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

TL;DR

This paper introduces a matching-with-contracts framework for incentive mechanisms in AI-RAN markets, addressing information asymmetry and competition, and demonstrates improved utility through simulations.

Contribution

It develops a novel joint matching and contract design framework for competitive AI-RAN markets, incorporating queueing theory and market dynamics.

Findings

Proposed method improves AI-RAN operators' utility by at least 56.8%.

The framework effectively manages information asymmetry and competition.

Simulation results validate the robustness of the incentive mechanism.

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.