Emerging trend of ubiquitous data access is driving the demand for effective wireless communication connectivity. In essence to this, wireless local area network (WLAN) technology seems to be a reliable and cost effective access for the next-generation wireless ecosystem. But the pivotal challenge for WLAN in the next generation wireless networks is to cater the legions of heterogeneous services with characteristic sets of quality of service requirements. However, the strategies present in the existing literature are not accoutered for the application-agnostic association and are incompetent in handling the enormous WLAN state space. Realising the pitfalls of the existing strategies, a novel software-defined networking enabled artificial intelligence framework has been proposed. The proposed framework implements a novel invalid action reduction scheme and double deep reinforcement learning to guarantee the flow based association in a multi-service WLAN environment. Moreover, it allows the multi-parametric optimisation of the association decision and faster convergence to the stable solution. The analytical results validated through the extensive simulations revealed that the proposed scheme achieves high performance gain in terms of convergence, stability and network utility as compared to the other solutions in the literature.

普遍存在的数据访问的新兴趋势正在推动对有效无线通信连接的需求。从本质上讲，无线局域网（WLAN）技术似乎是下一代无线生态系统的可靠且经济高效的访问方式。但是，下一代无线网络中的WLAN面临的关键挑战是如何以特征性的服务质量要求集来满足大量的异构服务。然而，现有文献中存在的策略不适用于与应用程序无关的关联，并且在处理巨大的WLAN状态空间方面无能为力。实现现有策略的陷阱，提出了一种新颖的软件定义的联网人工智能框架。所提出的框架实现了新颖的无效动作减少方案和双重深度强化学习，以确保在多服务WLAN环境中基于流的关联。而且，它允许关联决策的多参数优化，并更快地收敛到稳定的解决方案。通过大量仿真验证的分析结果表明，与文献中的其他解决方案相比，该方案在收敛性，稳定性和网络实用性方面均实现了高性能。