This is a simple 5G RAN scheduler that uses the scheduler environment build using OpenAI Gym [1]. The power consumption model used in this scheduler is described in [2]
and the design of the scheduler is inspired by [3].
python 3.8.5.gymnumpymath
Run inside 5GRANSched folder:
cd scheduler
pip install -e .
- Sched_QL_SARSA.py allows the user to select between a simple Q-learning (off-policy) approach and the SARSA algorithm that aims to maximize the RAN energy efficiency by selecting the transmission power per resource block.
- cleanScheduler.bat deletes and re-registers the
schedulerenvironment.
5GRANSched is provided under GPLv2.
[1] https://github.com/openai/gym
[2]. A. Khalili, S. Zarandi, M. Rasti and E. Hossain, "Multi-Objective Optimization for Energy- and Spectral-Efficiency Tradeoff in In-Band Full-Duplex (IBFD) Communication," 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6.
[3]. M. Elsayed and M. Erol-Kantarci, "AI-Enabled Radio Resource Allocation in 5G for URLLC and eMBB Users," 2019 IEEE 2nd 5G World Forum (5GWF), Dresden, Germany, 2019, pp. 590-595.