proposes a chaos-based encryption framework that uses the Sprott chaotic oscillator to generate secure and unpredictable signals for encryption. To achieve accurate synchronization between the transmitter and the receiver, two bio-inspired metaheuristic algorithms—the Pachycondyla Apicalis Algorithm (API) and the Penguin Search Optimization Algorithm (PeSOA)—are employed to identify the optimal control parameters of the Sprott system. This optimization improves synchronization accuracy and reduces computational overhead. Simulation results show that PeSOA-based synchronization outperforms API in convergence speed and Root Mean Square Error (RMSE). The proposed framework provides robust, scalable, and low-latency encryption for IoT and 5G/6G networks, where massive connectivity and real-time data protection are essential.