应用人工神经网络预测最优功率分配增强安全-可靠性权衡

Xiaoyu Wang, Yuanyuan Gao, Guangna Zhang, Mingxi Guo
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引用次数: 2

摘要

为了提高无线两跳阈值选择译码转发(DF)中继网络的安全可靠性能,本文提出了一种功率分配方案,在中继节点上设置与源信号信噪比(SNR)相关的阈值是实现完美译码的关键。我们采用最大比组合(MRC)接收信噪比从直接路径和中继路径在目的地和窃听者。特别值得一提的是,驱动了中断概率和拦截概率的封闭表达式,可以分别对安全性和可靠性进行量化。我们还尝试使用一个度量来权衡安全性和可靠性(SRT),并在平衡的情况下找出它们之间的相关性。但除此之外,在追求权衡性能时,功率分配往往依赖于阈值。换句话说,它提供了一种通过阈值优化源和继电器总功率的新方法。结果通过分析得到,仿真验证,并利用BP算法训练人工神经网络进行预测,从而验证了所提方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Optimal Power Allocation for Enhancing Security-Reliability Tradeoff with the Application of Artificial Neural Networks
In this paper, we propose a power allocation scheme in order to improve both secure and reliable performance in the wireless two-hop threshold-selection decode-and-forward (DF) relaying networks, which is so crucial to set a threshold value related the signal-to-noise ratio (SNR) of the source signal at relay nodes for perfect decoding. We adapt the maximal-ratio combining (MRC) receiving SNR from the direct and relaying paths both at the destination and at the eavesdropper. Particularly worth mentioning is that the closed expression form of outage probability and intercept probability is driven, which can quantify the security and reliability, respectively. We also make endeavors to utilize a metric to tradeoff the security and the reliability (SRT) and find out the relevance between them in the balanced case. But beyond that, in the pursuit of tradeoff performance, power allocation tends to depend on the threshold value. In other words, it provides a new method optimizing total power to the source and the relay by the threshold value. The results are obtained from analysis, confirmed by simulation, and predicted by artificial neural networks (ANNs), which is trained with back propagation (BP) algorithm, and thus the feasibility of the proposed method is verified.
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