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The usage of artificial intelligence and machine learning in wireless communications is the stepping stone towards a technological breakthrough in the current limitations of wireless communication systems. The trend of future coding schemes towards 6G appears to be based on rateless schemes and machine learning. Channel coding is important when transmitting data or information reliably as it provides error-correcting purposes. However, there is still a demand for more research regarding machine learning for channel coding. There is also a lack of a specific term or classification for existing machine learning applications for channel coding. This paper explores and compiles current trending machine learning techniques for channel coding. We are also introducing and proposing a new type of machine learning classification for channel coding purposes, as well as surveying some of the papers that fall under the respective class. This paper also discusses current challenges and future machine learning trends for channel coding, which are expected to impact future wireless communications development, especially in channel coding advancements.

The estimation of Electromagnetic Field (EMF) exposure is critical for evaluating the potential health risks associated with wireless network implementation. With the advent of new communication technologies, such as 5G and 6G, new methodologies are required. Indeed, higher frequency ranges are being studied, necessitating an increased number of base stations for service provision. In this paper, we introduce a novel methodology for EMF estimation in 5G mobile networks. We also evaluate common scenarios that mobile network operators frequently encounter, resulting in additional exposure, such as interference, overpropagation, and the presence of other users. Our approach is validated through a combination of simulated values and real data collected from sensors deployed in the field. Finally, a digital twin approach is proposed to combine the simulated values with the real data obtained from radio frequency sensors. The scenario is deployed in Belval, Luxembourg.

Modern electrical systems are evolving with data communication networks, ushering in upgraded electrical infrastructures and enabling bidirectional communication between utility grids and consumers. The selection of communication technologies is crucial, where wireless communications have emerged as one of the main benefactor technologies due to its cost-effectiveness, scalability, and ease of deployment. Ensuring the streamlined transmission of diverse applications between residential users and utility control centers is crucial for the effective data delivery in smart grids. This paper proposes a congestion control mechanism tailored to smart grid applications using unreliable transport protocols such as UDP, which, unlike TCP, lacks inherent congestion control, presenting a significant challenge to the performance of the network. In this article, we have exploited a reinforcement learning (RL) algorithm and a deep Q-neural network (DQN), to manage congestion control under a UDP environment. In this particular instance, the DQN model learns on its own from interactions with the environment without the need to generate a dataset, rendering it apt for intricate and dynamic scenarios within smart grid communications. Our evaluation covers two scenarios: 1) a grid-like configuration, and 2) urban scenarios considering the deployment of smart meters in the cities of Montreal, Berlin and Beijing. These evaluations provide a comprehensive examination of the proposed DQN-based congestion control approach under different conditions, showing its effectiveness and adaptability. Conducting a comprehensive performance assessment in both scenarios leads to improvements in metrics such as packet delivery ratio, network throughput, fairness between different traffic sources, packet network transit time, and QoS provision.

This paper presents an in-depth investigation into path loss modeling for wireless communication systems, emphasizing the critical role of Exploratory Data Analysis (EDA) and accurate field data collection. It reveals the enhanced efficacy of theoretical models when synergized with specific, real-world data, highlighting the necessity for empirical grounding. Central to our research is the demonstration of the indispensability of thorough EDA in path loss modeling. This phase uncovers key trends and anomalies, setting a solid foundation for subsequent Machine Learning (ML)-based predictive modeling. Deep dataset insights allow for the effective tuning of algorithms to reflect the complexities of signal propagation environments accurately. The study further underscores the impact of the quality of field data collection on model accuracy. Meticulous data gathering that accurately mirrors environmental conditions is essential, providing insights into signal behavior and validating theoretical models. Our research also explores the importance of spatial pattern analysis in understanding path loss. Advanced visualization and clustering methods are employed to illustrate how spatial variability significantly influences path loss, shedding light on the effects of environmental factors like obstructions and topography on signal propagation.

The evolving landscape of beyond 5G and 6G wireless communication systems in smart urban environments faces numerous interference-related challenges posed by legitimate and illicit devices. In this context, Intelligent Reflecting Surfaces (IRS) have emerged as a promising solution to mitigate interference caused by obstacles and unknown jamming devices. Existing techniques mainly focus on mitigating the impact of a single jammer in IRS-assisted communications systems, which affects both stationary and mobile devices. Additionally, these approaches target a single objective, such as minimizing the energy, enhancing the transmission rate, or maximizing the Signal-to-Interference-plus-Noise Ratio (SINR), which restrains the performance of the system. This paper offers a comprehensive anti-jamming solution for securing wireless communications in a smart city urban environment comprising diverse public events such as sporting events, parades, festivals, and exhibitions. The focus is on maintaining essential services like security, law enforcement, logistics, emergency response, crowd management, and public health. We introduce a Reinforcement Learning-based technique for UAV-mounted IRS, optimizing trajectory and phase shift beamforming to counteract the disruptive impact of jammers, ensuring reliable communication in dynamic, security-sensitive settings Our approach also seeks to achieve multi-objective optimization by striking a balance between transmission rate and energy consumption in this highly challenging environment. The formulated optimization is computationally complex due to its combinatorial nature. Hence, we leverage the light-weight Deep Reinforcement Learning (DRL) technique called Deep Deterministic Policy Gradient (DDPG) to optimize trajectory and IRS phase shifts and achieve multiple objectives jointly. Experimental results demonstrate the effectiveness of our proposed DDPG-based approach in outperforming other RL algorithms. It achieves a near-optimal solution compared to the benchmark technique within the close gap and improves both achievable transmission rates and energy efficiency compared to related works by 50-70%.