Load Balancing in 5G mmWave small cells for Integrated Access and Backhaul

Abstract

Millimeter wave (mmWave) communication is a promising technique for keeping up with the exponential growth of next-generation networks, such as 5G, 5G advanced, and beyond future networks. To utilize the mmWave spectrum, these networks need a dense deployment of base stations (BSs) but it leads to high costs in wired infrastructure. Therefore, IAB has emerged as a cost-effective and flexible solution because it uses the multi-hop wireless backhaul to the core network via macro BS. However, multi-hop transmission and spectrum sharing between access and backhaul links cause an unbalanced load across BSs in the IAB network. In this thesis, we investigate the unbalanced load problem in the IAB network and propose a graph-based scheduling algorithm that increases the network throughput and achieves load distribution effectively. In the proposed algorithm, the weight of the graph is represented as the cost of the link between two BSs and is defined to reflect the cell load and link capacity. By utilizing the weight, we apply maximum weight-matching in graph optimization to estimate the forthcoming load and adjust the network transmission schedule, thereby achieving load balancing within the network. To validate the effectiveness of our proposed algorithm, we compare it with other algorithms in various environments through simulations. The results indicate that the proposed algorithm not only distributes the load across small cells more evenly but also low packet loss, increases network throughput, and improves the quality of experience at UEs. Keywords: 5G mmWave, Integrated access and backhaul, Load balancing, Scheduling, Graph Theory