Self-Attention-Based Real-Time Signal Detector for Communication Systems With Unknown Channel Models

摘要

In this letter, a deep-learning-assisted signal detector is developed for communication systems with unknown channel models. By embedding domain knowledge into a self-attention model, a novel detection unit is devised that enables both reliable estimation and fast training. Furthermore, a sliding-window structure is in combined use with the detection unit to realize real-time signal recovery. We evaluate the performance of the proposed detector using a chemical communication experimental platform, and show the superiority of our design in terms of detection accuracy as well as implementation complexity.