Load Balancing for Integrated Access and Backhaul in mmWave Small Cells

• Published in: IEEE access Vol. 11; pp. 138664 - 138674
• Main Authors: Tran, Quang-Huy, Duong, Tho-Minh, Kwon, Sungoh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G advanced; Algorithms; Backhaul networks; Downlink; graph theory; Graphical representations; Installation costs; integrated access and backhaul; Load balancing; Load management; millimeter wave; Millimeter wave communication; Millimeter waves; Radio equipment; Resource management; Spread spectrum communication; Unbalance; Wireless communication
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3338567

In this paper, we propose a load-balancing algorithm for small-cell integrated access and backhaul (IAB) networks operating in the millimeter wave (mmWave) band. With the help of mmWave communications, ultra-dense small cell deployment is a key technology for future networks, but it leads to high fiber installation costs. IAB has emerged as a cost-effective and flexible solution because it uses the multi-hop wireless backhaul to the core network via macro base stations (BSs). Multi-hop transmission and spectrum sharing between access and backhaul links cause an unbalanced load across BSs under IAB. To tackle the unbalanced load problem, we propose a graph-based load balancing algorithm for IAB by estimating and adapting to network load status. 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. The proposed algorithm estimates the upcoming load and adjusts the network transmission schedule to balance the load inside the network. Through simulations, performance is evaluated in various environments. The simulation results show that the proposed algorithm not only distributes the load across small cells more evenly but also increases network throughput by 22.3% and 32.1% with 16.3% and 28.7% higher in UEs satisfaction when compared to MaxMinThroughput and Backpressure algorithms.