Chaos-Based Space-Time Trellis Codes With Deep Learning Decoding
In this brief we propose a space-time trellis code scheme based on three-dimensional chaotic attractors. The chaotic trajectories are represented by the symbolic dynamics generated by a labeled Poincaré section and are transmitted by multiple antennas, defining a chaos-based space-time trellis code...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2021-04, Vol.68 (4), p.1472-1476 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | In this brief we propose a space-time trellis code scheme based on three-dimensional chaotic attractors. The chaotic trajectories are represented by the symbolic dynamics generated by a labeled Poincaré section and are transmitted by multiple antennas, defining a chaos-based space-time trellis code (CB-STTC). This code is defined by a finite state encoder that maps information sequences to restricted sequences satisfying the dynamics of the attactor. We also propose a neural network architecture capable of learning how to decode the CB-STTC. Finally, the frame error rate of the proposed CB-STTC is analyzed with maximum likelihood and neural network decoding. |
---|---|
ISSN: | 1549-7747 1558-3791 |
DOI: | 10.1109/TCSII.2020.3038481 |