International Symposium on Telecommunication (IST)

Home / 11th International Symposium on Telecommunication (IST'2024)

Kuhn-Munkres-Based Sub-optimum Resource Allocation Algorithm for Ultra-Dense Networks

Authors :

Shahriar Shirvani Moghaddam¹ Ebrahim Ashoor²

1- Shahid Rajaee Teacher Training University 2- Shahid Rajaee Teacher Training University

Keywords :

Ultra Dense Network (UDN)،Resource allocation،Kuhn-Munkres algorithm،Genetic Algorithm (GA)،Particle Swarm Optimization (PSO) algorithm،Sum-rate،Throughput،Time complexity order

Abstract :

In this paper, a suboptimum solution based on Kuhn-Munkres algorithm is proposed for resource allocation in ultra dense networks, which aims to maximize the throughput and the number of connected users. In the proposed method, users are first allocated to access points with the possibility of providing higher rates considering the interference of all access points. Then, only the interference of the selected access points is considered and users connected to these access points that meet the minimum throughput threshold level are found. In the second step, considering the interference of the access points assigned in the first step and the remaining access points selected in the second step, new users are connected to the remaining selected access points. Simulations in MATLAB and numerical analyses for an area of 250 meters by 250 meters including 250 access points and different numbers of 10 to 250 randomly distributed users, show the higher values of the number of connected users, the average throughput per user, and the total sum-rate (throughput) while the processing time is far lower than the two other simulated ones, Genetic and particle swarm optimization algorithms. In the case where the number of users and access points are the same and equal to 250, the proposed algorithm compared to the Genetic and particle swarm optimization algorithms increases the number of connected users by 14% and 54%, the average throughput per user by 55% and 30%, and the total throughput by 77% and 163%. Due to the lower order of complexity of the proposed algorithm compared to the two other ones, it experiences a lower processing time of 99.97 and 99.96 percent, respectively.