In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multiple-input-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail.

Growing and diverse user needs, along with the need for continuous access with minimal delay in densely populated machine-type networks, have led to a significant overhaul of modern mobile communication systems. Within this realm, the integration of advanced physical layer techniques such as relaying-assisted transmission in beyond fifth-generation (B5G) networks aims to not only enhance network performance but also extend coverage across multicellular orientations. However, in cellular environments, the increased interference levels and the complex channel representations introduce a notable rise in the computational complexity associated with radio resource management (RRM) tasks. Machine and deep learning (ML/DL) have been proposed as an efficient way to support the enhanced user demands in densely populated environments since ML/DL models can relax the traffic load that is associated with RRM tasks. There is, however, in these solutions the need for distributed execution of training tasks to accelerate the decision-making process in RRM tasks. For this purpose, federated learning (FL) schemes are considered a promising field of research for next-generation (NG) networks’ RRM. This paper proposes an FL approach to tackle the joint relay node (RN) selection and resource allocation problem subject to power management constraints when in B5G networks. The optimization objective of this approach is to jointly elevate energy (EE) and spectral efficiency (SE) levels. The performance of the proposed approach is evaluated for various relaying-assisted transmission topologies and through comparison with other state-of-the-art ones (both ML and non-ML). In particular, the total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to approximately 10–20% compared to a state-of-the-art centralized ML scheme. Moreover, achieved accuracy can be improved by up to 10% compared to state-of-the-art non-ML solutions, while training time is reduced by approximately 50%.

Maritime communication networks are critical for supporting the increasing demands of oceanic and coastal activities, including shipping, fishing, and offshore operations. However, traditional systems face significant challenges in providing reliable, high-throughput connectivity due to dynamic sea environments, mobility, and non-line-of-sight (NLoS) conditions. Reconfigurable intelligent surfaces (RISs) have been proposed as a promising solution to overcome these limitations by enabling programmable control of electromagnetic wave propagation in next-generation mobile communication networks, such as beyond fifth generation and sixth generation ones (B5G/6G). This paper presents a deep learning-based (DL) scheme for beam selection in RIS-aided maritime next-generation networks. The proposed approach leverages deep learning to optimize beam selection dynamically, enhancing signal quality, coverage, and network efficiency in complex maritime environments. By integrating RIS configurations with data-driven insights, the proposed framework adapts to changing channel conditions and potential vessel mobility while minimizing latency and computational overhead. Simulation results demonstrate significant improvements in both machine learning (ML) metrics, such as beam selection accuracy, and overall communication reliability compared to traditional methods. More specifically, the proposed scheme reaches around 99% Top-$K$ Accuracy levels while jointly improving energy efficiency (ee) and spectral efficiency (SE) by approx. 2 times compared to state-of-the-art approaches. This study provides a robust foundation for employing DL in RIS-aided maritime networks, contributing to the advancement of intelligent, high-performance wireless communication systems for advanced maritime applications, such as autonomous mooring, the autonomous approach, and just-in-time arrival (JIT).

The ever-increasing and diverse user demands as well as the need for uninterrupted access to the medium with minimum latency in dense machine type communication networks, are the key driving forces to a holistic network redesign. In this context, fifth-generation and beyond (5G/B5G) networks, incorporate various advanced physical layer techniques, such as relaying-assisted transmission, aiming to improve network performance and extend the coverage area of multicellular orientations. However, the deployment of such techniques in a cellular environment characterized by high interference levels and multi-variate channel representations, leads to increased computational complexity for radio resource management (RRM) tasks. Machine learning (ML), and especially Deep Learning (DL), is proposed as an efficient way to support end-to-end user applications in highly complex environments, since ML/DL models can relax the RRM-associated computational burden. In this paper, we consider the joint problem of relay node (RN) placement and selection subject to subcarrier allocation and power management constraints in 5G/B5G networks. Various DL-based methods are examined and combined to solve both sub-problems. The performance of these schemes is evaluated for various relaying-assisted transmission approaches, either considering known channel state information (CSI) or not. According to the derived results, total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to 30%, when considering only the DL-based RN placement scheme compared to state-of-the-art non-ML schemes. The deployment of the reinforcement learning (RL) model for RN selection, can improve EE up to 80%, while SE can be improved up to 75%, compared to a system with only DL-enabled RN placement.

This chapter presents a survey that focuses on the implementation of federated learning (FL) techniques in sixth generation (6G) networks' physical layer (PHY) to meet the increasing user requirements. FL in PHY perspectives are discussed, along with the current trends and the present challenges in order to deploy efficient (cost, energy, spectral, computational) FL models for PHY tasks. Moreover, the utilization of FL methods is, also, discussed when channel state information (CSI) is not guaranteed in a 6G scenario. In such conditions, the joint use of cell free (CF) massive multiple-input-multiple-output (mMIMO), reconfigurable intelligent surfaces (RIS), and non-orthogonal multiple access (NOMA) and FL methods is proposed. Finally, an FL-based scheme for relay node (RN) placement in 6G networks is presented as an indicative use case for FL utilization in modern era networks. Results indicate that the proposed FL scheme overperforms state-of-the-art centralized learning schemes concerning the trade-off between machine learning (ML) metrics maximization and training latency.

Unmanned Surface Vehicles (USVs) are increasingly employed in maritime operations requiring high levels of autonomy, safety, and energy efficiency. However, traditional path planning techniques struggle to simultaneously address multiple conflicting objectives such as fuel consumption, trajectory smoothness, and obstacle avoidance in dynamic maritime environments. To overcome these limitations, this paper introduces a Deep Q-Learning (DQN) framework and a novel Fuzzy Deep Q-Learning (F-DQN) algorithm that integrates Mamdani-type fuzzy reasoning into the reinforcement-learning (RL) reward model. The key contribution of the proposed approach lies in combining fuzzy inference with deep reinforcement learning (DRL) to achieve adaptive, interpretable, and multi-objective USV navigation—overcoming the fixed-weight reward limitations of existing DRL methods. The study develops a multi-objective reward formulation that jointly considers path deviation, curvature smoothness, and fuel consumption, and evaluates both algorithms in a simulation environment with varying obstacle densities. The results demonstrate that the proposed F-DQN model significantly improves trajectory optimality, convergence stability, and energy efficiency, achieving over 35% reduction in path length and approximately 70–80% lower fuel consumption compared with the baseline DQN, while maintaining comparable success rates. Overall, the findings highlight the effectiveness of fuzzy-augmented reinforcement learning in enabling efficient and interpretable autonomous maritime navigation.