Spreading code design: optimizing binary spreading codes, or pseudorandom noise (PRN) codes to have low auto-correlation and cross-correlation. Contains efficient implementations of bit-flip descent methods that iteratively decorrelate a set of codes.
Accompanies the paper:
A. Yang, T. Mina, S. Boyd, and G. Gao. Large-Scale GNSS Spreading Code Optimization. Proceedings of the 37th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2024)
Paper link: https://stanford.edu/~boyd/papers/code_design.html
decor is a Python package for designing and optimizing binary spreading codes,
also known as pseudorandom noise (PRN) codes. These codes are optimized to have
low auto-correlation and cross-correlation properties, making them suitable for
applications in communication systems, such as CDMA and GPS.
- Generate binary spreading codes
- Optimize codes for low auto-correlation and cross-correlation
- Gold and Weil Codes
Example usage:
import decor
# Generate 63 random binary spreading codes, each length 1023
# objective function with parameter p=6
codes = decor.random_code_family(63, 1023, p=6)
# Generate Gold codes
gold_codes = decor.gold_code_family(63, 1023, p=6)
# evaluate correlation values
codes.correlation()
# evaluate objective function
codes.objective()
# optimize codes
optimizer = AdaptiveKGreedyCodeOptimizer()
optimizer.optimize(codes, n_iter=1000)
The scripts directory contains an example.py file. To run the example, use:
python3 -m scripts.example
This project is licensed under the Apache 2.0 License. See the LICENSE file for more details.