Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this study, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems. We discuss potential challenges and future research inspired by our initial study in this paper.
翻译:在目前的通信系统中,广泛应用了由人工智能(AI)辅助的ODM接收器,以取代和改进传统的ODM接收器。在本研究中,我们首先比较了由人工智能(AI)辅助的ODM接收器,以取代和改进传统的ODM接收器。我们首先比较了两个由人工智能辅助的DM接收器,即数据驱动的完全连接的深神经网络和模型驱动的ComNet,通过广泛的模拟和实时视频传输,利用5G快速原型系统进行超空测试。我们发现模拟和OTA测试之间的性能差距,这是由离线培训频道模型和实际环境之间的差异造成的。我们开发了一个新的在线培训系统,称为SwitchNet接收器,以解决这一问题。这个接收器有一个灵活和可扩展的结构,只能通过在线培训若干参数适应真实的渠道。从OTA试验开始,由AI辅助的DM接收器,特别是开关网络接收器,对真实环境很强大,对未来的通信系统很有希望。我们讨论了我们最初研究中激发的潜在挑战和未来研究。