Joint UAV Deployment and Resource Allocation in THz-Assisted MEC-Enabled Integrated Space-Air-Ground Networks

TL;DR

This work tackles energy-efficient computation in THz-assisted MEC-enabled integrated SAG networks by jointly optimizing device/UAV offloading, THz sub-band allocation, transmit power, and UAV deployment. It introduces a non-convex MILP and solves it with a block coordinate descent framework that decomposes into device offloading, sub-band matching with CCP-based power control, UAV deployment via SCA, and UAV task offloading via BSUM. The approach leverages a one-to-one matching game for sub-band allocation, CCP for power control, and proximal BSUM for discrete UAV decisions, with convergence demonstrated through simulations. Results show significant energy savings compared to All local and No UAVs collaboration baselines, validating the practicality of THz SAG MEC with UAV collaboration for remote-area computing.

Abstract

Multi-access edge computing (MEC)-enabled integrated space-air-ground (SAG) networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). Leveraging the ample bandwidth in the terahertz (THz) spectrum, in this paper, we propose MEC-enabled integrated SAG networks with collaboration among unmanned aerial vehicles (UAVs). We then formulate the problem of minimizing the energy consumption of devices and UAVs in the proposed MEC-enabled integrated SAG networks by optimizing tasks offloading decisions, THz sub-bands assignment, transmit power control, and UAVs deployment. The formulated problem is a mixed-integer nonlinear programming (MILP) problem with a non-convex structure, which is challenging to solve. We thus propose a block coordinate descent (BCD) approach to decompose the problem into four sub-problems: 1) device task offloading decision problem, 2) THz sub-band assignment and power control problem, 3) UAV deployment problem, and 4) UAV task offloading decision problem. We then propose to use a matching game, concave-convex procedure (CCP) method, successive convex approximation (SCA), and block successive upper-bound minimization (BSUM) approaches for solving the individual subproblems. Finally, extensive simulations are performed to demonstrate the effectiveness of our proposed algorithm.

Figures (8)

• Figure 1: Illustration of MEC-enabled integrated space-air-ground networks.
• Figure 2: Energy consumption vs. number of devices for proposed, local computing only and without UAVs collaboration.
• Figure 3: Energy consumption as a function of the number of devices for variants of the proposed scheme.
• Figure 4: Energy consumption as a function of the number of UAVs for $J= 20, 40, 60$ and $80$.
• Figure 5: Energy consumption for $J=60$ devices under different average data sizes.