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Online Sparse Beamforming in C-RAN: A Deep Reinforcement Learning Approach
- Source :
- WCNC
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Higher communication rates are required given that cloud radio access network (C-RAN) becomes a significant component of 5G wireless communication, yet the problem of using sparse beamforming to maximize the achievable sum rate in the long term subject to transmit power constraints still remains open in C-RAN. Inspired by the success of Deep Reinforcement Learning (DRL) in solving dynamic programming problems, we propose a DRL-based framework for online sparse beamforming in C-RAN. Particularly, the DRL agent is in charge of remote radio head (RRH) activation based on the defined state space, action space, and reward function, and meanwhile makes a decision on transmit beamforming at active RRHs in each decision period. Through simulations, we evaluate the performance of the proposed framework by comparing it with traditional ways and show that it can achieve higher sum rate in time-varying network environment.
- Subjects :
- Beamforming
Radio access network
business.industry
Computer science
020302 automobile design & engineering
020206 networking & telecommunications
02 engineering and technology
Transmitter power output
Remote radio head
0203 mechanical engineering
Computer engineering
0202 electrical engineering, electronic engineering, information engineering
Wireless
Reinforcement learning
State space
business
C-RAN
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2021 IEEE Wireless Communications and Networking Conference (WCNC)
- Accession number :
- edsair.doi...........05026656df1101290e3c5d017f0607af