MINDSDR OS platform is a highly customizable module that can be flexibly and efficiently configured, and encapsulates inheritance, allowing for the free combination to produce custom signal waveforms. The goal of the system product software waveform development tool platform MINDSDR OS is to achieve a fully domestically produced, controllable, and customizable AI intelligent programming program that decouples the radio (SDR) platform. This system serves as a new generation integrated intelligent program decoupling radio waveform development and management platform, reducing R&D code costs. It provides online teaching, training, learning, management, research, verification, signal R&D, simulation, and an industrial platform that combines software-defined radio and hardware.
It promotes the evolution of wireless communication development paradigms from “closed innovation” to “open collaboration.” Its core value lies in deeply integrating the flexibility and advantages of software with enterprise-level development governance needs, building a comprehensive value chain innovation infrastructure covering “prototype verification – product development – ecological co-construction.” For communication enterprises, adopting the MOS platform for new product development means embracing the wave of technological innovation with lower costs and higher efficiency, seizing strategic opportunities in the era of the intelligent Internet of Things.
Many software-defined radio development platforms on the market require repetitive development of the same code and logic. The MINDSDR OS platform adopts a self-developed architecture based on PYGTK3+PYGOBJECT+FLASK+TYPESCRIPT, combined with the Linux system debian12.5.0, compatible with the latest version of the open-source GNUCLIB library. Based on the open software-defined radio architecture, it has achieved component management based on CS+BS, custom development modules, custom signal waveforms, real-time observation, visual verification of signal data, flowchart layout, automatic layout, high operational efficiency, and other key core technologies. It also features hierarchical user management, supporting student management, teacher management, class management, job management, assignment management, experiment report management, book management, course management, task management, plan management, role management, permission management, and department organization management, with real-time development and demonstration effect evaluation and system security automatic monitoring functions, possessing complete independent intellectual property rights.
It is suitable for researchers and enterprises in the fields of radar, communication, countermeasures, electronics, interference, and information, with stable algorithm support for the development of most related fields such as: “Software Defined Radio”, “Communication Principles”, “Electronic Countermeasures”, “Information Countermeasures”, “Digital Signal Processing”, “Communication Countermeasures”, “Mobile Communication”, “Radar Principles”, and “Radar + AI”. It can also develop related signal cases such as: AM, APSK, QPSK, FSK, FM, LINK11, LINK16, and other simulations and semi-physical verification signals.
lDevelopment Efficiency: By reusing modules and using automated toolchains, the development cycle of complex systems is shortened by over 50%.
lTechnical Cost: Reduces R&D thresholds, hardware interfaces use open-source software-defined radio drivers, allowing for multi-device adaptation.
lInnovation Barriers: Visual development and AI-assisted tools empower innovation.
The platform adopts a self-developed architecture based on PYTHON3+CPP+MOS AI engine, combined with BS+CS+SCCS+MOS+DevOps architecture to realize software-defined radio, achieving component management, custom development modules, custom signal waveforms, real-time observation, visual verification of signal data, flowchart layout correction, automatic layout, high operational efficiency, assembly flowchart designer, multi-domain collaborative simulation, AI-assisted wiring and topology optimization algorithms, real-time spectrum monitoring, and eye diagram analysis tools.
1.1. Product Positioning
“MINDSDR OS platform“ is a new generation intelligent software-defined radio general experimental operation software and hardware combined simulation + hardware learning platform developed based on international standard protocol decoupling interface rules, innovatively integrating “teaching – learning – practice – examination – evaluation” into a unified intelligent education function and system-level management module. The platform adopts an assembly-style architecture design, supporting the R&D verification of advanced communication protocols while providing a complete teaching solution from theoretical learning to practical assessment, equipped with multi-level permission management, development governance, and intelligent operation and maintenance, supporting researchers and enterprises in radar, communication, countermeasures, electronics, interference, and information.
Stable algorithms support the development of many related fields such as: “Software Defined Radio”, “Communication Principles”, “Electronic Countermeasures”, “Information Countermeasures”, “Digital Signal Processing”, “Communication Countermeasures”, “Mobile Communication”, “Radar Principles”, and “Radar + AI”. It can also develop related signal cases such as: AM, APSK, QPSK, FSK, FM, LINK11, LINK16, and other simulations and semi-physical verification signals.
The system provides rich functional expansion capabilities, over 500 signal processing modules, and secondary waveform development functions that can be flexibly customized according to user needs, ensuring content flexibility and keeping pace with their unique development expansion features.
Users can flexibly construct complex radio communication functional systems using an assembly visualization approach. It can be used in conjunction with external RF hardware (such as SDR devices) or simulated and processed in a pure software environment. It is suitable for research, industry, and education, as well as various scenarios such as wireless communication, signal processing, and radar systems, providing comprehensive solutions.
In terms of software support, MOS AI breaks the shackles of traditional teaching equipment in the rapidly evolving fields of wireless communication and artificial intelligence, deeply integrating the latest software-defined radio technology and cleverly incorporating advanced AI teaching concepts. It relies on a unified software and hardware platform, carefully designing experimental cases covering communication principles, software-defined radio, communication countermeasures, digital signal processing, radar principles, satellite communication, satellite navigation, and mobile communication.
These experimental case components are not only rich in content but also inherit waveform development functions that can be flexibly customized according to user needs, ensuring content flexibility and keeping pace with their unique development expansion features.
In terms of hardware support, MOS AI also demonstrates outstanding performance. It is equipped with a self-developed software-defined teaching experimental system, a rich library of course experimental cases, high-performance signal simulation servers, and various RF devices (including comprehensive, standard, pocket types), capable of easily meeting different teaching needs in various scenarios. At the same time, it is widely compatible with mainstream software-defined radio devices such as the USRP series, HackRF, LimeSDR, and ADALM-Pluto, allowing teachers and students to experience cutting-edge technology in experiments and supporting the autonomous selection of commonly used devices on the market, such as B210, B2920, X310, X410, and other USRP devices.
The greatest innovation of MOS AI lies in its clever integration of virtual simulation experiments with physical hardware experiments, creating a new teaching model of “remote control, virtual entity combination, multi-functional integration, and real experience.” Students can not only conduct experimental operations remotely, enjoying the convenience and efficiency brought by virtual simulation but also obtain the most authentic operational experience and learning feedback through physical hardware. This teaching model greatly enhances students’ interest in learning and hands-on ability, laying a solid foundation for their future research and innovation.
Looking ahead, the MOS AI software-defined radio teaching experimental system will continue to stand at the forefront of wireless communication and artificial intelligence education, continuously innovating and breaking through, building a bridge to future technology for teachers and students. Here, they will explore freely, practice bravely, and continuously stimulate unlimited innovative potential and exploratory desire.