arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.06068cs.NIcs.LG
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用于无线通信的深度学习

Deep Learning for Wireless Communications

Virginia Tech(弗吉尼亚理工大学);DeepSig, Inc.(DeepSig公司);Intelligent Automation, Inc.(智能自动化公司)

Tugba Erpek, Timothy J. O'Shea, Yalin E. Sagduyu, Yi Shi, T. Charles Clancy

摘要:

当处理具有高自由度的新兴无线应用的优化的复杂性时,现有通信系统在将理论转化为实践方面表现出固有的局限性。深度学习具有强大的潜力,可以通过数据驱动的解决方案克服这一挑战,并提高无线系统在利用有限频谱资源方面的性能。在本章中,我们首先描述如何使用深度学习来设计使用自动编码器的端到端通信系统。这种灵活的设计有效地捕捉信道损伤,并优化发射机和接收机的操作,在单天线,多天线和多用户通信。接下来,我们介绍了深度学习在频谱态势感知中的优势,从信道建模和估计到信号检测和分类任务。当基于模型的方法失败时,深度学习可以提高性能。最后,我们讨论了深度学习如何应用于无线通信安全。在这种情况下,对抗性机器学习提供了发起和防御无线攻击的新方法。这些应用展示了深度学习在提供设计、优化、调整和保护无线通信的新方法方面的强大功能。

英文摘要:

Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom. Deep learning has a strong potential to overcome this challenge via data-driven solutions and improve the performance of wireless systems in utilizing limited spectrum resources. In this chapter, we first describe how deep learning is used to design an end-to-end communication system using autoencoders. This flexible design effectively captures channel impairments and optimizes transmitter and receiver operations jointly in single-antenna, multiple-antenna, and multiuser communications. Next, we present the benefits of deep learning in spectrum situation awareness ranging from channel modeling and estimation to signal detection and classification tasks. Deep learning improves the performance when the model-based methods fail. Finally, we discuss how deep learning applies to wireless communication security. In this context, adversarial machine learning provides novel means to launch and defend against wireless attacks. These applications demonstrate the power of deep learning in providing novel means to design, optimize, adapt, and secure wireless communications.

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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