A Deep Learning Based Energy Efficient Downlink Power Control Mechanism for Cellular Networks
- Resource Type
- Conference
- Authors
- Biswas, Subrata; Nasir, Aurongo Mohammod; Hossain, Md. Farhad
- Source
- 2020 11th International Conference on Electrical and Computer Engineering (ICECE) Electrical and Computer Engineering (ICECE), 2020 11th International Conference on. :343-346 Dec, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Wireless communication
Power control
Interference
Quality of service
Energy efficiency
Resource management
Signal to noise ratio
SINR
time-series
Energy Efficiency
CNN
AUC
- Language
Management of radio resources in wireless communication has always been a challenging task. The complexity of this resource management is high, because wireless channels always contribute to different interference levels which ultimately results in degradation of the quality of service (QoS). To combat these challenges, many power control algorithms are developed. In this paper, we propose a deep learning (DL) based mechanism for power controlling. A convolutional time-series prediction model is developed which predicts future signal-to-noise-to-interference-ratio (SINR) and allocates power to maintain minimum required SINR level subject to overall amount of power consumed remaining minimum. The results generated by this method are benchmarked with greedy iterative SINR target setting power control technique and the result shows significant improvement in total power consumption and energy efficiency (EE).