A Fast Heuristic for Gateway Location in Wireless Backhaul of 5G Ultra-Dense Networks

TL;DR

This paper introduces a novel K-GA heuristic combining Genetic Algorithm and K-means clustering to efficiently locate gateways in 5G ultra-dense network backhaul, significantly improving capacity and reducing computation time.

Contribution

The paper presents a new heuristic method for gateway placement in 5G backhaul networks that outperforms existing approaches in speed and near-optimal capacity.

Findings

K-GA achieves within 2% of optimal network capacity.

K-GA reduces execution time by 95% compared to exact methods.

The approach performs well across various user distribution scenarios.

Abstract

In 5G Ultra-Dense Networks, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core. The macro-cell coverage area is divided into many small cells. A few of these cells are designated as gateways and are linked to the core by high-capacity fiber optic links. Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks. We investigate the gateway location problem and show that finding near-optimal gateway locations improves the backhaul network capacity. An exact p-median integer linear program is formulated for comparison with our novel K-GA heuristic that combines a Genetic Algorithm (GA) with K-means clustering to find near-optimal gateway locations. We compare the performance of KGA with six other approaches in terms of average number of hops and backhaul network capacity at different node densities through extensive Monte Carlo simulations. All approaches are tested in various user distribution scenarios, including uniform distribution, bivariate Gaussian distribution, and cluster distribution. In all cases K-GA provides near-optimal results, achieving average number of hops and backhaul network capacity within 2% of optimal while saving an average of 95% of the execution time.