Abstract
Resource allocation stands out as one of the most critical tasks in wireless communication systems. To efficiently service many users with various network requirements, algorithms used in these systems must become more intelligent and dynamic, utilizing developing wireless technologies and techniques. Resource distribution encounters several challenges such as interference alignment issues, security flaws, and the need of employing ecologically friendly communication techniques. Wireless technology users, devices, and associated systems struggle with resource limitations, highlighting the significance of their equitable and efficient distribution while aiming for optimal network performance. The Ultra-Dense Network (UDN) design is expected to play a crucial role with the upcoming introduction of the fifth generation (5G) of mobile communication systems, especially in high-traffic areas and wireless blind spots. In this context, energy and spectrum are two crucial factors. To achieve a balance between these parameters, this study proposes an improved iteration of the Modified Crossover Genetic Algorithm (MCGA)-based methodology. This approach takes into account the current comprehensive search and weighted sum methods. The proposed method equips small cell users in 5G UDNs to maximize their effectiveness by carefully allocating transmission power and resource components. Our proposal is compared to existing solutions through thorough simulations, showing a significant increase in efficiency. The research also explores the suggested method's convergence qualities and computational cost, offering valuable insights into its applicability and performance.