6G-AUTOR: Autonomic Transceiver via Realtime On-Device Signal Analytics
6G-AUTOR: Autonomic Transceiver via Realtime On-Device Signal Analytics
复制标题
6G-AUTOR：通过实时设备信号分析的自主收发器
DOI：
10.1007/s11265-023-01858-8
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Chu, Liang C.
中科院分区：
文献类型：
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作者：
Lin, Chia-Hung;Rohit, K. V.;Lin, Shih-Chun;Chu, Liang C.
下一代无线系统旨在满足不同的应用需求，但从根本上依赖于点对点传输质量。结合最近的AI无线实现，本文介绍了自主无线电6 G-AUTOR，该无线电利用新型算法-硬件分离平台，传输（TX）和接收（RX）操作的软件化以及RF前端的自动重新配置，以支持链路性能和弹性。因此，可以提供一种软件架构，以实现用于无缝托管和执行环境的事件触发操作。也就是说，这些功能可以在按需的基础上执行，使得在执行点之前没有资源被占用，以获得更好的设备效率。作为一个全面的收发器解决方案，我们的设计包括几个ML驱动的模型，每个模型都增强了TX或RX的特定方面，从而在未来无线系统的严格限制下实现稳健的收发器操作。对于Tx场景，实现了数据驱动的频谱感知算法，以获得当前频带的使用情况以供进一步使用。此外，通过深度Q网络开发了数据驱动的无线电管理模块，以支持TX资源块（RB）的快速重新配置和主动多代理接入。至于Rx场景，添加了基本工具-自动调制分类（AMC），其涉及复杂的相关熵提取，然后是基于卷积神经网络（CNN）的分类，以及基于深度学习的LDPC解码器，以提高接收质量和无线电性能。个别算法的仿真表明，在适当的训练下，每个相应的无线电功能要么优于基准解决方案，要么表现与基准解决方案不相上下。
Next-generation wireless systems aim at fulfilling diverse application requirements but fundamentally rely on point-to-point transmission qualities. Aligning with recent AI-enabled wireless implementations, this paper introduces autonomic radios, 6G-AUTOR, that leverage novel algorithm-hardware separation platforms, softwarization of transmission (TX) and reception (RX) operations, and automatic reconfiguration of RF frontends, to support link performance and resilience. Hence, a software architecture can be provided to enable event-triggered operations for seamless hosting and execution environment. That is, those functions can be executed on an on-demand basis so that no resources will be preoccupied until the point of execution for better device efficiency. As a comprehensive transceiver solution, our design encompasses several ML-driven models, each enhancing a specific aspect of either TX or RX, leading to robust transceiver operation under tight constraints of future wireless systems. As for Tx scenarios, a data-driven spectrum sensing algorithm was implemented to obtain usages of current frequency bands for further use. Also, a data-driven radio management module was developed via deep Q-networks to support fast-reconfiguration of TX resource blocks (RB) and proactive multi-agent access. As for Rx scenarios, a fundamental tool - automatic modulation classification (AMC) which involves a complex correntropy extraction, followed by a convolutional neural network (CNN)-based classification, and a deep learning-based LDPC decoder were added to improve the reception quality and radio performance. Simulations of individual algorithms demonstrate that under appropriate training, each of the corresponding radio functions have either outperformed or have performed on-par with the benchmark solutions.