  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.
