This paper proposes a sub-optimum Kuhn-Munkres-based resource allocation algorithm to maximize the number of connected links and total throughput served by ultra-dense networks consisting of densely distributed co-channel access points and user equipment. In the proposed seven-step algorithm, 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. Afterward, considering the interference of the access points assigned in the first run and the remaining access points selected in the next runs, new users are connected to the remaining selected access points. Simulations in MATLAB for a service area of 250 meters by 250 meters including randomly distributed 250 access points and different numbers of 25 to 250 users, show more connected users and total throughput, and much less processing time of the proposed algorithm than those for Genetic, particle swarm optimization, cuckoo search, and grey wolf optimization algorithms. By changing the number of users from 10% to 100% of the number of access points, the proposed algorithm increases the number of connected users by 10% to 48%, 47% to 96%, 57% to 109%, and 22% to 58%, and the total throughput by 20% to 52%, 44% to 86%, 50% to 105%, and 22% to 69% compared to the four mentioned algorithms. Due to the lower complexity order, it experiences at least 99% less processing time.