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

TL;DR

This paper addresses resource allocation in UAV-assisted integrated terrestrial and non-terrestrial networks with multi-connectivity, proposing algorithms to optimize capacity and fairness amid mobility and QoS challenges.

Contribution

It introduces two novel algorithms for resource sharing in UAV-based networks, balancing capacity maximization and fairness under QoS constraints.

Findings

Algorithms improve network capacity and fairness.

Simulation results validate effectiveness of proposed methods.

Enhanced reliability in UAV communication links.

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