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UAV/HAP-Assisted Vehicular Edge Computing in 6G: Where and What to Offload?

Alessandro Traspadini, Marco Giordani, Michele Zorzi

TL;DR

The paper addresses real-time VEC in 6G by offloading GV sensor workloads to aerial NTN platforms (UAVs and HAPs). It develops a formal optimization over the offloading fraction $\eta$, integrating a Poisson-arrival model with an M/D/c queuing framework to minimize total processing time under latency and capacity constraints. Key findings show that HAP-enabled VEC can outperform local processing in many scenarios, and hybrid UAV/HAP offloading yields further gains, though achieving sub-100 ms frame-times requires substantial HAP capacity and multi-server deployments. The work provides design insights for dimensioning aerial computing resources and highlights the trade-offs between link latency, edge processing, and hardware costs in 6G V2X deployments.

Abstract

In the context of 6th generation (6G) networks, vehicular edge computing (VEC) is emerging as a promising solution to let battery-powered ground vehicles with limited computing and storage resources offload processing tasks to more powerful devices. Given the dynamic vehicular environment, VEC systems need to be as flexible, intelligent, and adaptive as possible. To this aim, in this paper we study the opportunity to realize VEC via non-terrestrial networks (NTNs), where ground vehicles offload resource-hungry tasks to Unmanned Aerial Vehicles (UAVs), High Altitude Platforms (HAPs), or a combination of the two. We define an optimization problem in which tasks are modeled as a Poisson arrival process, and apply queuing theory to find the optimal offloading factor in the system. Numerical results show that aerial-assisted VEC is feasible even in dense networks, provided that high-capacity HAP/UAV platforms are available.

UAV/HAP-Assisted Vehicular Edge Computing in 6G: Where and What to Offload?

TL;DR

The paper addresses real-time VEC in 6G by offloading GV sensor workloads to aerial NTN platforms (UAVs and HAPs). It develops a formal optimization over the offloading fraction , integrating a Poisson-arrival model with an M/D/c queuing framework to minimize total processing time under latency and capacity constraints. Key findings show that HAP-enabled VEC can outperform local processing in many scenarios, and hybrid UAV/HAP offloading yields further gains, though achieving sub-100 ms frame-times requires substantial HAP capacity and multi-server deployments. The work provides design insights for dimensioning aerial computing resources and highlights the trade-offs between link latency, edge processing, and hardware costs in 6G V2X deployments.

Abstract

In the context of 6th generation (6G) networks, vehicular edge computing (VEC) is emerging as a promising solution to let battery-powered ground vehicles with limited computing and storage resources offload processing tasks to more powerful devices. Given the dynamic vehicular environment, VEC systems need to be as flexible, intelligent, and adaptive as possible. To this aim, in this paper we study the opportunity to realize VEC via non-terrestrial networks (NTNs), where ground vehicles offload resource-hungry tasks to Unmanned Aerial Vehicles (UAVs), High Altitude Platforms (HAPs), or a combination of the two. We define an optimization problem in which tasks are modeled as a Poisson arrival process, and apply queuing theory to find the optimal offloading factor in the system. Numerical results show that aerial-assisted VEC is feasible even in dense networks, provided that high-capacity HAP/UAV platforms are available.
Paper Structure (18 sections, 20 equations, 5 figures, 1 table)

This paper contains 18 sections, 20 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation
  4. Delay Model
  5. Local processing
  6. VEC-assisted processing
  7. Channel Model
  8. Queuing Model
  9. Optimal Offloading for NTN-Assisted VEC
  10. Standalone Offloading
  11. Hybrid Offloading
  12. Performance Evaluation
  13. Simulation Setup and Parameters
  14. Numerical Results
  15. Users density
  16. ...and 3 more sections

Figures (5)

  • Figure 1: System model for NTN-assisted VEC.
  • Figure 2: Total processing time for different offloading schemes, vs. $C_{\rm GV}$ and $k$.
  • Figure 3: Offloading factor for different offloading schemes, vs. $C_{\rm GV}$ and $k$.
  • Figure 4: Total processing time for different offloading schemes and $k=200$, vs. $C_{\rm HAP}$ and $n_{\rm UL}$.
  • Figure 5: Total processing time for different offloading schemes, vs. the UAV (up) and HAP (down) computational capacity, for $k=200$.