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arXiv 2201.05524eess.SPcs.LG
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非线性功率放大器下降低带外辐射的波形学习

Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier

Nokia Bell Labs(诺基亚贝尔实验室);NVIDIA(英伟达)

Dani Korpi, Mikko Honkala, Janne M. J. Huttunen, Fayçal Ait Aoudia, Jakob Hoydis

摘要:

机器学习(ML)在优化无线通信系统中的物理层处理的各个方面中已经显示出巨大的前景。在本文中,我们使用ML来联合学习发射波形和频域接收机。特别是,我们考虑的情况下,发射机功率放大器以非线性方式工作,ML是用来优化波形,以尽量减少带外发射。该系统还学习星座形状,该星座形状便于由同时学习的接收机进行无导频检测。仿真结果表明,这种端到端优化系统可以更准确地传输数据,并且比传统系统具有更少的带外辐射,从而展示了ML在优化空中接口方面的潜力。据我们所知,没有考虑在端到端学习系统中的功率放大器引起的发射的先前工作。这些发现为ML本地空中接口铺平了道路,这可能是6G的构建模块之一。

英文摘要:

Machine learning (ML) has shown great promise in optimizing various aspects of the physical layer processing in wireless communication systems. In this paper, we use ML to learn jointly the transmit waveform and the frequency-domain receiver. In particular, we consider a scenario where the transmitter power amplifier is operating in a nonlinear manner, and ML is used to optimize the waveform to minimize the out-of-band emissions. The system also learns a constellation shape that facilitates pilotless detection by the simultaneously learned receiver. The simulation results show that such an end-to-end optimized system can communicate data more accurately and with less out-of-band emissions than conventional systems, thereby demonstrating the potential of ML in optimizing the air interface. To the best of our knowledge, there are no prior works considering the power amplifier induced emissions in an end-to-end learned system. These findings pave the way towards an ML-native air interface, which could be one of the building blocks of 6G.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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