Hybrid Satellite-UAV-Terrestrial Networks for 6G Ubiquitous Coverage: A Maritime Communications Perspective

TL;DR

This paper proposes a hierarchical satellite-UAV-terrestrial network for maritime communications in 6G, optimizing link scheduling and rate adaptation to enhance coverage, reduce energy consumption, and operate efficiently with large-scale CSI.

Contribution

It introduces a novel hybrid network architecture with a joint link scheduling and rate adaptation scheme tailored for maritime 6G communications, using large-scale CSI for practical deployment.

Findings

Achieves near-optimal performance with reduced complexity.

Provides agile, on-demand maritime coverage.

Reduces system overhead significantly.

Abstract

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. Current maritime communication networks (MCNs) mainly rely on marine satellites and on-shore base stations (BSs). The former generally provides limited transmission rate, while the latter lacks wide-area coverage capability. Due to these facts, the state-of-the-art MCN falls far behind terrestrial fifth-generation (5G) networks. To fill up the gap in the coming sixth-generation (6G) era, we explore the benefit of deployable BSs for maritime coverage enhancement. Both unmanned aerial vehicles (UAVs) and mobile vessels are used to configure deployable BSs. This leads to a hierarchical satellite-UAV-terrestrial network on the ocean. We address the joint link scheduling and rate adaptation problem for this hybrid network, to minimize the total energy consumption with quality of service (QoS) guarantees. Different from previous studies, we use only the large-scale channel state information (CSI), which is location-dependent and thus can be predicted through the position information of each UAV/vessel based on its specific trajectory/shipping lane. The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. We solve it suboptimally by using Min-Max transformation and iterative problem relaxation, leading to a process-oriented joint link scheduling and rate adaptation scheme. As observed by simulations, the scheme can provide agile on-demand coverage for all users with much reduced system overhead and a polynomial computation complexity. Moreover, it can achieve a prominent performance close to the optimal solution.