The analysis of coverage probability based on signalto-interference-plus-noise-ratio (SINR) is a classical problem in the study of wireless and cellular networks. Stochastic geometry (SG) has opened up the possibility of accurate coverage characterization for random spatial deployment of base stations. While results obtained using SG are tractable and compact, however in most cases, they are usually in the form of incomplete integrals, which need to be efficiently computed. Although that is possible with the computational capabilities available today, it masks the underlying structure in the analysis precluding the possibility of using it for solving optimization and system design problems. This paper provides an alternate approach to analyzing SINRbased coverage probability using direct probability computation. We analyze a uniformly random wireless network as a test case and compare the analytical results with widely accepted frameworks in the SG literature. Our analytical derivations, validated through simulation studies, agree with well-known results in the literature. The developed approach provides the groundwork for coverage analysis in more complex network scenarios and channel conditions.

The deployment of fifth-generation (5G) networks across various industry verticals is poised to transform communication and data exchange, promising unparalleled speed and capacity. However, the security concerns related to the widespread adoption of 5G, particularly in mission-critical sectors, present significant challenges. This article investigates the potential of a Zero Trust (ZT) security philosophy as a viable countermeasure to these concerns. It delves into the practicalities of implementing ZT principles within 5G networks, with a specific focus on harnessing AI/ML technologies for proactive security measures, dynamic policy adaptations, and advanced risk assessments. Further, the article underscores the importance of developing a tailored ZT maturity model for 5G networks. Furthermore, the paper outlines key future research directions aimed at improving the ZT maturity of 5G deployments, contributing to the safe and secure integration of 5G technology in various sectors.

Modern fifth-generation (5G) networks are increasingly moving towards architectures characterized by softwarization and virtualization. This paper addresses the complexities and challenges in deploying applications and services in the emerging multi-tiered 5G network architecture, particularly in the context of microservices-based applications. These applications, characterized by their structure as directed graphs of interdependent functions, are sensitive to the deployment tiers and resource allocation strategies, which can result in performance degradation and susceptibility to failures. Additionally, the threat of deploying potentially malicious applications exacerbates resource allocation inefficiencies. To address these issues, we propose a novel optimization framework that incorporates a probabilistic approach for assessing the risk of malicious applications, leading to a more resilient resource allocation strategy. Our framework dynamically optimizes both computational and networking resources across various tiers, aiming to enhance key performance metrics such as latency, accuracy, and resource utilization. Through detailed simulations, we demonstrate that our framework not only satisfies strict performance requirements but also surpasses existing methods in efficiency and security.

Effective resource orchestration for network slicing is critical for optimizing the performance of diverse applications running on next generation communication networks. This paper presents a novel approach that leverages advancements in multiagent reinforcement learning (MARL) to adaptively learn the resource requirements of various applications in network slices and orchestrate resources in real-time. Our proposed MARL-based orchestration scheme aims to balance the varying requirements of individual network slices, ensuring optimal performance amid dynamic application deployments with limited network information. Simulation results and comparative analyses validate the efficiency and efficacy of our methodology, demonstrating its superiority over traditional methods in terms of system performance and resource utilization. Simulation results indicate that our strategy significantly enhances system utility and efficiency, particularly with limited resources.

The rapid evolution of autonomous vehicular technologies demands advanced solutions for reliable navigation in diverse and often unfamiliar environments. This paper introduces a novel CNN-based Federated Learning approach for vehicular systems, designed to enhance their self-driving adaptability and decision-making capabilities in novel environments through collaborative training. Our method leverages the decentralized nature of FL to enable vehicles to learn collectively from shared experiences while maintaining the privacy of individual data. We utilize the CARLA simulation environment to generate a wide range of driving scenarios, including multiple vehicles operating under varied weather conditions. This diverse dataset serves as a testbed for our FL framework, allowing us to evaluate its effectiveness in accurately predicting steering wheel angles while simultaneously adapting to different environmental challenges. Our results demonstrate that vehicles trained using our FL framework exhibit enhanced performance in predictive analytics, showing greater resilience to environmental changes and improved decision-making in real-time scenarios with a much lower computational complexity.

Modern 5G systems are not standalone systems that come from a single vendor or supplier. In fact, it comprises an integration of complex software, hardware, and cloud services that are developed by specialist entities. Moreover, these components have a supply chain that may have linkages and relationships between different vendors. A mobile network operator relies on the functionality and integrity of all the constituent components and their suppliers to ensure the communication network's confidentiality, integrity, and availability. While the operator can employ cybersecurity best practices itself, it does not have control over the cybersecurity practices of its immediate vendors and the wider supply chain. Recently, attackers have exploited cyber vulnerabilities in the supplier network to launch largescale breaches and attacks. Hence, the supply chain becomes a weak link in the overall cybersecurity of the 5G system. Hence, it is becoming crucial for operators to understand the cyber risk to their infrastructure, with a particular emphasis on the supply chain risk. In this paper, we systematically break down and analyze the 5G network architecture and its complex supply chains. We present an overview of the key challenges in the cybersecurity of 5G supply chains and propose a systemic cyber risk assessment methodology to help illuminate the risk sources and use it to manage and mitigate the risk. It will guide stakeholders in establishing a secure and resilient 5G network ecosystem, safeguarding the backbone of modern digital infrastructure against potential cybersecurity threats.

Unmanned aerial vehicles (UAVs) can enhance wireless access by dynamically positioning themselves closer to users while maintaining a backhaul connection to cellular base stations. In scenarios where users are geographically dispersed, multiple UAVs can be orchestrated to establish multi-hop integrated access and backhaul (IAB) connections. Traditionally, finding optimal UAV placement has required computationally demanding methods, such as combinatorial optimization or reinforcement learning, which are often impractical for real-time applications due to their complexity and training requirements. Additionally, UAV operators may lack the capability to solve complex optimization problems during live operations. This paper presents a novel framework that leverages large language models (LLMs) for optimizing the placement of multiple UAVs through iterative structured prompting. The proposed method achieves nearoptimal solutions in significantly fewer iterations compared to traditional methods, making it suitable for real-time deployment without extensive mathematical modeling. Through simulations, we demonstrate that this approach can efficiently manage UAV placements and ensure robust network connectivity and service quality. The LLM-driven framework simplifies problem-solving for UAV network operators, paving the way for its application in more complex real-world scenarios.