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UAV-enabled Computing Power Networks: Design and Performance Analysis under Energy Constraints

Yiqin Deng, Zhengru Fang, Senkang Hu, Yanan Ma, Xiaoyu Guo, Haixia Zhang, Yuguang Fang

TL;DR

This work addresses latency-sensitive computing over distributed resources by introducing UAV-enabled Computing Power Networks (UAV-CPNs), where a UAV dynamically accesses ubiquitous computing nodes to mitigate the island effect. The authors develop a semi-analytical framework based on stochastic geometry to quantify task completion probability $P_{success}$, accounting for end-to-end latency $T_{E2E}=t_1+t_2+t_c$ and dual-energy constraints. They formulate and solve an energy-aware optimization problem (P1) that jointly tunes UAV altitude $h$ and transmit power $P_d$ under battery and fuel budgets, using a two-stage alternating optimization approach. Numerical results show optimal altitudes around a few hundred meters, substantial gains from joint optimization, and the critical balance between communication coverage and computing power under energy constraints, highlighting the practical viability of UAV-CPNs in infrastructure-scarce and disaster scenarios.

Abstract

This paper presents an innovative framework that boosts computing power by utilizing ubiquitous computing power distribution and enabling higher computing node accessibility via adaptive UAV positioning, establishing a UAV-enabled Computing Power Network (UAV-CPN). In a UAV-CPN, a UAV functions as a dynamic relay, outsourcing computing tasks from the request zone to an expanded service zone with diverse computing nodes, including vehicle onboard units, edge servers, and dedicated powerful nodes. This approach has the potential to alleviate communication bottlenecks and overcome the "island effect" observed in multi-access edge computing. A significant challenge is to quantify computing power performance under complex dynamics of communication and computing. To address this challenge, we introduce task completion probability to capture the capability of UAV-CPNs for task computing. We further enhance UAV-CPN performance under a hybrid energy architecture by jointly optimizing UAV altitude and transmit power, where fuel cells and batteries collectively power both UAV propulsion and communication systems. Extensive evaluations show significant performance gains, highlighting the importance of balancing communication and computing capabilities, especially under dual-energy constraints. These findings underscore the potential of UAV-CPNs to significantly boost computing power.

UAV-enabled Computing Power Networks: Design and Performance Analysis under Energy Constraints

TL;DR

This work addresses latency-sensitive computing over distributed resources by introducing UAV-enabled Computing Power Networks (UAV-CPNs), where a UAV dynamically accesses ubiquitous computing nodes to mitigate the island effect. The authors develop a semi-analytical framework based on stochastic geometry to quantify task completion probability , accounting for end-to-end latency and dual-energy constraints. They formulate and solve an energy-aware optimization problem (P1) that jointly tunes UAV altitude and transmit power under battery and fuel budgets, using a two-stage alternating optimization approach. Numerical results show optimal altitudes around a few hundred meters, substantial gains from joint optimization, and the critical balance between communication coverage and computing power under energy constraints, highlighting the practical viability of UAV-CPNs in infrastructure-scarce and disaster scenarios.

Abstract

This paper presents an innovative framework that boosts computing power by utilizing ubiquitous computing power distribution and enabling higher computing node accessibility via adaptive UAV positioning, establishing a UAV-enabled Computing Power Network (UAV-CPN). In a UAV-CPN, a UAV functions as a dynamic relay, outsourcing computing tasks from the request zone to an expanded service zone with diverse computing nodes, including vehicle onboard units, edge servers, and dedicated powerful nodes. This approach has the potential to alleviate communication bottlenecks and overcome the "island effect" observed in multi-access edge computing. A significant challenge is to quantify computing power performance under complex dynamics of communication and computing. To address this challenge, we introduce task completion probability to capture the capability of UAV-CPNs for task computing. We further enhance UAV-CPN performance under a hybrid energy architecture by jointly optimizing UAV altitude and transmit power, where fuel cells and batteries collectively power both UAV propulsion and communication systems. Extensive evaluations show significant performance gains, highlighting the importance of balancing communication and computing capabilities, especially under dual-energy constraints. These findings underscore the potential of UAV-CPNs to significantly boost computing power.
Paper Structure (21 sections, 4 theorems, 25 equations, 9 figures, 2 tables, 1 algorithm)

This paper contains 21 sections, 4 theorems, 25 equations, 9 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. UAV-assisted Computing Framework
  4. Energy-Constrained UAV Optimization
  5. Modeling UAV-CPNs
  6. Network Spatial Model
  7. Air-ground Channel Model
  8. Computing Model
  9. Task completion probability analysis
  10. End-to-end latency
  11. Performance metric
  12. Bottleneck Analysis
  13. Task completion probability
  14. Maximizing Task Completion under Energy Constraints
  15. Energy Consumption Modeling
  16. ...and 6 more sections

Key Result

Proposition 4.1

For a GU at a horizontal distance $r_u$ from the UAV, the spatial density of CNs satisfying E2E latency constraint, simply called effective density or conditional spatial density, is given by: where $\mathbb{I}_{\{r_c \leq r_c^{\mathrm{max}}(r_u)\}}$ indicates successful communication; $F_{t_c}(T_{\text{res}}; D)$, as the CDF of computing latency, quantifies the probability of computing latency w

Figures (9)

  • Figure 1: Illustration of a UAV-enabled computing power network, where GUs offload tasks generated within the service area to distributed computing power nodes for processing. The computing accessibility is enhanced by strategically adjusting key network parameters, e.g., the position of the aerial UAV relay.
  • Figure 2: Task completion probability vs. UAV altitude.
  • Figure 3: Task completion probability vs. CN density & UAV altitude.
  • Figure 4: Task completion probability vs. CN distribution & UAV altitude.
  • Figure 5: The joint impact of UAV transmit power and altitude on task completion probability under different energy supply conditions: (a) Ideal case without energy constraints, (b) Under energy budgets of $E_{\text{battery}}=40$ J and $E_{\text{fuel}}=40,000$ J.
  • ...and 4 more figures

Theorems & Definitions (8)

  • Proposition 4.1: Effective CN Density
  • proof
  • Proposition 4.2: The Qualified Number of CNs
  • proof
  • Theorem 4.1
  • proof
  • Theorem 4.2
  • proof