vidits-kth / bayesla-link-adaptation

Bayesian Link Adaptation under a BLER Target

Bayesian Link Adaptation under a BLER Target

This respository contains the simulation code for running the numerical experiments reported in the paper:
Vidit Saxena and Joakim Jaldén,"Bayesian Link Adaptation under a BLER Target", In 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) on May 26-29, 2020.

This simulation code is written in Python3. Running each of the cells in the corresponding Jupyter Notebooks will execute the experiments, generate a results file, and plot the results.

The simulations make extensive use of the py-itpp, Numpy, and Matplotlib packages.

Additionally, to speed up the generation of results, the simulations are parallezlized using the Ray package. It is possible to run single-threaded simulations at the cost of slowness, by commenting out the Ray-specific lines in the notebook - this is indicated in the appropriate sections of the code.

Files

Link Adaptation - OLLA and BayesLA.ipynb contains the code for running the experiments and saving the results to disk.
Plot Results.ipynb contains the code for plotting experiment results from the result file read from the disk.
source.py contains helper code for simulating a Rayleigh fading wireless channel and for the OLLA and BayesLA algorithms.
AWGN_DATASET.npy contains offline lookup data for mapping between instantaneous channel SNR and CQI values for each MCS.