Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as terahertz communications). Current wireless networks are designed to perform adequately across multiple scenarios so they lack the adaptability needed for specific use cases. Therefore, meeting the stringent requirements of next-generation applications incorporating technology advances and operating in novel scenarios will necessitate wireless specialized networks which we refer to as SpecNets. These networks, equipped with cognitive capabilities, dynamically adapt to the unique demands of each application, e.g., by automatically selecting and configuring network mechanisms. An enabler of SpecNets are the recent advances in artificial intelligence and machine learning (AI/ML), which allow to continuously learn and react to changing requirements and scenarios. By integrating AI/ML functionalities, SpecNets will fully leverage the concept of AI/ML-defined radios (MLDRs) that are able to autonomously establish their own communication protocols by acquiring contextual information and dynamically adapting to it. In this paper, we introduce SpecNets and explain how MLDR interfaces enable this concept. We present three illustrative use cases for wireless local area networks (WLANs): bespoke industrial networks, traffic-aware robust THz links, and coexisting networks. Finally, we showcase SpecNets' benefits in the industrial use case by introducing a lightweight, fast-converging ML agent based on multi-armed bandits (MABs). This agent dynamically optimizes channel access to meet varying performance needs: high throughput, low delay, or fair access. Results demonstrate significant gains over IEEE 802.11, highlighting the system's autonomous adaptability across diverse scenarios.

翻译：未来的无线网络将需要支持多样化的应用（如扩展现实）、场景（如全自动化工业）和技术进步（如太赫兹通信）。当前的无线网络设计旨在多种场景下均能充分运行，因此缺乏针对特定用例所需的适应性。因此，要满足融合技术进步并在新颖场景中运行的下一代应用的严格要求，将需要无线专用网络，我们称之为SpecNets。这些网络具备认知能力，能够动态适应每个应用的独特需求，例如通过自动选择和配置网络机制。SpecNets的一个推动因素是人工智能和机器学习（AI/ML）的最新进展，它们使得网络能够持续学习并响应不断变化的需求和场景。通过集成AI/ML功能，SpecNets将充分利用AI/ML定义无线电（MLDRs）的概念，这些无线电能够通过获取上下文信息并动态适应，自主建立自己的通信协议。在本文中，我们介绍了SpecNets，并解释了MLDR接口如何实现这一概念。我们提出了无线局域网（WLANs）的三个示例用例：定制化工业网络、流量感知的鲁棒太赫兹链路以及共存网络。最后，我们通过引入一个基于多臂赌博机（MABs）的轻量级、快速收敛的ML代理，在工业用例中展示了SpecNets的优势。该代理动态优化信道接入，以满足不同的性能需求：高吞吐量、低延迟或公平接入。结果表明，与IEEE 802.11相比，该系统取得了显著增益，突显了其在多样化场景中的自主适应性。