Radio Interface Engines for Standardized AI/ML Radio Reconfiguration
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Solution Overview
Problem
Existing radio communication systems lack standardized interfaces for integrating cognitive context information and AI/ML functions, requiring proprietary solutions that are not scalable or adaptable across different hardware platforms and radio technologies.
Innovation Solution
A Radio Interface Engine (RIE) with standardized interfaces is introduced to provide cognitive context information and support AI/ML functions, enabling efficient acquisition and management of context information in heterogeneous radio environments, and allowing for reconfiguration of radio technologies through Radio Application Packages (RAPs) executed on Radio Virtual Machines (RVMs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary solutions are used for integrating cognitive context information and AI/ML functions, then integration capability is achieved, but scalability and adaptability across different hardware platforms and radio technologies deteriorate
Solution Approach 1:
The patent implements a universal architecture where the Radio Interface Engine and Radio Virtual Machine can execute multiple different radio technologies and AI/ML functions through standardized interfaces. The RVM acts as a universal execution environment that can load and run different RAPs for various radio access technologies (LTE, 5G, WiFi, etc.), eliminating the need for proprietary solutions for each technology and enabling single-platform multi-functionality.
Solution Approach 2:
The system is segmented into distinct functional modules: the Radio Interface Engine for standardized access, the Radio Virtual Machine for execution, and Radio Application Packages for specific functionalities. This segmentation allows each component to be independently developed, optimized, and reused across different applications and hardware platforms, reducing overall system complexity while improving adaptability.
2Adaptability or versatility
If standardized interfaces are introduced for AI/ML integration, then scalability and adaptability improve, but implementation complexity increases
Solution Approach 1:
The Radio Virtual Machine serves as an intermediary layer between the standardized Radio Interface Engine and the diverse AI/ML applications. It translates high-level AI/ML operations into hardware-specific instructions, shielding upper-layer applications from underlying hardware complexity while enabling standardized access to diverse resources. This mediator approach allows scalability without proportionally increasing interface complexity.
Solution Approach 2:
The system uses virtualization to create copies of radio interface functionality within the RVM environment. Multiple virtual instances of radio interfaces can be created and managed through standardized interfaces, allowing scalable deployment across different hardware platforms without requiring proportional increases in physical interface complexity. Virtual copies enable resource sharing and efficient utilization.
3Adaptability or versatility
If reconfigurable radio technologies are implemented through RAPs and RVMs, then flexibility and adaptability improve, but processing overhead and system complexity increase
Solution Approach 1:
Radio Application Packages are pre-configured with optimized parameter sets and AI/ML models for specific radio technologies and use cases. This preliminary preparation allows the RVM to quickly load and execute pre-optimized configurations without performing complex real-time optimization, maintaining processing efficiency while enabling flexible reconfiguration across different radio technologies and operational conditions.
Data Source
AI summary
Disclosed embodiments generally relate to Reconfigurable Radio Systems (RRS), wireless networks, and wireless communication, and in particular, to various Radio Interface Engine (RIE) and Radio Virtual Machine (RVM) arrangements and configurations for RRS. Other embodiments may be described and/or claimed.