Cognitive radio-enabled vehicular nodes as unlicensed users can competitively and opportunistically access the radio spectrum provided by a licensed provider and simultaneously use a dedicated channel for vehicular communications. In such cognitive vehicular networks, channel access optimization plays a key role in making the most of the spectrum resources. In this paper, we present the competition among self-interest-driven vehicular nodes as an evolutionary game and study fundamental properties of the Nash equilibrium and the evolutionary stability. To deal with the inefficiency of the Nash equilibrium, we design a delayed pricing mechanism and propose a discretized replicator dynamics with this pricing mechanism. The strategy adaptation and the channel pricing can be performed in an asynchronous manner, such that vehicular users can obtain the knowledge of the channel prices prior to actually making access decisions. We prove that the Nash equilibrium of the proposed evolutionary dynamics is evolutionary stable and coincides with the social optimum. Besides, performance comparison is also carried out in different environments to demonstrate the effectiveness and advantages of our method over the distributed multi-agent reinforcement learning scheme in current literature in terms of the system convergence, stability and adaptability.

中文翻译：

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具有自适应拥塞定价的用于认知车辆网络的信道访问优化：一种进化博弈方法

作为未授权用户的认知无线电启用的车辆节点可以竞争性地和机会性地访问由授权提供商提供的无线电频谱，并同时使用专用信道进行车辆通信。在这样的认知车载网络中，信道接入优化在充分利用频谱资源方面起着关键作用。在本文中，我们将自利驱动的车辆节点之间的竞争呈现为一种进化博弈，并研究了纳什均衡的基本性质和进化稳定性。为了解决纳什均衡的低效率问题，我们设计了一种延迟定价机制，并提出了具有这种定价机制的离散化复制器动力学。策略适配和渠道定价可以异步进行，这样车辆用户可以在实际做出访问决策之前获得有关频道价格的知识。我们证明所提出的进化动力学的纳什均衡是进化稳定的，并且与社会最优解一致。此外，还在不同环境下进行了性能比较，以证明我们的方法在系统收敛性、稳定性和适应性方面相对于现有文献中的分布式多智能体强化学习方案的有效性和优势。