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.

翻译：人工智能无线接入网络（AI-RAN）是支撑边缘AI生态系统繁荣发展的有前途架构。设计良好的激励机制可进一步确保该生态系统的可持续发展。然而，激励机制设计面临两大挑战：1）信息不对称，即AI-RAN运营商仅能部分获知AI用户的效用函数；2）竞争性，即实际市场中存在多个共存的AI-RAN运营商。值得注意的是，混乱且对抗性的竞争可能损害AI-RAN运营商的效用。为此，我们为AI-RAN服务市场中的激励机制设计开发了一个合约匹配框架。该框架通过联合刻画多个竞争性运营商的合约设计、用户-运营商匹配以及市场状态的动态演化，对静态合约匹配模型进行了扩展。具体而言，每个AI-RAN运营商提供的激励机制采用合约菜单形式，其中每个合约条目包含AI服务延迟协议及相应价格。我们将AI服务过程建模为三个独立队列，并利用排队论和Chernoff界来刻画延迟协议的违反概率。为推导有效的激励机制，我们进一步提出一种混合稳定合约匹配算法，该算法联合更新用户侧匹配决策和运营商侧合约菜单。面向远程操作的AIGC服务仿真结果表明，所提方法具有有效性和鲁棒性。与基准方案相比，在代表性设置下，我们的方法使AI-RAN运营商的总效用至少提升56.8%。