What is it about?
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.
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Why is it important?
Simulation results in MATLAB for a 250 m × 250 m service area with 250 randomly distributed APs and varying numbers of UEs (25 to 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 Grey 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.
Perspectives
A seven-step sub-optimal algorithm was proposed to address the non-convex NP-hard resource allocation problem in ultra-dense networks (UDNs) with densely distributed APs and UEs. Based on the Kuhn–Munkres (KM) assignment algorithm, the method was evaluated in a 250 m × 250 m area with randomly distributed 250 APs and 10-250 UEs. The simulation accounted for path loss, additive white Gaussian noise, multipath Rayleigh fading, Log-normal shadowing, and co-channel interference among AP–UE links sharing the same resources. Numerical results show that the proposed KM-based algorithm outperforms Genetic, PSO, CS, and GWO algorithms by achieving more connected UEs, higher total throughput, lower time complexity, and reduced processing time, demonstrating its efficiency for resource allocation in UDNs.
Dr. Shahriar Shirvani Moghaddam
Shahid Rajaee Teacher Training University
Read the Original
This page is a summary of: A Sub‐Optimum Algorithm for Turning On/Off Co‐Channel Access Points in Ultra‐Dense Networks, Engineering Reports, November 2025, Wiley,
DOI: 10.1002/eng2.70483.
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