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Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative

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This study explores non-orthogonal multiple access (NOMA) with aerial-ground networks for mobile edge computing (MEC). It introduces an incentive mechanism and UAV positioning strategy to enhance system performance and service provider utility.

Keywords:
age of informationair-ground collaborative heterogeneous networkscontract theorygenerative diffusion modelmobile edge computing

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Mobile edge computing (MEC) is crucial for latency-sensitive applications.
  • Heterogeneous air-ground networks offer potential for improved throughput and spectral efficiency.
  • Coordinated resource allocation in NOMA-enabled MEC is a significant challenge.

Purpose of the Study:

  • To investigate the convergence of NOMA with air-ground networks for enhanced MEC.
  • To address the challenge of coordinated resource allocation between UAVs and MEC servers.
  • To develop an incentive mechanism and UAV deployment strategy to optimize service provider utility.

Main Methods:

  • A two-stage incentive mechanism based on contract theory.
  • Optimization of service provider utility under individual rationality (IR) and incentive compatibility (IC) constraints.
  • Block coordinate descent and generative diffusion models for contract design.
  • An improved differential evolutionary algorithm for UAV positioning.

Main Results:

  • The proposed incentive mechanism effectively optimizes service provider utility.
  • The UAV positioning strategy maximizes service provider effectiveness.
  • The approach demonstrates robustness in both deterministic and unpredictable scenarios.
  • Significant improvements in system throughput and spectral efficiency are achieved.

Conclusions:

  • The integration of NOMA with heterogeneous air-ground networks presents a promising solution for advanced MEC.
  • The developed incentive mechanism and UAV deployment strategy are effective in enhancing system performance and economic viability.
  • This research provides a robust framework for future MEC network optimization.