Delay-Aware Link Scheduling in IAB Networks With Dynamic User Demands

• Published in: IEEE transactions on vehicular technology Vol. 73; no. 10; pp. 15125 - 15139
• Main Authors: Sadovaya, Yekaterina, Vikhrova, Olga, Mao, Wei, Yeh, Shu-ping, Semiari, Omid, Nikopour, Hosein, Talwar, Shilpa, Andreev, Sergey
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Backhaul networks; Cellular communication; Complexity; Constraints; Delays; Densification; Genetic algorithms; half-duplex constraint; IAB; interference; link scheduling; Machine learning; Millimeter waves; millimeter-wave; Multiplexing; Network topologies; Network topology; Nodes; Relay stations; Relaying; Routing; Scheduling; Search algorithms; Topology; Traffic delay; user dynamics; Wireless communication
• ISSN: 0018-9545; 1939-9359; 1939-9359
• DOI: 10.1109/TVT.2024.3409179

Integrated Access and Backhaul (IAB) is a cost-efficient network densification technology for improving the coverage and capacity of the millimeter-wave (mmWave) cellular networks. In IAB systems, user traffic is forwarded to/from the wired base station by one or more relay stations, known as IAB nodes. Due to the multi-hop relaying, these systems may be subject to large packet delays and poor performance when the load is unevenly distributed among nodes. Addressing this limitation via delay-aware access and backhaul link scheduling in IAB networks is challenging due to potentially large network scale, complex topology, half-duplex, and interference constraints. In this paper, the topical link scheduling problem is formulated as a Markov decision problem (MDP) for a single-donor IAB system with a general topology that allows for users with different delay requirements and traffic dynamics. The proposed link scheduling strategy jointly optimizes (i) user traffic routing and (ii) multiplexing of access and backhaul links under half-duplex constraints and non-negligible interference that may arise in dense IAB systems even with high beam directionality. To address the complexity of our formulated MDP, we consider several approximation methods, namely, Q-learning, Monte Carlo Tree Search (MCTS), and genetic algorithms (GAs). Then, we propose a customized version of the GA, which provides the preferred optimality-complexity trade-off and offers a 15% packet delay reduction as compared to the state-of-the-art backpressure algorithm.