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Chaos-Based Space-Time Trellis Codes With Deep Learning Decoding
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DOI：10.1109/TCSII.2020.3038481.png)
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Abstract
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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 Poincare 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.
Keywords:
Chaotic communication
Trajectory
Decoding
Transmitting antennas
Convolutional codes
Receiving antennas
Deep learning
Chaos communication
space-time trellis codes
wireless channel
deep learning
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