TY - GEN
T1 - Deep learning based power control for quality-driven wireless video transmissions
AU - Ye, Chuang
AU - Gursoy, M. Cenk
AU - Velipasalar, Senem
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions while using the energy resources efficiently, we assume that power control is employed. Due to the presence of interference, determining the optimal power control is a non-convex problem but can be solved via monotonic optimization framework. However, monotonic optimization is an iterative algorithm and can often entail considerable computational complexity, making it not suitable for real-time applications. To address this, we propose a learning-based approach that treats the input and output of a resource allocation algorithm as an unknown nonlinear mapping and a deep neural network (DNN) is employed to learn this mapping. This learned mapping via DNN can provide the optimal power level quickly for given channel conditions.
AB - In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions while using the energy resources efficiently, we assume that power control is employed. Due to the presence of interference, determining the optimal power control is a non-convex problem but can be solved via monotonic optimization framework. However, monotonic optimization is an iterative algorithm and can often entail considerable computational complexity, making it not suitable for real-time applications. To address this, we propose a learning-based approach that treats the input and output of a resource allocation algorithm as an unknown nonlinear mapping and a deep neural network (DNN) is employed to learn this mapping. This learned mapping via DNN can provide the optimal power level quickly for given channel conditions.
KW - Deep learning
KW - Monotonic optimization
KW - Power control
KW - Resource allocation
KW - Wireless video transmissions
UR - http://www.scopus.com/inward/record.url?scp=85063083170&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063083170&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2018.8646470
DO - 10.1109/GlobalSIP.2018.8646470
M3 - Conference contribution
AN - SCOPUS:85063083170
T3 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
SP - 574
EP - 578
BT - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Y2 - 26 November 2018 through 29 November 2018
ER -