TY - GEN
T1 - MBPANet
T2 - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
AU - Sun, Yanzan
AU - Zhu, Wenxing
AU - Sheng, Zhichao
AU - Zhang, Shunqing
AU - Fang, Yong
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Deep learning (DL)-based methods have shown great potentials to solve power allocation optimization problems in the wireless network. However, most of the existing DL-based works are dedicated to satisfy a specific network performance metric. Therefore, for various metrics, different neural network models need to be trained and stored, which cause parameters redundancy and large storage space occupation. For solving the above issues, we propose a universal neural network architecture called MetricBank Power Allocation Neural Network(MBPANet), which can realize power allocation for multiple metrics with a single neural network model. Furthermore, a two-branch training strategy that integrates supervised learning with unsupervised learning is also introduced to improve training efficiency. Simulation results show that our proposed MBPANet can perform power allocation optimization for multiple metrics with fewer parameters compared to the existing dedicated neural network model. Meanwhile, MBPANet can achieve almost a similar or even higher performance than traditional iterative power allocation algorithm with much lower time complexity.
AB - Deep learning (DL)-based methods have shown great potentials to solve power allocation optimization problems in the wireless network. However, most of the existing DL-based works are dedicated to satisfy a specific network performance metric. Therefore, for various metrics, different neural network models need to be trained and stored, which cause parameters redundancy and large storage space occupation. For solving the above issues, we propose a universal neural network architecture called MetricBank Power Allocation Neural Network(MBPANet), which can realize power allocation for multiple metrics with a single neural network model. Furthermore, a two-branch training strategy that integrates supervised learning with unsupervised learning is also introduced to improve training efficiency. Simulation results show that our proposed MBPANet can perform power allocation optimization for multiple metrics with fewer parameters compared to the existing dedicated neural network model. Meanwhile, MBPANet can achieve almost a similar or even higher performance than traditional iterative power allocation algorithm with much lower time complexity.
KW - Deep Learning
KW - MBPANet
KW - Power Allocation
KW - Training Strategy
KW - Wireless Communication
UR - http://www.scopus.com/inward/record.url?scp=85090278116&partnerID=8YFLogxK
U2 - 10.1109/ICCWorkshops49005.2020.9145093
DO - 10.1109/ICCWorkshops49005.2020.9145093
M3 - Conference Proceeding
AN - SCOPUS:85090278116
T3 - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
BT - 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 June 2020 through 11 June 2020
ER -