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

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 K-GA 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. INDEX TERMS 5G, backhaul network capacity, gateway location problem, heuristic, machine learning, small cells, ultra-dense networks.

Optimal Gateway Placement for Minimizing Intersatellite Link Usage in LEO Megaconstellation Networks

“…In the field of ground wireless networks, there have been many studies on the gateway placement [16]- [19]. Previous studies have investigated the gateway placement or deployment in different scenarios such as Wireless Mesh Network (WMN) [16], ground IoT [17], [18], and 5G ultra-dense networks [19].…”

“…In the field of ground wireless networks, there have been many studies on the gateway placement [16]- [19]. Previous studies have investigated the gateway placement or deployment in different scenarios such as Wireless Mesh Network (WMN) [16], ground IoT [17], [18], and 5G ultra-dense networks [19]. The general ideas are similar, i.e., to formulate a combination optimization or integer programming model to select the optimal gateway placement from the candidate gateway set.…”

“…The computation complexity can be divided into two parts: i) computation for PSO optimization; and ii) computation for evaluating the objective function value. For the first part, the computational complexity is O(M N KI + N pop N iter ) [19]. The K-means takes O(M N KI ) to obtain the best clustering result, where M is the number of candidate sites and N KI is the iteration number of the K-means algorithm.…”

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“…It is possible to determine video surveillance traffic in a straightforward and accurate manner. For the sake of our application, a 4 Mbps stream is a realistic assumption [12][13][14]. It is necessary to calculate the required bandwidth by taking into account the combined speed of all video streams.…”

Wireless link scheduling via parallel genetic algorithm

“…This was motivated by the GA's ability to solve complex engineering problems, and its ability to produce global optimal solutions 15 . See, for example, References 16‐18. The contributions of the GA in Reference 14 can be summarized as follows.…”