A Fast Sub-Optimum Access Point Selection in Ultra-Dense Networks

Abstract

Solving the main optimization problem, including a large number of access points and user equipment in ultra-dense networks (UDNs) using the Munkres algorithm, is a time-consuming solution. In this paper, we propose two new ideas for selecting the appropriate access points (APs) in UDNs that reduce the complexity order and find the suboptimum solution efficiently. Applying the first idea, all user equipment (UE) that are out of access points’ service area and all access points that cannot find any user equipment in their service area are removed. In this case, the solutions of the conventional and proposed version of the Munkres algorithm are the same, which offers a high total sum value of the signal to noise ratios (SNRs). Still, it does not guarantee minimum interference. Hence, as the second idea, we consider estimation of the signal to interference plus noise ratio (SINR), entitled by average-SINR and random-SINR, and solve the optimization problem, including a large number of access points and a variable number of user equipment densely distributed. The simulation results show the effectiveness of the proposed algorithms in the view of sum-rate, the number of successful user equipment, and computational complexity for an area, including 250 APs and a variable number of UEs.