Integrated communication and navigation (ICN) technology based on low Earth orbit (LEO) constellations has become a key development direction of the next-generation of space information infrastructure and an important pillar in the construction of China’s comprehensive positioning, navigation, and timing (PNT) system. Given the rapid deployment of global mega-LEO constellations, the industry is urgently requires interdisciplinary talents with expertise in satellite communication and navigation technologies, as well as capabilities in systematic design and engineering practice. However, experimental teaching in navigation and communication courses in colleges currently has critical shortcomings: traditional teaching separates navigation and communication into independent systems, and most existing experimental platforms only focus on traditional Global Navigation Satellite System (GNSS) or standalone satellite communication systems, lacking native support for LEO ICN technology and high-dynamic characteristic simulation. Thus, it is difficult for students to establish a systematic understanding of communication–navigation integration logic. This study designs a specialized LEO ICN experimental teaching simulation platform to solve these core problems and meet industrial demand for training talent.

This platform is developed based on MATLAB/Simulink with a modular, highly scalable hierarchical architecture comprising six core functional modules: LEO constellation simulation, user simulation, observation simulation, signal simulation and broadcasting, signal reception and processing, and performance visualization. Each module interacts via standard data interfaces, with fully open parameter configuration and algorithm expansion interfaces, which enable flexible adjustment of constellation parameters, signal modulation modes, and positioning algorithms for customized experimental design. This ability supports dynamic simulation of Walker- and SOC-typical LEO constellations, multi-scenario user configurations with built-in environmental error models, simultaneous generation of three signal systems, including traditional GNSS, cooperative ICN signals, and noncooperative communication opportunity signals, and mode-matched differential signal processing. On this basis, a four-stage progressive experimental teaching system that follows cognitive laws is constructed, covering constellation configuration and signal generation, high-dynamic signal acquisition and tracking, positioning solution comparison, and universal software radio peripheral-based (USRP-based) semi-physical simulation, with a 6–8 person team-based staged teaching mode combining early theoretical demonstration and mid–late innovative practice.

The platform ends the long-standing isolation between traditional navigation and communication experimental teaching, builds a full-link closed-loop LEO ICN simulation environment, and fills the gap of specialized LEO ICN experimental teaching resources. It perfectly combines the ease of use of graphical pure software simulation, which lowers the entry threshold for students with no prior experience, and the engineering practicability of USRP semi-physical verification, which effectively connects theoretical simulation with industrial practice. The supporting teaching system has been successfully applied to core courses such as innovative practice of LEO Satellites. It also provides sufficient support for students’ independent, innovative projects and curriculum design, helping them transform theoretical knowledge into practical engineering capabilities. Teaching experience shows that the platform effectively connects theory and practice in traditional education, considerably improves students’ systematic design thinking and engineering practice capabilities, shortens the post-graduation industry adaptation cycle, and is widely praised by students.

The platform effectively solves the core problems of current navigation and communication experimental teaching, fully meets the training demand for LEO ICN interdisciplinary talents, and provides practical modern teaching tools for related professional courses. In the future, we will further optimize platform functions, incorporate real LEO satellite observation data, and expand advanced experimental content to better support talent training and technological innovation in the LEO ICN field.