QoS-Aware Power Management with Convolutional Neural Network

Seeking an autonomous optimal power allocation strategy is one of the most important research issues in machine-to-machine communication networks. Recently, deep learning-based methods provide a promising way to address this research issue. In this paper, we focus on exploring the optimal performance of the convolutional neural network (CNN) by making use of the grid-like topology of the channel information. Extensive comparative experiments have been conducted to compare the advantages of different deep learning methods in allocating power resources. It is shown that, under a similar training setting, on average the optimal value of objective function of CNN is about a quarter better than feedforward neural network (FNN). For several different optimization models, CNN acquired this desirable performance with only about 1 millisecond ultra computational time.

[1]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Mérouane Debbah,et al.  Online Energy-Efficient Power Control in Wireless Networks by Deep Neural Networks , 2018, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[3]  Chunxiao Jiang,et al.  Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks , 2019, IEEE Communications Surveys & Tutorials.

[4]  Alejandro Ribeiro,et al.  Learning Optimal Resource Allocations in Wireless Systems , 2018, IEEE Transactions on Signal Processing.

[5]  Shugong Xu,et al.  A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[6]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[7]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[8]  Woongsup Lee,et al.  Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network , 2018, IEEE Communications Letters.

[9]  Hui Wang,et al.  Power Allocation for Multiple Transmitter-Receiver Pairs Under Frequency-Selective Fading Based on Convolutional Neural Network , 2020, IEEE Access.

[10]  Zhi-Quan Luo,et al.  An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Nikos D. Sidiropoulos,et al.  Learning to optimize: Training deep neural networks for wireless resource management , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[12]  Changcheng Huang,et al.  Machine Learning for Power Allocation of a D2D Network , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[13]  Bernard Fino,et al.  Multiuser detection: , 1999, Ann. des Télécommunications.

[14]  Xian Liu,et al.  QoS-Aware Power Management with Deep Learning , 2019, 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).