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.

The blockage of the propagation path is one of the major challenges preventing the deployment of fifth-generation New Radio systems in the millimeter-wave band. To address this issue, the Integrated Access and Backhaul technology has been proposed as a cost-effective solution for increasing the density of access networks. These systems are designed with the goal of avoiding blockages, leaving the question of providing quality-of-service guarantees aside. However, the use of multi-hop transmission negatively impacts the end-to-end packet latency. In this work, motivated by the need for latency reduction, we design a new link activation policy for self-backhauled Integrated Access and Backhaul systems operating in half-duplex mode. The proposed approach utilizes dynamic queue prioritization based on the number of packets that can be transmitted within a single time slot, enabling more efficient use of resources. Our numerical results show that the proposed priority-based algorithm performs better than existing link scheduling methods for typical system parameter values.

The Peak Age of Information (PAoI) quantifies the freshness of updates used in cyber-physical systems (CPSs), realized within the Internet of Things (IoT) paradigm, encompassing devices, networks, and control algorithms. Consequently, PAoI is a critical metric for real-time applications enabled by Ultra-Reliable Low Latency Communication (URLLC). While highly useful for system evaluation, the direct analysis of this metric is complicated by the correlation between the random variables constituting the PAoI. Thus, it is often evaluated using only the mean value rather than the full distribution. Furthermore, since CPS communication technologies like Wi-Fi or DECT-2020 involve multiple processing stages, modeling them as tandem queueing systems is essential for accurate PAoI analysis. In this paper, we develop an analytical model for a DECT-2020 network segment represented as a two-phase tandem queueing system, enabling detailed PAoI analysis via Laplace–Stieltjes transforms (LST). We circumvent the dependence between generation and sojourn times by classifying updates into four mutually exclusive groups. This approach allows us to derive the LST of the PAoI and determine the exact Probability Density Function (PDF) for $M\left|M\right|1\to M\left|M\right|1$ system. We also calculate the mean and variance of the PAoIs and validate our results through numerical experiments. Additionally, we evaluate the impact of different service time distributions on PAoI variability. These findings contribute to the theoretical understanding of PAoI in tandem queueing systems and provide practical insights for optimizing DECT-2020-based communication systems.