A Survey of Delay-Oriented Dynamic Link Scheduling Policies for 5G/6G Integrated Access and Backhaul Systems

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Abstract
Integrated Access and Backhaul (IAB) is expected to become a key part of the 5G and future cellular systems enabling cost-controlled network densification. IAB relies upon multi-hop relaying, which makes delay a critical factor, especially for the half-duplex operational regime where only a subset of access and backhaul links can be scheduled simultaneously. Numerous dynamic link scheduling policies were developed in the past for different applications, backpressure being the best known example. Each such policy offers its own balance of supported network throughput, computational complexity and end-to-end delay performance. The latter metric is the least investigated in the literature and is the most important for multi-hop 5G IAB systems. The aim of this paper is to revisit and compare these techniques in the context of 5G/6G IAB and with a particular focus on delay. To this end, we formalize the IAB system as a queuing network, present a taxonomy and a systematic review of link scheduling policies applied to this model, and compare the performance of selected policies. Our numerical results identify several policies consistently outperforming the others in terms of delay and coping well with imbalanced uplink and downlink hardware limitations and traffic loads. However, these policies differ substantially in their throughput and complexity characteristics, which determines the provided recommendations as to their application for IAB control. Finally, we also demonstrated that supplementing these policies with work-conserving operation that allows to transmit packets of low priority flows when the link capacity is not fully utilized, provides additional performance gains.
Funding Information
  • Russian Science Foundation (23-79-01140)
  • Grant for Research Centers in the field of AI provided by the Analytical Center for the Government of the Russian Federation (000000D730321P5Q0002)
  • Higher School of Economics, National Research University (70-2021-00139)
  • Russian Science Foundation (23-79-01140)
  • Grant for Research Centers in the field of AI provided by ACRF (000000D730321P5Q0002)
  • National Research University Higher School of Economics (70-2021-00139)

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