Configurable Air Interface for Device-Specific Wireless Optimization
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Solution Overview
Problem
Existing wireless communication systems have a one-size-fits-all approach to air interface definitions, which cannot be adapted or changed once defined, leading to suboptimal transmission conditions and requirements for different devices.
Innovation Solution
The implementation of a configurable air interface using artificial intelligence and machine learning to provide device-specific optimization, allowing for personalized tailored air interfaces that adapt to the unique transmission capabilities and requirements of each device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a one-size-fits-all air interface definition is used, then device complexity is reduced and ease of manufacture is improved, but transmission efficiency and adaptability to different device requirements deteriorate
Solution Approach 1:
The air interface is segmented into multiple configurable components including waveform parameters, frame structure, multiple access schemes, coding schemes, and modulation schemes. Each component can be independently configured to match specific device requirements, allowing the system to maintain simplicity through modular design while achieving high transmission efficiency through customized configurations for different devices
Solution Approach 2:
The air interface configuration is made dynamic and adaptable rather than fixed. The system can change air interface parameters based on device capabilities, transmission conditions, and service requirements. This dynamic configuration allows the same physical network infrastructure to efficiently serve diverse devices with different transmission needs
2Adaptability or versatility
If a configurable air interface with predetermined parameters is used, then adaptability to different services is improved, but device-specific optimization capability deteriorates
Solution Approach 1:
The system applies local quality by configuring air interface parameters specifically for each device rather than using uniform configurations. Each device receives customized air interface settings tailored to its specific capabilities and requirements, such as adjusting waveform parameters, coding schemes, or modulation orders based on individual device characteristics, thereby achieving precise device-specific optimization while maintaining service-level adaptability
3Device complexity
If limited air interface parameters can be configured, then device complexity is reduced, but transmission optimization capability deteriorates
Solution Approach 1:
The air interface configuration is divided into multiple independent parameter groups including waveform parameters, frame structure, multiple access schemes, coding schemes, and modulation schemes. This segmentation allows the system to manage complexity through modular configuration while providing comprehensive transmission optimization by adjusting multiple parameters across different layers of the communication protocol
Solution Approach 2:
The system utilizes parameter changes across multiple air interface dimensions to achieve transmission optimization. By adjusting parameters such as subcarrier spacing, cyclic prefix length, resource block allocation, coding rate, and modulation order, the system can optimize transmission performance for different device capabilities and channel conditions without requiring fundamental changes to the air interface architecture
Data Source
AI summary
Methods and devices utilizing artificial intelligence (AI) or machine learning (ML) for customization of a device specific air interface configuration in a wireless communication network are provided. An over the air information exchange to facilitate the training of one or more AI/ML modules involves the exchange of AI/ML capability information identifying whether a device supports AI/ML for optimization of the air interface.