TY - JOUR
T1 - GWO-BP neural network based OP performance prediction for mobile multiuser communication networks
AU - Xu, Lingwei
AU - Wang, Han
AU - Lin, Wen
AU - Gulliver, Thomas Aaron
AU - Le, Khoa N.
PY - 2019
Y1 - 2019
N2 - The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction.
AB - The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction.
UR - https://hdl.handle.net/1959.7/uws:60097
U2 - 10.1109/ACCESS.2019.2948475
DO - 10.1109/ACCESS.2019.2948475
M3 - Article
SN - 2169-3536
VL - 7
SP - 152690
EP - 152700
JO - IEEE Access
JF - IEEE Access
ER -