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

This paper proposes a sub-optimum Kuhn-Munkres-based algorithm for resource allocation in ultra-dense networks, which aims to maximize the number of connected users and throughput. In the proposed method, users are first assigned to access points supporting higher data 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 $\mathbf{2 5 0}$ meters by $\mathbf{2 5 0}$ meters including $\mathbf{2 5 0}$ access points and different numbers of 10 to 250 randomly distributed users, show more connected users, the total sum-rate (throughput), and the average throughput per user while the processing time is far lower than the two other simulated ones, Genetic and particle swarm optimization algorithms. Also, where the number of users and access points are the same and equal to 250, the proposed algorithm compared to the two other optimization algorithms increases the number of connected users by 14% and 54%, the average throughput per user by 55% and $\mathbf{3 0} \%$, and the total throughput by 77% and 163%. Due to the lower order of complexity of the proposed algorithm compared to the two others, it experiences a lower processing time of 99.97 and 99.96 percent, respectively.