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UAV-enabled Computing Power Networks: Task Completion Probability Analysis

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

TL;DR

This work introduces UAV-enabled Computing Power Networks (UAV-CPNs) to alleviate computation bottlenecks by using UAVs as dynamic relays that route GU tasks to distributed CNs. It develops a joint spatial, air-ground channel, and computing latency model and derives task completion probability as the primary performance metric, leveraging stochastic geometry and PPP thinning to capture the spatio-temporal dynamics. Key findings show a nontrivial trade-off between UAV altitude, CN density, and CN coverage radius, with an optimal altitude emerging from the balance between LoS/NLoS effects and path loss, and significant gains achievable by widespread CN distribution. The framework provides a theoretical foundation for designing latency-constrained, AI-enabled services in future wireless networks through coordinated communication and computing resources.

Abstract

This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibility control via adaptive unmanned aerial vehicle (UAV) positioning, establishing UAV-enabled Computing Power Networks (UAV-CPNs). In UAV-CPNs, UAVs function as dynamic aerial relays, outsourcing tasks generated in the request zone to an expanded service zone, consisting of a diverse range of computing devices, from vehicles with onboard computational capabilities and edge servers to dedicated computing nodes. This approach has the potential to alleviate communication bottlenecks in traditional computing power networks and overcome the "island effect" observed in multi-access edge computing. However, how to quantify the network performance under the complex spatio-temporal dynamics of both communication and computing power is a significant challenge, which introduces intricacies beyond those found in conventional networks. To address this, in this paper, we introduce task completion probability as the primary performance metric for evaluating the ability of UAV-CPNs to complete ground users' tasks within specified end-to-end latency requirements. Utilizing theories from stochastic processes and stochastic geometry, we derive analytical expressions that facilitate the assessment of this metric. Our numerical results emphasize that striking a delicate balance between communication and computational capabilities is essential for enhancing the performance of UAV-CPNs. Moreover, our findings show significant performance gains from the widespread distribution of computing nodes.

UAV-enabled Computing Power Networks: Task Completion Probability Analysis

TL;DR

This work introduces UAV-enabled Computing Power Networks (UAV-CPNs) to alleviate computation bottlenecks by using UAVs as dynamic relays that route GU tasks to distributed CNs. It develops a joint spatial, air-ground channel, and computing latency model and derives task completion probability as the primary performance metric, leveraging stochastic geometry and PPP thinning to capture the spatio-temporal dynamics. Key findings show a nontrivial trade-off between UAV altitude, CN density, and CN coverage radius, with an optimal altitude emerging from the balance between LoS/NLoS effects and path loss, and significant gains achievable by widespread CN distribution. The framework provides a theoretical foundation for designing latency-constrained, AI-enabled services in future wireless networks through coordinated communication and computing resources.

Abstract

This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibility control via adaptive unmanned aerial vehicle (UAV) positioning, establishing UAV-enabled Computing Power Networks (UAV-CPNs). In UAV-CPNs, UAVs function as dynamic aerial relays, outsourcing tasks generated in the request zone to an expanded service zone, consisting of a diverse range of computing devices, from vehicles with onboard computational capabilities and edge servers to dedicated computing nodes. This approach has the potential to alleviate communication bottlenecks in traditional computing power networks and overcome the "island effect" observed in multi-access edge computing. However, how to quantify the network performance under the complex spatio-temporal dynamics of both communication and computing power is a significant challenge, which introduces intricacies beyond those found in conventional networks. To address this, in this paper, we introduce task completion probability as the primary performance metric for evaluating the ability of UAV-CPNs to complete ground users' tasks within specified end-to-end latency requirements. Utilizing theories from stochastic processes and stochastic geometry, we derive analytical expressions that facilitate the assessment of this metric. Our numerical results emphasize that striking a delicate balance between communication and computational capabilities is essential for enhancing the performance of UAV-CPNs. Moreover, our findings show significant performance gains from the widespread distribution of computing nodes.

Paper Structure

This paper contains 12 sections, 5 theorems, 15 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Modeling UAV-enabled computing power networks
  3. Network Spatial Model
  4. Air-ground Channel Model
  5. Computing Model
  6. Task completion probability analysis
  7. End-to-end latency
  8. Performance metric
  9. Bottleneck Analysis
  10. Task completion probability
  11. Numerical results
  12. Conclusion

Key Result

Proposition 3.1

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 (4)

  • 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.

Theorems & Definitions (9)

  • Proposition 3.1: Effective CN Density
  • proof
  • Proposition 3.2: Qualified Numbers of CNs
  • proof
  • Theorem 3.1
  • proof
  • Theorem 3.2
  • Theorem 3.2
  • proof