ABSTRACT Fractional frequency reuse (FFR) is an efficient interference coordination method for enhancing cell‐edge performance of dense fifth‐generation (5G) heterogeneous networks (HetNets). Unfortunately, traditional FFR arrangements based on static or rule‐oriented settings cannot adapt to dynamic traffic distribution and spatial interference patterns. To overcome these limitations, we present a spatially aware resource allocation framework that integrates convolutional neural networks (CNNs) with double deep Q‐learning (DDQN) to optimize FFR. The proposed approach represents the network state as a multi‐channel spatial tensor that captures the signal‐to‐interference‐plus‐noise ratio (SINR), interference levels, user density, and channel quality across the network area. A lightweight CNN architecture is employed to extract hierarchical spatial features from the network state, enabling the DDQN agent to learn interference‐aware FFR policies. Extensive simulations based on 3GPP‐compliant HetNet models are conducted across multiple deployment scenarios, including urban dense, suburban, and rural environments. The results demonstrate that the proposed CNN‐DDQN framework achieves significant performance improvements, including up to a 21% increase in average throughput, a 30% improvement in cell‐edge throughput, and a 15% improvement in fairness compared with traditional static FFR schemes. In addition, the proposed model improves performance by 8%–12% over conventional DRL‐based approaches that lack spatial feature extraction. The lightweight architecture contains only 26,425 trainable parameters, enabling fast training and real‐time inference with latency below 10 ms. These results demonstrate that spatial feature extraction can significantly enhance reinforcement learning for interference management and resource allocation in next‐generation wireless networks.

The advent of 5G heterogenous network (HetNets) with densely deployed femtocells, offer enhanced capacity, handoff, throughput, and spectrum utilization. Yet, the intricacies of radio resource allocation pose significant challenges, exacerbated by interference, penetration losses, reduced capacity and throughput. Consequently, mitigating interference within femtocell networks is imperative for optimal network performance. By surveying existing methodologies and exploring machine learning (ML) driven solutions, the reinforcement learning (RL) algorithm has emerged as a promising solution for optimize network performance. The RL Algorithm has been surveyed as a capable solution to the various problems of 5G HetNet focusing on double deep neural network (DDNN). We reviewed existing RL approaches including Q-learning and deep q-networks (DQN), highlighting their limitation. Our analysis revealed that using DDQN approach which can dynamically adjust power control and frequency allocation strategies to optimize network performance, DDNN superiority stem from ability learn complex pattens, adapt to dynamic environments and generalize well. DDQN ability to handle non-stationarity and reduce overestimation bias makes it a promising approach for improving spectral efficiency and reducing interference in complex HetNet environments. this research contributes to the evolving landscape of 5G network optimization and interference mitigation strategies.

Solar energy is the cleanest and most abundant form of energy that can be obtained from the Sun. Solar panels convert this energy to generate solar power, which can be used for various electrical purposes, particularly in rural areas. Maximum solar power can be generated only when the Sun is perpendicular to the panel, which can be achieved only for a few hours when using a fixed solar panel system, hence the development of an automatic solar tracking system. Over the years, different solar tracking systems have been proposed and developed, and a few have been reviewed in the literature. However, the existing review works have not adequately provided a comprehensive survey and taxonomies of these solar tracking systems to show the trends and possible further research direction. This paper aims to bridge these gaps by extensively reviewing these time-based solar tracking systems based on axis rotation and drive types. Lessons learned from the comprehensive review have been highlighted and discussed. Finally, critical open research issues are identified and elaborated.

Signal propagation in a particular region differs from another due to differences in atmospheric, climatic and environmental properties, distinct terrain and clutter features. Adequate analysis is essential to understand the radio propagation behavior in a particular region. The ITU-R designated four rain regions, M, N, P, and Q, for Nigeria representing the rain rate distribution and also provided further classifications based on ground conductivity, among other salient parameters. Based on these classifications, this paper utilized the EDX Signal Pro software® to model and simulate a typical Point-to-Point (P2P), Non-Line of Sight (NLOS) link scenarios for 5G networks. The objective was to estimate and compare the total loss, excess loss, and flat fade margins for each rain region in Nigeria. Results obtained from the comparison showed that signals propagating in region N experience the highest level of losses and fading, while, region Q has the least losses and fading.

Massive multiple-input multiple-output (MIMO) is a promising 5G technology, but the high energy consumption from numerous radio components is a key challenge. Prior work has explored higher than 4-bit ADCs, but very low 1-2 bit remain uninvestigated. This paper provides an in-depth examination of the resolution versus power savings and signal quality degradation trade-offs with 1–3-bit analog-to-digital converters (ADCs) in Massive MIMO systems. Mathematical modeling and simulations quantify the exponential reduction in power consumption along with losses in key performance metrics like signal-to-interference-plus-noise ratio (SINR), throughput, and spectral efficiency with lower ADC resolutions stemming from increased quantization noise. Advanced linear detection techniques are shown to effectively recover the signal quality degradation. The analysis provides valuable insights on jointly optimizing ADC configuration and signal processing to maximize overall system efficiency. Energy savings of over 50% are demonstrated with 1-bit ADCs along with the ability to maintain acceptable performance metrics using linear detectors. The practical implementation feasibility is also discussed. This work enables ultra-low-resolution ADC deployments in energy-efficient Massive MIMO systems.

