Scope

This project contains code (written in Python3) for simulating two mechanisms which modify the operation of IEEE 802.11ax UORA OBO countdown:

• Efficient OBO (E-OBO) -- proposed in K. Kosek-Szott and K. Domino, “An Efficient Backoff Procedure for IEEE 802.11 ax Uplink OFDMA-Based Random Access,” IEEE Access, vol. 10, pp. 8855–8863, 2022.
• Reinforcement Learning OBO (RL-OBO) -- proposed in K. Kosek-Szott, S. Szott and F. Dressler, "Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches", IEEE Access, DOI 10.1109/ACCESS.2022.3221423, 2022.

E-OBO

The following files are necessary to simulate E-OBO operation:

• Times.py -- methods which calculate IEEE 802.11 frame transmission durations.
• UORA.py -- a simulator of the IEEE 802.11ax UORA frame exchange, extended to support the E-OBO mechanism.
• EOBO.py -- allows running static or dynamic simulations.

The simulation can be run as follows:

./python EOBO.py

Within the code, the dynamic boolean defines which scenario is run (static or dynamic).

RL-OBO

The following files are necessary to simulate RL-OBO operation:

• Times.py -- methods which calculate 802.11 frame transmission durations.
• UORA_RL.py -- a simulator of the IEEE 802.11ax UORA frame exchange, extended to support RL methods.
• RL-OBO.py -- implements the RL-OBO mechanism and, if executed, runs the simulation (training, then testing).

The simulation can be run as follows:

./python RL-OBO.py

Additionally, to perform a reward parameter sweep (Appendix A in the RL-OBO paper), execute RL-OBO_reward_params (which imports UORA_RL_reward_params) as follows:

./ipython RL-OBO_reward_params.py