Mohammad AHMED

This work introduces the framework for selecting architecture in 5G networks, considering various technological, performance, economic, and operational factors. With the emergence of 5G technology, the architecture selection process has become pivotal in meeting diverse requirements for ultra-high-speed connectivity, low latency, scalability, and diverse service demands. The evaluation comprehensively analyses different architecture options, including centralized, distributed, cloud-based, and virtualized architectures. Factors such as network performance, scalability, cost-effectiveness, security, and compatibility are considered within a multi-criteria decision-making framework. Findings reveal each architecture option's diverse strengths and limitations, emphasizing the importance of aligning architectural choices with specific use cases, deployment scenarios, and business objectives. We used the concept of multi-criteria decision-making (MCDM) methodology to control the various criteria. We used the single-valued neutrosophic set (SVNS) to deal with vague and inconsistent data. The SVNS is integrated with the hyperSoft set. The proposed methodology uses a single-valued neutrosophic hyperSoft set (SVNHSS) to select the best 5G architecture. We used the VIKOR method as an MCDM method to rank the alternatives. The proposed framework provides stakeholders with a structured methodology to evaluate and prioritize architecture options, facilitating informed decision-making in the complex landscape of 5G network deployments.

The question posed is whether the optimization methods of calculus that are often used in social and scientific analyses offer an appropriate analytical approach to analyze problems that are immersed in systemic complexity and its consequences. This paper refers to the portfolio of such complex problems belonging to social and scientific forces. We refer to such a complex combination by the term ‘socio-scientific’. In the study of socio-scientific complexity, dynamic preferences, intricate decisions, and uncertain behavior, endogenous relations and systemic perturbations abound. The arguments presented in this paper establish that the methodological approach of optimization and steady-state equilibrium turns out to be nicety rather than objectivity in the presence of complexity. Complex situations of the socio-scientific universe cause perturbations in the variables; there explaining complexity formed by the social embedding of variables. Indeed, human individuals, institutions and governments, and society at large are complex interrelated entities. Therefore, complex interrelations caused by social embedding remain submerged in social perturbations. However, interactions arising from social embedding also generate endogenous and complex relations. The contribution of this paper is in the area of endogenous learning in the wellbeing objective criterion function. The interrelationships between the emergent cognitive variables now cause interactive embedding, complexity, and social perturbations. Yet such perturbations are not altogether uncontrollable. They can be and need to be controlled for the purposes of social explanation—hence demanding predictability—even as the complex nature of evolutionary systemic learning proceeds. The controllability problem of extreme perturbations leads us to formalize a mathematical methodology to study controllable perturbations while avoiding extreme forms of perturbations in socio-scientific theory