Over the years, several research groups have been developing effective and efficient scheduling algorithms to enhance the quality of service of mobile communication networks. The arrival of the fifth generation of mobile networks (5G) has demonstrated the importance of advanced scheduling techniques to manage the limited frequency spectrum available while achieving 5G transmission requirements. This issue has been picked up extensively within the research community due to the increasing demand for mobile communications and the desire for a fully connected world. Consequently, the scientific community has developed novel approaches and varied scheduling schemes to meet the needs of various applications and scenario conditions. In this context, this paper presents an overview of the state-of-the-art methods, highlights seminal and innovative research, and investigates the current state of 5G radio resource management. This review of literature compares emerging strategy methods based on their metrics, analyzes their performances, and emphasizes the existing works with a vision for the future of modern 5G and upcoming networks in terms of radio resource allocation to provide a thorough introspection of the literature. Furthermore, gaining a better understanding of the radio resource management state-of-the-art would provide valuable information for future work and might be helpful for new researchers in the field.

This paper presents a study and an array design consisting of two microstrip patch antennas connected in series in a 2×1 form. This antenna provides better performance for the fifth-generation (5G) wireless communication system. The microstrip line feeding technique realizes the design of this antenna. This feed offers the best bandwidth, is easy to model, and has low spurious radiation. The distance between the feed line and the patch can adapt to the antenna’s impedance. In addition, the antenna array proposed in this paper is designed and simulated using the high frequency structure simulator (HFSS) simulation software at the operating frequency of 28 GHz for the 5G band. The support material used is Rogers RT/duroid® 5880, with relative permittivity of 2.2, a thickness of h=0.5 mm, and a loss tangent of 0.0009. The simulation results obtained in this research paper are as: reflection coefficient: -35.91 dB, standing wave ratio (SWR): 1.032, bandwidth: 1.43 GHz, gain: 9.42 dB, directivity: 9.47 dB, radiated power: 29.94 dBm, accepted the power: 29.99 dBm, radiation efficiency: 29.95, efficiency: 99.83%. This proposed antenna array has achieved better performance than other antenna arrays recently published in scientific journals regarding bandwidth, beam gain, reflection coefficient, SWR, radiated power, accepted power, and efficiency. Therefore, this antenna array will likely become an important competitor for many uses within the 5G wireless applications.

The 5th generation of mobile communications, currently being rolled out, aims to improve network performance and efficiency over the older generations. With mmWaves, densified cell deployment is necessary for 5G networks, increasing capacity and improving network coverage. This imposes a considerable increase in the energy consumption of the 5G stations, which not only increases operating expenses for operators but also burdens the environment. Optimizing the energy consumption of 5G networks would be necessary to curb the energy curve. In this context, this paper presents a new algorithm, called Energy Consumption Optimization Algorithm (ECOA), which combines cell selection and standby techniques to optimize energy consumption while preserving network performance. A comparison is conducted between ECOA and standard cell selection modes to evaluate the performance of the conventional approach. Our algorithm exhibits good performance, particularly in high-density, high-load scenarios. For instance, in a site with 25 Pico base stations serving 500 users, our algorithm achieves an average throughput of 23 Mb/s per user while consuming 1750.75 W of energy. This represents a 2.44% increase in energy consumption compared to the optimal solution.

The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.

With the evolution of 5G-network, wireless mobile networks are growing to take a strong stand in attempts to achieve ubiquitous large-scale acquisition, connectivity and processing. Smart-grids are among the critical areas that can benefit from the capabilities of the 5G-network, especially internet of things (IoT) applications such as massive machine-type-communications or ultra-reliable low-latency communications. A distributed cloud-services use the cloud, fog and edge computing infrastructures and applications to take advantage of every available resource including network equipment and connected objects to optimize cost, energy, and latency depending on the planned optimization criteria. In this article, we present smart-grid solution based on cloud-services and 5G-network, then we study the integration of smart-grid services in the cloud based on: placement in the cloud and in the end-device, and finally we introduce our proposed solution based on Intelligent placement strategy. The scenarios are evaluated by the iFogSim simulator, and the analyzed results compare the standard cloud placement, edge placement and our intelligent placement with regard to the optimization of the energy consumption, latency, and network usage. The findings show that cloud energy consumption can be substantially reduced using Intelligent Placement while respecting the potential central processing unit (CPU) processing power-limit for each IoT-device used and network constraints in smart-grid.

An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people’s lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the network that interconnected IoT devices uses advanced communications norms and technologies to capture and transmit data. Still, these networks are subject to various types of attacks that will lead to the loss of user data. Concurrently, the field of anomaly detection for the Internet of Things (IoT) is experiencing rapid expansion. This expansion requires a thorough analysis of application trends and existing gaps. Furthermore, it is critical in detecting interesting phenomena such as device damage and unknown events. However, this task is tough due to the unpredictable nature of anomalies and the complexity of the environment. This paper offers a technique that uses an autoencoder neural network to identify anomalous network communications in IoT networks. More specifically, we propose and implement a model that uses DAE (deep autoencoder) to detect and classify the network data, with an ANOVA F-Test for the feature selection. The proposed model is validated using the NSL-KDD dataset. Compared to some IoT-based anomaly detection models, the experimental results reveal that the suggested model is more efficient at enhancing the accuracy of detecting malicious data. The simulation results show that it works better, with an overall accuracy rate of 85% and 92% successively for the binary and multi-class classifications.