OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

IEEE Access

Ye Tao¹  Lingwei Xu¹  Han Wang¹  Xianpeng Wang² 

[1] Department of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, China;State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, China;

关键词: Extreme learning machine;    multiuser diversity;    outage probability;    performance prediction;   

DOI  :  10.1109/ACCESS.2020.2966690

来源: DOAJ

【 摘 要 】

Due to the complex and variable environments of mobile communication, the mobile multiuser networks become a hot topic. To process active complex event in mobile multiuser networks, it is important to predict the system performance. In this work, the authors consider the multiuser networks which utilizes transmit antenna selection (TAS). We derive novel closed-form expressions for the outage probability (OP) in terms of the Meijer's G-function. Then, a extreme learning machine (ELM)-based OP performance prediction algorithm is proposed. We use the theoretical results to generate training data. We test back-propagation (BP) neural network, locally weighted linear regression (LWLR), wavelet neural network, ELM, and support vector machine (SVM) methods. Compared with wavelet neural network, SVM, BP neural network, and LWLR methods, the Monte-Carlo results shows that the proposed prediction algorithm can consistently achieve higher OP performance prediction results.

【 授权许可】

Unknown