A Sub‐Optimum Algorithm for Turning on/Off Co‐Channel Access Points in Ultra‐Dense Networks

Abstract

ABSTRACT

This paper proposes a sub‐optimal Kuhn–Munkres‐based resource assignment algorithm to maximize both the number of connected links and the mean throughput per link in ultra‐dense networks (UDNs) consisting of densely distributed co‐channel access points (APs) and user equipment (UEs). The proposed seven‐step algorithm first assigns UEs to APs that provide higher data rates while accounting for the interference of all APs. Next, only the interference from the selected APs is considered to identify UEs that meet the minimum throughput threshold level. In subsequent steps, considering both the interference of previously assigned APs and the remaining candidate APs, additional UEs are connected. Simulation results in MATLAB for a $$$ 250\ \mathrm{m}\times 250\ \mathrm{m} $$$ service area with $$$ 250 $$$ randomly distributed APs and varying numbers of UEs (25–$$$ 250 $$$) demonstrate that the proposed algorithm achieves higher connectivity and total throughput with significantly reduced processing time compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Gray Wolf Optimization (GWO). Specifically, as the number of UEs increases from $$$ 10\% $$$ to $$$ 100\% $$$ of the number of APs, the proposed algorithm improves the number of connected UEs by $$$ 10\%-48\% $$$, $$$ 47\%-96\% $$$, $$$ 57\%-109\% $$$, and $$$ 22\%-58\% $$$, and the total throughput by $$$ 20\%-52\% $$$, $$$ 44\%-86\% $$$, $$$ 50\%-105\% $$$, and $$$ 22\%-69\% $$$, respectively, over the four benchmark algorithms. Moreover, owing to its lower computational complexity, the proposed method achieves at least $$$ 99\% $$$ reduction in processing time.