Reference Signal Tone Placement for Adaptive 5G Spectral Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The pre-defined tone patterns for reference signals in 5G communication systems are not optimized for all environments, leading to suboptimal signal and spectral efficiency, and increased latency.
Innovation Solution
Dynamic determination of optimized tone patterns for reference signal transmission using machine learning models, which are trained to associate various conditions with the best Resource Elements (REs) for signal placement, improving spectral efficiency and reducing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-defined tone patterns are used for reference signal transmission, then device complexity is reduced and ease of operation is improved, but signal efficiency and spectral efficiency deteriorate and latency increases
Solution Approach 1:
The patent implements dynamic tone pattern selection by training machine learning models to determine optimal tone patterns based on real-time channel conditions, mobility states, and environmental parameters. The system transitions from static pre-defined patterns to adaptive dynamic patterns that optimize signal efficiency while maintaining operational simplicity through automated model-based decision-making
Solution Approach 2:
The system changes the parameters of tone patterns dynamically by selecting different patterns from a library based on channel conditions, mobility states, and environmental factors. The machine learning models predict optimal parameter configurations for tone patterns, allowing the system to adapt to varying conditions and improve signal efficiency without increasing operational complexity
2Device complexity
If pre-defined tone patterns are used for reference signal transmission, then device complexity is reduced, but spectral efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline with extensive channel condition data, mobility patterns, and environmental parameters. The trained models are then deployed in the device, enabling real-time optimal tone pattern selection without adding operational complexity. The heavy computational work is performed in advance during model training, while inference at runtime remains efficient
Solution Approach 2:
The system creates optimized copies of tone patterns for different channel conditions and scenarios. Instead of using a single pre-defined pattern, the system maintains multiple pattern copies optimized for specific conditions (e.g., high mobility, low mobility, urban environments, rural environments) and selects the appropriate copy based on real-time conditions, improving spectral efficiency while keeping the selection process simple
3Ease of manufacture
If pre-defined tone patterns are used for reference signal transmission, then implementation simplicity is improved, but latency increases
Solution Approach 1:
The system implements dynamic tone pattern adaptation that responds rapidly to changing channel conditions and mobility states. The machine learning models process current conditions and select optimal patterns in real-time, reducing the latency associated with using fixed pre-defined patterns that may become suboptimal as conditions change. The dynamic selection enables faster convergence to optimal performance
4Productivity
If optimized tone patterns are determined dynamically using machine learning, then signal efficiency and spectral efficiency are improved, but device complexity increases
Solution Approach 1:
The patent resolves the complexity issue by performing the complex machine learning model training in advance offline. The training process, which involves extensive computation and data processing, is completed before deployment. The trained model is then embedded in the device, allowing real-time optimal tone pattern selection with minimal runtime computation, thus improving signal efficiency without significantly increasing operational device complexity
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
Disclosed are techniques for determining tone patterns for transmission of reference signals. One or more parameters associated with communications with a base station over a time duration, may be determined by a user equipment. The user equipment may determine, based on the one or more parameters, a tone pattern for a reference signal for use in communications with the base station over a future time duration. The user equipment may then transmit the tone pattern to the base station. The user equipment may then receive the reference signal from the base station, over the tone patterns.