Matching-with-Contracts for the AI-RAN Market: AIGC-as-a-Service for Teleoperation

2026-05-11

AI Summary

This study addresses the challenge of incentive mechanism design in the AI-RAN market, where information asymmetry and competition among multiple operators complicate efficient resource allocation. The work proposes the first dynamic matching-with-contracts framework that jointly optimizes contract menu design across multiple operators, user–operator matching, and market state evolution. It innovatively extends the matching-with-contracts model to a dynamic, multi-operator setting, integrating queueing theory and Chernoff bounds to characterize the probability of AI service latency violations. A hybrid stable matching algorithm is developed to balance system efficiency and stability. Simulations on teleoperated AIGC services demonstrate that the proposed approach improves total operator utility by at least 56.8% compared to baseline schemes, confirming its effectiveness and robustness.

Abstract

Artificial intelligence radio access networks (AI-RANs) are a promising architecture for bolstering the prosperity of the edge AI ecosystem. A well-designed incentive mechanism can further ensure the sustainable development of this ecosystem. However, incentive mechanism design faces two major challenges: 1) information asymmetry, where AI-RAN operators have only partial knowledge of AI users' utility functions, and 2) competition, as multiple AI-RAN operators coexist in real-world markets. Remarkably, chaotic and adversarial competition might compromise AI-RAN operators' utility. To this end, we develop a matching-with-contracts framework for incentive mechanism design in AI-RAN service markets. The framework extends the static matching-with-contracts model by jointly characterizing the contract design of multiple competitive operators, user-operator matching, and dynamic evolution of the market state. Specifically, the incentive mechanism offered by each AI-RAN operator takes the form of a contract menu, where each contract item consists of an AI service latency agreement and a corresponding price. We model the AI service process as three independent queues and characterize the violation probability of the latency agreement using queueing theory and the Chernoff bound. To derive an effective incentive mechanism, we further propose a mixed stable matching-with-contracts algorithm that jointly updates user-side matching decisions and operator-side contract menus. Simulation results for a teleoperation-oriented AIGC service demonstrate the effectiveness and robustness of the proposed method. Compared with benchmark schemes, our method improves the total utility of AI-RAN operators by at least 56.8\% under representative settings.

Problem

• AI-RAN
• incentive mechanism
• information asymmetry
• market competition
• matching-with-contracts

Innovation

• matching-with-contracts
• AI-RAN
• incentive mechanism
• queueing theory
• AIGC-as-a-Service