A Deep Neural Network Based Optimization Approach for Wireless Resource Management
- Resource Type
- Conference
- Authors
- Rahman, M.H.; Mowla, M.M.
- Source
- 2020 IEEE Region 10 Symposium (TENSYMP) Region 10 Symposium (TENSYMP), 2020 IEEE. :803-806 Jun, 2020
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Resource management
Signal processing algorithms
Optimization
Artificial neural networks
Wireless communication
Approximation algorithms
Machine learning
deep neural network
wireless resource management
transmit power control
optimization
- Language
- ISSN
- 2642-6102
This paper demonstrates the feasibility of emerging disruptive deep learning technology to solve NP-hard transmit power control problem in future wireless networks. Existing approaches solve this optimization problem by converging to an optimal power allocation solution and require high computation time. This research proposes a deep neural network (DNN) based strategy for solving this high computational convergence time problem while maintaining sufficient sum-rate. The proposed scheme offers a low computational time to the optimization problem of radio resource allocation in real-time applications. The results reveal that our proposed DNN based approach can approximate the behavior of the optimization algorithm and speeds up the operation up to 288 times compared to an iterative based approach.