Terrestrial Radio Propagation (TRP) involves radio wave propagation from one station to another over the surface of the earth. Radio communication systems have been deployed for broadcasting, mobile cellular, and public safety. Radio propagation planning plays a crucial role in designing and deploying terrestrial radio networks. Radio propagation models (Path loss Models) are utilized during the link budget, coverage, and interference estimations. The models guide radio network engineers in choosing appropriate placements of radio network equipment, such as the base stations. However, the high cost attributed to TRP data collection, lack of open access, and accurate TRP datasets hinders the development of path loss models that can reliably predict outcomes for different use cases. To address this problem, this paper aims to present a robust TRP repository that provides a platform for hosting and disseminating TRP datasets, which the research community could use for path loss modeling. The repository was implemented using the latest technologies, and its performance was evaluated. The system has been deployed for public access.

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

In modern times, people’s fast-paced lifestyles make it challenging to keep track of essential recurring bills such as electricity, water, and gas. This paper proposes a unified smart metering system utilizing the Internet of Things (IoT) technology to address this need. This system comprises two interconnected sub-systems that manage resource consumption. The first subsystem is a unified metering sub-system, while the second is a cloud-controlled sub-system that stores and analyzes data. Both sub-systems communicate wirelessly, and the server is automatically updated as resources are consumed. The design employs an intelligent interaction approach that leverages information and communication technologies to improve user experiences, task performance, and quality of life. The expected outcome is the development of a mobile application that integrates a billing portal for payment processing. The implementation of this solution is expected to enhance resource utilization, promote sustainability practices, and revolutionize the user experience of interacting with utility services.

ITU-R categorized Nigeria into four rain regions (i.e., M, N, P, and Q) depending on the atmospheric conditions. Previous works that have conducted rain and attenuation modeling and simulations have assumed similar signal propagation behavior and parameters within locations categorized under the same region. This paper aims to explore these assumptions by conducting an extensive radio propagation simulations of point-to-point microwave links with a clear line-of-sight in order to estimate the total path loss (attenuation), excess path loss, and flat fade margins for each of the locations within the M-regions, i.e., Kano, Sokoto, and Adamawa. Results obtained showed that in all the locations, the loss monotonically increases with distance, with Sokoto having the highest level of signal losses and fading, while, Adamawa has the lowest. The deviation for both total loss, excess loss, and flat fading margin was found to be between 15 dB across the region.

Telehealth systems have rapidly emerged as a critical component of modern healthcare, enabling remote patient care, real-time monitoring, and improved access to medical services. With the advent of artificial intelligence (AI) and blockchain technology, telehealth systems have witnessed significant advancements in terms of efficiency, security, and interoperability. AI and blockchain technologies have emerged as powerful tools with immense potential in healthcare. However, there exists the problem of effectively categorizing and classifying the various applications and implementations of AI and blockchain technology in the context of telehealth systems. There is a need for a comprehensive taxonomy to organize and understand the diverse applications of AI and blockchain technology in telehealth due to the rapid growth in these fields. Therefore, this chapter presents a taxonomy that explores the integration of AI and blockchain in telehealth systems. The taxonomy aims to categorize, classify, and analyze the various applications, benefits, challenges, and future directions of this integration, providing a comprehensive understanding of the synergistic relationship among AI, blockchain, and telehealth. The chapter, thus, contributes to the advancement and adoption of these technologies in healthcare settings.

The rapid increase in data traffic caused by the proliferation of smart devices has spurred the demand for extremely large-capacity wireless networks. Thus, faster data transmission rates and greater spectral efficiency have become critical requirements in modern-day networks. The ubiquitous 5G is an end-to-end network capable of accommodating billions of linked devices and offering high-performance broadcast services due to its several enabling technologies. However, the existing review works on 5G wireless systems examined only a subset of these enabling technologies by providing a limited coverage of the system model, performance analysis, technology advancements, and critical design issues, thus requiring further research directions. In order to fill this gap and fully grasp the potential of 5G, this study comprehensively examines various aspects of 5G technology. Specifically, a systematic and all-encompassing evaluation of the candidate 5G enabling technologies was conducted. The evolution of 5G, the progression of wireless mobile networks, potential use cases, channel models, applications, frequency standardization, key research issues, and prospects are discussed extensively. Key findings from the elaborate review reveal that these enabling technologies are critical to developing robust, flexible, dependable, and scalable 5G and future wireless communication systems. Overall, this review is useful as a resource for wireless communication researchers and specialists.

Wireless communication is one of the very successful technologies that have found applications in our daily lives. It has drawn the attention of researchers, standard bodies, and organizations who have continuously proposed and developed different standards and regulations to further advance the communication system. However, one of the major issues and concerns that have been raised in wireless communication is security with its legal frameworks. Security as well as privacy are very crucial schemes in wireless communication because of the transmission of signals over unprotected media, thus exposing signals to security and privacy attacks such as eavesdropping, modification, and data theft, among others. Depending on the type of data being transmitted, security and privacy and the legal frameworks become even more critical, especially with the adoption of new technology such as cloud computing in the healthcare and other sectors. While framework is a structure that combines, in the form of a single hybrid conceptual solution, different but relevant areas together, the legal frameworks are simply a set of standards which can be utilized to deal with a challenge or to decide what to do. Some legal frameworks have been developed, over the years, particularly in the area of healthcare, Artificial Intelligence (AI) and Internet of Things (IoT). However, there are no sufficient legal frameworks for the security and privacy of wireless network, particularly for the envisioned 6G network. Thus, this chapter presents the fundamental factors of legal frameworks for the security and privacy in a wireless communication network with a focus on the 6G network.