Blue Data Computation Maximization in 6G Space-Air-Sea Non-Terrestrial Networks

TL;DR

This paper proposes an algorithm for efficient blue data computation maximization in 6G space-air-sea non-terrestrial networks, focusing on maritime communication and offloading tasks to satellites and UAVs.

Contribution

It introduces a joint task computation and resource allocation framework with a novel MILP solution using Bender and primal decomposition methods.

Findings

Algorithm achieves near-optimal solutions

Provides polynomial time computational complexity

Ensures reliable maritime network coverage

Abstract

Non-terrestrial networks (NTN), encompassing space and air platforms, are a key component of the upcoming sixth-generation (6G) cellular network. Meanwhile, maritime network traffic has grown significantly in recent years due to sea transportation used for national defense, research, recreational activities, domestic and international trade. In this paper, the seamless and reliable demand for communication and computation in maritime wireless networks is investigated. Two types of marine user equipment (UEs), i.e., low-antenna gain and high-antenna gain UEs, are considered. A joint task computation and time allocation problem for weighted sum-rate maximization is formulated as mixed-integer linear programming (MILP). The goal is to design an algorithm that enables the network to efficiently provide backhaul resources to an unmanned aerial vehicle (UAV) and offload HUEs tasks to LEO satellite for blue data (i.e., marine user's data). To solve this MILP, a solution based on the Bender and primal decomposition is proposed. The Bender decomposes MILP into the master problem for binary task decision and subproblem for continuous-time resource allocation. Moreover, primal decomposition deals with a coupling constraint in the subproblem. Finally, numerical results demonstrate that the proposed algorithm provides the maritime UEs coverage demand in polynomial time computational complexity and achieves a near-optimal solution.