Mobile ad hoc networks (MANETs) are autonomous systems composed of multiple mobile nodes that communicate wirelessly without relying on any pre-established infrastructure. These networks operate in highly dynamic environments, which can compromise their ability to guarantee consistent link lifetimes, security, reliability, and overall stability. Factors such as mobility, energy availability, and security critically influence network performance. Consequently, the selection of paths and relay nodes that ensure stability, security, and extended network lifetimes is fundamental in designing routing protocols for MANETs. This selection is pivotal in maintaining robust network operations and optimizing communication efficiency. This paper introduces a sophisticated algorithm for selecting multipoint relays (MPRs) in MANETs, addressing the challenges posed by node mobility, energy constraints, and security vulnerabilities. By employing a multicriteria-weighted technique that assesses the mobility, energy levels, and trustworthiness of mobile nodes, the proposed approach enhances network stability, reachability, and longevity. The enhanced algorithm is integrated into the Optimized Link State Routing Protocol (OLSR) and validated through NS3 simulations, using the Random Waypoint and ManhattanGrid mobility models. The results indicate superior performance of the enhanced algorithm over traditional OLSR, particularly in terms of packet delivery, delay reduction, and throughput in dynamic network conditions. This study not only advances the design of routing protocols for MANETs but also significantly contributes to the development of robust communication frameworks within the realm of smart mobile communications.

Ensuring the integrity and reliability of optical fiber networks is essential for modern global communication systems, as faults such as fiber cuts, malicious tapping, and connector degradation can result in significant service disruptions and security vulnerabilities. Traditional fault detection methods, such as optical time-domain reflectometry (OTDR), face challenges due to noisy signals, signal attenuation, and reliance on expert interpretation. Additionally, existing machine learning approaches often struggle with generalization across diverse fault types and imbalanced datasets. This paper introduces a novel Hybrid CNN-Ensemble framework, combining convolutional neural networks (CNNs) for deep feature extraction with ensemble learning techniques including XGBoost, Support Vector Machines (SVM), and Random Forest (RF). The framework processes CNN-extracted features from OTDR traces, with predictions aggregated through a meta-classifier. Validated on a comprehensive OTDR dataset covering eight distinct fault categories, the proposed method achieves 99.5% accuracy and an F1-score of 0.99, offering exceptional precision in detecting and classifying fiber faults. The results demonstrate the framework’s ability to improve fault detection performance, streamline fiber monitoring processes, reduce operational costs, and enhance network resilience to both physical attacks and accidental failures.

Traditional mathematical models for communication-system design often use simplifying assumptions and are difficult to extend to complex or rapidly changing channels. In recent years, deep learning, and in particular autoencoders (AEs), has enabled end-to-end optimization of transmitters and receivers and has shown strong performance in many communication scenarios. This paper provides a comprehensive review of AE-based communication systems across wireless, optical (fiber and wireless), semantic, and quantum domains. The review is based on recent works indexed in major digital libraries and is organized around four cross-cutting design themes: channel modeling and differentiability, complexity and scalability, generalization and model mismatch, and data scarcity and realism. For each theme, as well as for each application domain, we summarize representative AE architectures, training strategies, reported performance, and practical limitations. The paper also outlines how to quantify the computational complexity of AE-based transceivers using Big-O notation and discusses implications for deployment in resource-constrained environments. Finally, we identify open challenges, including robust learning over non-differentiable or partially known channels, scalable and hardware-aware architectures, and standardized evaluation protocols, and we provide guidelines for future research on AE-based designs for next-generation communication systems.

Connect-4, a solved two-player perfect-information game, offers a compact benchmark for artificial intelligence research due to its strategic depth and structural regularities, including board symmetries. This review presents a taxonomy-driven synthesis of Connect-4 AI research, encompassing game-theoretical foundations, classical search algorithms, reinforcement learning methods, explainable AI, and formal verification approaches. Analysis of search-, learning-, and hybrid-based methods reveals three dominant patterns: (i) classical search techniques prioritize determinism and efficiency but face scalability limits; (ii) reinforcement learning and neural approaches improve adaptability at the cost of interpretability and computational resources; and (iii) explainable and formally verified frameworks enhance transparency and reliability while imposing additional performance constraints. Recent advances in Connect-4 AI are driven less by raw performance gains than by strategic integration of efficiency, adaptability, interpretability, and robustness. Structuring the literature through a multidimensional taxonomy clarifies conceptual relationships, highlights underexplored research intersections, and points to emerging trends, including hybrid search–learning systems and explainable game intelligence. Overall, Connect-4 serves as a concise experimental domain for investigating fundamental challenges in game-playing AI, system design, and human–AI interaction.

This paper presents cyclic shift phase rotation (CSPR), an innovative technique designed to mitigate the peak-to-average power ratio (PAPR) in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. CSPR demonstrates notable improvements when compared to traditional techniques, such as discrete Fourier transform spread (DFT-spread) and selective mapping (SLM). CSPR eliminates the need for side information, additional inverse fast Fourier transforms (IFFTs), and complex search processes that are needed by other techniques. Analytical evaluations confirm that CSPR requires O(N log C₀) operations, which is significantly lower than the O(M · N logN) complexity of SLM and the O(L · n log n) complexity of DFT-spread. The CSPR has been evaluated within the CO-OFDM transmission. At a distance of 800 km, CSPR facilitates a 3 dB reduction in the required optical signal-to-noise ratio (OSNR) necessary for achieving hard-decision forward error correction (H-FEC) when compared to DFT-spread utilizing 112 sub-bands, and it exceeds the performance of the SLM at low OSNR with the use of 64 candidates. Moreover, it exhibits an 8 dB enhancement in the Q-factor over transmission distances extending up to 2000 km under nonlinear conditions. These results position CSPR as an effective and efficient solution for PAPR mitigation, significantly improving system performance, and reducing hardware complexity.