Devising High-Performing Random Spreading Code Sequences Using a Multi-Objective Genetic Algorithm

Reinvigorating the Navigation Technology Satellite (NTS) experimentation platform from its previous initiative in 1977, the United States Air Force (USAF) has expressed recent interest to enhance PNT resiliency and performance, while seeking to explore modificaiton to all layers of the GPS signal. For satellite navigation, developing spreading codes with reduced correlation sidelobes would correspondingly reduce inter-channel interference between the simultaneously broadcast satellite signals. Utilizing low-correlation spreading codes would enable GPS to provide improved navigation performance as well as incorporate a greater number of navigation signals, which further improves redundancy and accuracy. In this work, we develop a multi-objective, genetic algorithm-based architecture to devise high-quality code families with low mean, circular non-central auto-correlation and cross-correlation properties. Our search algorithm explores the multi-objective cost function space and seeks to progress and expand the local Pareto-optimal front of solutions. We demonstrate that our algorithm devises high-quality families of spreading code sequences which achieve low mean non-central auto-correlation and cross-correlation values, out-performing well-chosen families of equal-length Gold codes and Weil codes.