{'fraction_non_alphanumeric': 0.022900763358778626, 'fraction_numerical': 0.0019083969465648854, 'mean_word_length': 5.357575757575757, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Learning Low-Correlation GPS Spreading Codes with a Policy Gradient Algorithm

With the birth of the next-generation GPS III constellation and the upcoming launch of the Navigation Technology Satellite3 (NTS-3) testing platform to explore future technologies for GPS, we are indeed entering a new era of satellite navigation. Correspondingly, it is time to revisit the design methods of the GPS spreading code families. In this work, we develop a Gaussian policy gradient-based reinforcement learning algorithmwhich constructs high-quality families of spreading code sequences. We demonstrate the ability of our algorithm to achieve better mean-squared autoand cross-correlation than well-chosen families of equal-length Gold codes and Weil codes. Furthermore, we compare our algorithm with an analogous genetic algorithm implementation assigned the same code evaluation metric. To the best of the authors’ knowledge, this is the first work to explore using a machine learning / reinforcement learning approach to design navigation spreading codes.