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arXiv 1901.02005eess.SP
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不可信中继网络中深度学习辅助的天线选择

Deep Learning Assisted Antenna Selection in Untrusted Relay Networks

Rugui Yao, Yuxin Zhang, Shengyao Wang, Nan Qi, Theodoros A. Tsiftsis, Nikos I. Miridakis

摘要:

本文主要研究不可信中继网络中基于深度学习的发射天线选择问题。在以前的工作中,我们发现,基于机器学习(ML)的天线选择方案有小的性能下降所造成的复杂的耦合关系之间的可实现的保密率和信道增益。为了解决这个问题,我们在这里引入深度神经网络(DNN)来解耦复杂的关系。仿真结果表明,DNN方案可以实现更好的解耦,从而执行几乎相同的性能与传统的穷尽搜索方案。

英文摘要:

This letter mainly studies the transmit antenna selection(TAS) based on deep learning (DL) scheme in untrusted relay networks. In previous work, we discover that machine learning (ML)-based antenna selection schemes have small performance degradation caused by complicated coupling relationship between achievable secrecy rate and the channel gains. To solve the issue, we here introduce deep neural network (DNN) to decouple the complicated relationship. The simulation results show the DNN scheme can achieve better decoupling and thus perform almost the same performance with conventional exhausted searching scheme.

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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