Seamless and Energy Efficient Maritime Coverage in Coordinated 6G Space-Air-Sea Non-Terrestrial Networks

TL;DR

This paper proposes an energy-efficient resource allocation framework for space-air-sea non-terrestrial networks in 6G, optimizing user association, power control, and UAV deployment to enhance system energy efficiency.

Contribution

It introduces a novel optimization algorithm combining Bender decomposition, Dinkelbach method, and ADMM for energy-efficient resource management in SAS-NTNs.

Findings

The proposed algorithm outperforms baseline methods in energy efficiency.

Simulation results validate the effectiveness of the optimization approach.

The method efficiently balances UAV energy costs with system performance.

Abstract

Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the high mobility and deployment of NTNs, managing the space-air-sea (SAS) NTN resources, i.e., energy, power, and channel allocation, is a major challenge. The design of a SAS-NTN for energy-efficient resource allocation is investigated in this study. The goal is to maximize system energy efficiency (EE) by collaboratively optimizing user equipment (UE) association, power control, and unmanned aerial vehicle (UAV) deployment. Given the limited payloads of UAVs, this work focuses on minimizing the total energy cost of UAVs (trajectory and transmission) while meeting EE requirements. A mixed-integer nonlinear programming problem is proposed, followed by the development of an algorithm to decompose, and solve each problem distributedly. The binary (UE association) and continuous (power, deployment) variables are separated using the Bender decomposition (BD), and then the Dinkelbach algorithm (DA) is used to convert fractional programming into an equivalent solvable form in the subproblem. A standard optimization solver is utilized to deal with the complexity of the master problem for binary variables. The alternating direction method of multipliers (ADMM) algorithm is used to solve the subproblem for the continuous variables. Our proposed algorithm provides a suboptimal solution, and simulation results demonstrate that the proposed algorithm achieves better EE than baselines.