TY - JOUR
T1 - Dynamic Power Control for NOMA Transmissions in Wireless Caching Networks
AU - Fu, Yaru
AU - Wen, Wanli
AU - Zhao, Zhongyuan
AU - Quek, Tony Q.S.
AU - Jin, Shi
AU - Zheng, Fu Chun
N1 - Funding Information:
Manuscript received April 16, 2019; revised May 22, 2019; accepted June 11, 2019. Date of publication June 18, 2019; date of current version October 11, 2019. The work of Z. Zhao was supported in part by the Beijing Natural Science Foundation under Grant L182039, and in part by the National Science and Technology Major Project under Grant 2017ZX03001014. The associate editor coordinating the review of this paper and approving it for publication was T. De Cola. (Corresponding author: Wanli Wen.) Y. Fu and T. Q. S. Quek are with the Department of Information Systems Technology and Design, Singapore University of Technology and Design, Singapore (e-mail: yaru_fu@sutd.edu.sg; tonyquek@sutd.edu.sg).
PY - 2019/10
Y1 - 2019/10
N2 - Non-orthogonal multiple access (NOMA) technique is capable of improving the efficiency of delivering and pushing contents in wireless caching networks. However, due to the differences of the data volume and the channel condition, the static power control schemes cannot fully explore the potential of NOMA. To solve this problem, dynamic power control for NOMA transmissions in wireless caching networks is studied in this letter, which can be adjusted based on the status of content transmissions. In particular, we focus on minimizing the transmission delay with the considerations of each user's transmission deadline and the total power constraint. An iterative algorithm is first proposed to approach the optimal solution of dynamic power control. Then a deep neural network (DNN)-based method is designed to keep a balance between the performance and the computational complexity. Finally, Monte-Carlo simulations are provided for verifications.
AB - Non-orthogonal multiple access (NOMA) technique is capable of improving the efficiency of delivering and pushing contents in wireless caching networks. However, due to the differences of the data volume and the channel condition, the static power control schemes cannot fully explore the potential of NOMA. To solve this problem, dynamic power control for NOMA transmissions in wireless caching networks is studied in this letter, which can be adjusted based on the status of content transmissions. In particular, we focus on minimizing the transmission delay with the considerations of each user's transmission deadline and the total power constraint. An iterative algorithm is first proposed to approach the optimal solution of dynamic power control. Then a deep neural network (DNN)-based method is designed to keep a balance between the performance and the computational complexity. Finally, Monte-Carlo simulations are provided for verifications.
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U2 - 10.1109/LWC.2019.2923410
DO - 10.1109/LWC.2019.2923410
M3 - Article
AN - SCOPUS:85073603176
VL - 8
SP - 1485
EP - 1488
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
SN - 2162-2337
IS - 5
M1 - 8738823
ER -