Air Interface Reporting for Adaptive Uplink Resource Allocation
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing air interface resources, particularly in heterogeneous networks with varying traffic loads and device capabilities, leading to suboptimal performance and resource utilization.
Innovation Solution
Implementing a model-based air interface management system that dynamically adjusts resource allocation based on traffic characteristics, device capabilities, and network conditions, using machine learning algorithms to optimize communication protocols and enhance network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic resource allocation is implemented to adapt to varying traffic loads and device capabilities, then resource utilization and network efficiency are improved, but system complexity and computational overhead increase
Solution Approach 1:
The system pre-configures multiple air interface parameter sets (e.g., modulation schemes, coding rates, transmission modes) before actual communication occurs. These pre-configured parameters are stored in the device and network side, allowing rapid selection without real-time complex calculations. This preliminary preparation enables adaptive resource allocation while reducing computational overhead during active communication.
Solution Approach 2:
The invention changes discrete parameter values from a predefined set rather than continuously optimizing. The system selects from predetermined parameter combinations (e.g., specific modulation-coding scheme pairs) based on traffic characteristics and device capabilities. This approach achieves adaptation through parameter switching rather than complex real-time optimization, improving resource utilization while controlling system complexity.
2Reliability
If machine learning algorithms are used to optimize communication protocols, then network performance is enhanced, but processing time and computational resources increase
Solution Approach 1:
Machine learning models are trained offline in advance to recognize traffic patterns and predict optimal parameters. The trained model weights and decision boundaries are deployed to the network side, where they enable rapid classification and parameter selection without executing complex learning algorithms in real-time. This preliminary training eliminates the need for time-consuming online computation while maintaining high network performance.
Solution Approach 2:
The invention replaces complex real-time machine learning computation with simplified rule-based decision logic derived from trained models. Instead of continuously running ML algorithms to optimize protocols, the system uses pre-computed model outputs to select from predefined parameter sets. This substitution of computational mechanisms maintains predictive accuracy while dramatically reducing processing time and computational resource requirements.
3Productivity
If air interface parameters are adjusted to match device capabilities, then communication efficiency is improved, but signaling overhead and configuration complexity increase
Solution Approach 1:
The system segments air interface parameters into distinct, predefined categories (modulation scheme, coding rate, transmission mode, MIMO configuration). Each parameter is independently selected from standardized values rather than being individually negotiated through complex signaling. This segmentation allows efficient matching of device capabilities to appropriate parameter sets while reducing the amount of detailed signaling required, as devices can indicate capability levels that map directly to predefined parameter combinations.
Data Source
AI summary
A wireless device receives, from a base station, configuration parameters for model performance reporting and/or channel state information (CSI) reporting. The configuration parameters indicating one or more conditions for transmitting a model performance report and/or a CSI report and an indication of an uplink resource. The wireless device transmits an uplink signal via the uplink resource based on the one or more conditions being met.