Integrated Access and Backhaul (IAB) in Low Altitude Platforms

TL;DR

This paper addresses backhaul bottlenecks in UAV-based non-terrestrial networks by introducing an IAB-enabled two-layer UAV LAP architecture that shares spectrum between aerial access, aerial backhaul, and terrestrial links. The authors formulate a non-convex sum-rate maximization and decouple it into a hybrid beamforming subproblem and a spectrum allocation subproblem, solving the beamforming via a near-zero-forcing approximation and alternating optimization, and deriving a closed-form bandwidth split $\mu_a^* = \frac{R_t}{R_t+R_b}$. They further account for bandwidth-dependent thermal noise using SCA, enabling a realistic bandwidth-noise trade-off; simulations demonstrate substantial gains over non-IAB baselines, with the closed-form solution closely matching genetic-algorithm benchmarks. The results show IAB improves both sum-rate and energy efficiency, and the framework provides insights into backhaul-aware bandwidth allocation for aerial backhaul in mmWave NTN systems. Overall, the work highlights the practicality of IAB for UAV-based backhaul in future 6G-like networks and points to ML-enabled, more realistic scenarios as future work.

Abstract

In this paper, we explore the problem of utilizing Integrated Access and Backhaul (IAB) technology in Non-Terrestrial Networks (NTN), with a particular focus on aerial access networks. We consider an Uncrewed Aerial Vehicle (UAV)-based wireless network comprised of two layers of UAVs: (a) a lower layer consisting a number of flying users and a UAV Base Station (BS) that provides coverage for terrestrial users and, (b) an upper layer designated to provide both wireless access for flying users and backhaul connectivity for UAV BS. By adopting IAB technology, the backhaul and access links collaboratively share their resources, enabling aerial backhauling and the utilization of the same infrastructure and frequency resources for access links. A sum-rate maximization problem is formulated by considering aerial backhaul constraints to optimally allocate the frequency spectrum between aerial and terrestrial networks. We decompose the resulting non-convex optimization problem into two sub-problems of beamforming and spectrum allocation and then propose efficient solutions for each. Numerical results in different scenarios yield insightful findings about the effectiveness of using the IAB technique in aerial networks.

Figures (13)

• Figure 1: Three stages of IAB Workflow.
• Figure 2: Aerial Integrated Access and Backhaul (IAB) enabled system model serving two sets of aerial and terrestrial users.
• Figure 3: In a $2.25~\textrm{km}^2$ area, both aerial and terrestrial UEs are randomly distributed according to a normal distribution. They satisfy a minimum specified distance from each other, as detailed in shehzad2021backhaul, and are within defined sub-regions. All aerial UEs are located at the same altitude.
• Figure 4: Convergence of hybrid beamforming (HBF) Algorithm \ref{['al:1']} based on the different initialization methods.
• Figure 5: Convergence of Successive Convex Approximation (SCA) Algorithm \ref{['al:2']}.