beam-align

Code for the paper https://arxiv.org/abs/2206.13166.

There are four main scripts:

1. simulate_opt_x.py: this finds the optimal solution using the optimization problem provided in new_optimization.py. In parameters.py are given, and you can input certain scenario's (see below) for certain parameter settings.
2. BEAM-ALIGN.py: we devised a heuristic algorithm based on beam alignment between user and BS. This algorithm again takes as input all parameters given in parameters.py and outputs the heuristic user association.
3. SNR_heuristic.py: similar to BEAM-ALIGN, but this is a heuristic based on only highest received SNR.
4. MCUA-PA heuristic.py: this heuristic is based on Harris Hawkes Optimization and described in [1]. We implemented the algorithm of this paper in MCUA-PA.py and MCUA-PA heuristic.py

Next to that, there are some helper functions: functions.py is a collection of all functions relevant to the system model (based on 3GPP), such as the beamforming gain, path loss and interference. In simulate_blockersj.py, we implemented an algorithm to simulate blockers (rectangles with a certain angle, width and length) in a certain area. Lastly, find_data.py and get_data.py are scripts to rewrite the data in such a format that it can be used to make all figures.

Scenario's

We defined different scenario's, based on different beamwidths, number of connections per user and whether we simulate a clustered user setting.

[1] Jin, K., Cai, X., Du, J., Park, H., & Tang, Z. (2022). Toward energy efficient and balanced user associations and power allocations in multiconnectivity-enabled mmWave networks. IEEE Transactions on Green Communications and Networking, 6(4), 1917-1931.