GR and BP neural network-based performance prediction of dual-antenna mobile communication networks
GR and BP neural network-based performance prediction of dual-antenna mobile communication networks
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基于GR和BP神经网络的双天线移动通信网络性能预测
DOI：
10.1016/j.comnet.2020.107172
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发表时间：
2020-05-08
影响因子：
5.6
通讯作者：
Le, Khoa N.
中科院分区：
文献类型：
--
作者：
Xu, Lingwei;Quan, Tianqi;Le, Khoa N.
研究了双天线移动的通信网络在2-Rayleigh衰落下的性能。推导了q进制相移键控（PSK）和脉冲幅度调制（PAM）系统中选择合并（SC）时平均误符号概率（SEP）的精确表达式。给出了信道容量的精确表达式。预测复杂无线环境下移动的通信网络的性能具有重要意义。因此，我们提出了基于广义回归（GR）和反向传播（BP）神经网络的SEP预测方法。理论结果用于生成训练数据。建议的预测方法进行比较极端学习机（ELM），局部加权线性回归（LWLR），支持向量机（SVM），径向基函数（RBF）神经网络方法。所获得的结果验证了所提出的方法提供更好的SEP预测。
The performance of a dual-antenna mobile communication network in 2-Rayleigh fading is investigated in this paper. Exact average symbol error probability (SEP) expressions with selection combining (SC) are derived for q-ary phase-shift keying (PSK) and pulse-amplitude modulation (PAM). Exact expressions are also given for the channel capacity. It is important to predict the performance of mobile communication networks in complex wireless environments. Thus, we propose generalized regression (GR) and back-propagation (BP) neural network-based SEP prediction methods. The theoretical results are used to generate training data. The proposed prediction methods are compared to the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), and radial basis function (RBF) neural network methods. The results obtained verify that the proposed methods provide better SEP predictions.