AI-Enabled Filters for Wireless QoS Adaptation
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Solution Overview
Problem
Current wireless communication systems face challenges in dynamically managing quality of service (QoS) and optimizing resource allocation due to the complexity of channel conditions and varying user equipment (UE) capabilities, leading to inefficiencies in data transmission and processing.
Innovation Solution
The implementation of AI-enabled filters within wireless transmit/receive units (WTRUs) that utilize machine learning algorithms to dynamically select and configure transmission parameters and resources based on real-time link conditions and contextual information, enabling adaptive QoS management and optimized data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional rule-based components are used for QoS management and resource allocation, then system complexity is reduced and ease of operation is improved, but adaptability to dynamic channel conditions and varying UE capabilities deteriorates
Solution Approach 1:
The patent segments the QoS management system into multiple AI filters operating at different protocol layers (L1, L2, L3). Each filter handles specific functions such as channel condition analysis, resource allocation decisions, and QoS parameter adjustment. This segmentation allows the system to achieve high adaptability through specialized AI components while managing complexity by distributing functions across layers rather than implementing a monolithic complex system.
Solution Approach 2:
The patent introduces AI filters as intermediary components between the physical channel conditions and the QoS management decisions. These filters act as mediators that process raw channel information and translate it into actionable QoS parameters and resource allocation decisions. The intermediary AI layer absorbs the complexity of adapting to dynamic conditions while presenting simplified control interfaces to higher-layer protocols.
2Productivity
If AI filters are implemented to dynamically manage QoS and optimize resource allocation, then adaptability and network performance are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent implements dynamic AI filters that continuously adapt their parameters and behavior based on real-time channel conditions and traffic patterns. The filters dynamically adjust QoS parameters, resource allocation decisions, and processing priorities according to current network state. This dynamic behavior enables high data transmission efficiency while the filters learn to optimize their processing requirements through adaptive mechanisms rather than requiring fixed high complexity.
Solution Approach 2:
The patent utilizes parameter changes in the AI filter configurations to optimize the balance between processing complexity and transmission efficiency. The system dynamically adjusts filter parameters such as window sizes, threshold values, and model complexity based on network conditions and device capabilities. This allows the system to achieve high productivity when needed while reducing processing requirements under constrained conditions through parameter optimization.
3Measurement precision
If AI filters process information at multiple protocol layers, then measurement precision and QoS management accuracy are improved, but loss of time due to increased processing overhead increases
Solution Approach 1:
The patent implements preliminary action through pre-trained AI filter models and pre-configured processing pipelines at multiple protocol layers. The AI filters are pre-trained on extensive channel condition data and traffic patterns before deployment, enabling them to make accurate measurements and decisions with minimal real-time processing. The system performs preliminary analysis of channel conditions and traffic patterns using the AI filters, preparing QoS parameters and resource allocation decisions in advance to reduce real-time processing time while maintaining high measurement precision.
Data Source
AI summary
Methods, apparatus and systems are disclosed. One method may include a wireless transmit/receive unit (WTRU) receiving a transmission including a data unit (DU) on a first set of resources. The WTRU may select an artificial intelligence (AI) filter based on the first set of resources and input the DU or a part of the DU to the selected AI filter. The WTRU may perform AI filtering on the inputted DU or part thereof to output any of: a set of AI-based transmission parameters or an AI-processed DU. The AI-processed DU may include: a first portion of the DU processed by the AI filter and a second portion of the DU processed by a rule-based component, or the DU processed by the AI filter. The WTRU may transmit any of: the AI-processed DU using a set of rule-based transmission parameters, or a rule-based DU using the AI-based transmission parameters.