TY - GEN
T1 - A Convolutional Neural Network based Resource Management Algorithm for NOMA enhanced D2D and Cellular Hybrid Networks
AU - Zhang, Zhenfeng
AU - Zhai, Daosen
AU - Zhang, Ruonan
AU - Tang, Xiao
AU - Wang, Yutong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - This paper mainly studies the channel and power allocation for the device-to-device (D2D) and cellular hybrid network with non-orthogonal multiple access (NOMA) technology. We formulate the joint channel and power allocation problem as a mixed integer programming problem (MIP). Since the MIP is non-convex and NP-hard, the computational complexity of the traditional optimization method is very high. To overcome this drawback, we construct a convolutional neural network (CNN) to approximate traditional optimization methods. Specifically, the inputs of the CNN are the channel state information of users, and the outputs are the channel allocation and power control policies. The relation between the inputs and the outputs is established by a hidden layer, which consists of a convolutional layer, a pooling layer, and a fully connected layer. The simulation results indicate that the CNN based resource allocation scheme can achieve a good performance with a ultra-low computational complexity.
AB - This paper mainly studies the channel and power allocation for the device-to-device (D2D) and cellular hybrid network with non-orthogonal multiple access (NOMA) technology. We formulate the joint channel and power allocation problem as a mixed integer programming problem (MIP). Since the MIP is non-convex and NP-hard, the computational complexity of the traditional optimization method is very high. To overcome this drawback, we construct a convolutional neural network (CNN) to approximate traditional optimization methods. Specifically, the inputs of the CNN are the channel state information of users, and the outputs are the channel allocation and power control policies. The relation between the inputs and the outputs is established by a hidden layer, which consists of a convolutional layer, a pooling layer, and a fully connected layer. The simulation results indicate that the CNN based resource allocation scheme can achieve a good performance with a ultra-low computational complexity.
KW - channel allocation
KW - convolutional neural network (CNN)
KW - Device to device (D2D)
KW - heterogeneous network (HetNet)
KW - non-orthogonal multiple access (NOMA)
KW - power control
UR - http://www.scopus.com/inward/record.url?scp=85077768135&partnerID=8YFLogxK
U2 - 10.1109/WCSP.2019.8928033
DO - 10.1109/WCSP.2019.8928033
M3 - 会议稿件
AN - SCOPUS:85077768135
T3 - 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019
BT - 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019
Y2 - 23 October 2019 through 25 October 2019
ER -