Adding Exploration to Tree-Based MIMO Detectors Using Insights from Bio-Inspired Firefly Algorithm
Bastien Trotobas, Youness Akourim, Amor Nafkha, Yves Louёt · 2021
The standard multiple-input multiple-output (MIMO) detectors exploit the available information to resolve the detection problem. Alternative algorithms, such as bioinspired or geometrical detectors, mix exploitation with exploration to bypass local minima and enhance the results. This paper examines the benefits of adding exploration to the traditional tree-based detectors. For this purpose, a new interpretation of the bio-inspired detector based on the firefly algorithm (FA) is proposed. It is studied in a tree search paradigm and extended to soft-outputs. The findings suggest that the addition of a stochastic exploration to tree-based detectors significantly improves performance with a small computational overhead.