Resource Allocation and Sharing for UAV-Assisted Integrated TN-NTN with Multi-Connectivity

TL;DR

This work tackles spectrum and power allocation for MC-enabled UAVs in an integrated TN-NTN network under mobility and heterogeneous QoS. It introduces a two-step design: (i) per-pair power optimization with reliability constraints and (ii) a Hungarian-method-based spectrum-sharing matching, followed by a max-min fairness extension. The authors propose two algorithms—one maximizing sum capacity and another maximizing the minimum HCU capacity—and demonstrate, via simulations, that MC-based resource sharing significantly outperforms baselines while providing a controllable trade-off between throughput and fairness. The results suggest practical benefits for high-throughput mission data and critical-coordination scenarios in dynamic UAV deployments, with clear avenues for future extensions to more complex interference and mobility models.

Abstract

Unmanned aerial vehicles (UAVs) with multi-connectivity (MC) capabilities efficiently and reliably transfer data between terrestrial networks (TNs) and non-terrestrial networks (NTNs). However, optimally sharing and allocating spectrum and power resources to maintain MC while ensuring reliable connectivity and optimal performance remains challenging in such networks. Channel variations induced by mobility in UAV networks, coupled with the varying quality of service (QoS) demands of heterogeneous devices, resource sharing, and fairness requirements in capacity distribution pose challenges to optimal resource allocation. Thus, this paper investigates resource allocation for QoS-constrained, MC-enabled, dynamic UAVs in an integrated TN-NTN environment with spectrum sharing and fairness considerations. To this end, we consider three types of links: UAV-to-radio base station (RBS), UAV-to-UAV, and UAV-to-HAP. We also assume two types of UAVs with diverse QoS requirements to reflect a practical scenario. Consequently, we propose two algorithms. The first algorithm maximizes the capacity of UAVs-RBS and UAVs-HAP links while ensuring the reliability of the UAV-UAV link. To achieve this, the algorithm maximizes the collective throughput of the UAVs by optimizing the sum capacity of all the UAV-RBS and UAV-HAP links. Next, to provide constant capacity to all links and ensure fairness, we propose another algorithm that maximizes the minimum capacity across all links. We validate the performance of both algorithms through simulation

Figures (9)

• Figure 1: System model of the UAV-assisted integrated TN-NTN.
• Figure 2: The capacity performance comparison with different $J/I$ ratios.
• Figure 3: The CDFs of the instantaneous system performances, when $P_{m}^{l} = P_{m}^{h} = 22$ dBm and $P_o = 0.01$.
• Figure 4: The impact of the LCU outage probability.
• Figure 5: The impact of the UAV speed.