AI-Driven Demodulators for Nonlinear Receivers in Shared Spectrum with High-Power Blockers
AI-Driven Demodulators for Nonlinear Receivers in Shared Spectrum with High-Power Blockers
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DOI：
10.1109/wcnc51071.2022.9771613
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
H. Mohammadi;Walaa AlQwider;T. Rahman;V. Marojevic
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文献类型：
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作者：
H. Mohammadi;Walaa AlQwider;T. Rahman;V. Marojevic
研究表明，通信系统和接收器受到高功率相邻信道信号（称为阻滞剂）的影响，这些信号会使射频（RF）前端进入非线性工作状态。由于简单的系统，如物联网（IoT），将与复杂的通信收发器，雷达和其他频谱消费者共存，因此需要采用简单但自适应的射频非线性解决方案来保护这些系统。因此，本文提出了一种灵活的数据驱动方法，该方法使用简单的人工神经网络（ANN）来帮助消除三阶互调失真（IMD），作为解调过程的一部分。我们介绍并数值评价了两种人工智能（AI）增强的接收器——作为IMD消除器的人工神经网络和作为解调器的人工神经网络。研究结果表明，简单的人工神经网络结构可以显著提高具有强阻滞器的非线性接收机的误码率（BER）性能，并且人工神经网络的结构和配置主要取决于射频前端特性，如三阶截获点（IP3）。因此，我们建议接收器具有硬件标签和监控这些标签的方法，以便AI和软件无线电处理堆栈可以有效地定制和自动更新，以应对不断变化的操作条件。
Research has shown that communications systems and receivers suffer from high power adjacent channel signals, called blockers, that drive the radio frequency (RF) front end into nonlinear operation. Since simple systems, such as the Internet of Things (IoT), will coexist with sophisticated communications transceivers, radars and other spectrum consumers, these need to be protected employing a simple, yet adaptive solution to RF nonlinearity. This paper therefore proposes a flexible data driven approach that uses a simple artificial neural network (ANN) to aid in the removal of the third order intermodulation distortion (IMD) as part of the demodulation process. We introduce and numerically evaluate two artificial intelligence (AI)-enhanced receivers—ANN as the IMD canceler and ANN as the demodulator. Our results show that a simple ANN structure can significantly improve the bit error rate (BER) performance of nonlinear receivers with strong blockers and that the ANN architecture and configuration depends mainly on the RF front end characteristics, such as the third order intercept point (IP3). We therefore recommend that receivers have hardware tags and ways to monitor those over time so that the AI and software radio processing stack can be effectively customized and automatically updated to deal with changing operating conditions.