Cell-Specific Downlink Link Adaptation Using SINR Prediction
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Solution Overview
Problem
Existing link adaptation techniques in wireless communication systems face challenges due to inaccurate channel quality information and computational delays, leading to suboptimal modulation and coding scheme selection, especially in dynamic 5G NR environments with diverse application scenarios.
Innovation Solution
A data-driven approach using deep reinforcement learning (DRL) with variational autoencoders and CNN-LSTM models for SINR prediction, combined with historical data and location-based analysis, to enhance channel estimation and optimize modulation and coding schemes in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional link adaptation techniques are used, then system complexity is low, but link adaptation accuracy deteriorates due to inaccurate channel quality information and computational delays
Solution Approach 1:
The patent replaces traditional mechanical/link-adaptation algorithms with machine learning models (CNN-LSTM networks) that process channel quality information and predict optimal modulation and coding schemes. This substitution enables more accurate link adaptation decisions by leveraging patterns learned from historical data, directly addressing the accuracy-complexity tradeoff.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between channel quality measurement and modulation/coding scheme selection. These models act as mediators that process raw channel quality information and transform it into optimized transmission parameter recommendations, improving overall system performance while managing complexity through modular architecture.
2Measurement precision
If deep reinforcement learning models are used for real-time optimization, then link adaptation accuracy is improved, but computational delay increases
Solution Approach 1:
The patent employs offline training of deep reinforcement learning models using historical channel data and performance outcomes. By pre-training the models beforehand, the system avoids heavy computational burdens during real-time operation, as the models only need to perform inference rather than full training computations, thus reducing operational latency.
Solution Approach 2:
The patent implements dynamic model updating mechanisms where the deep reinforcement learning models are periodically retrained with newly acquired channel data. This dynamic approach allows the system to adapt to changing channel characteristics over time while maintaining computational efficiency by updating models at optimized intervals rather than continuously.
3Productivity
If frequent channel quality reporting is implemented, then link adaptation timeliness is improved, but network overhead increases
Solution Approach 1:
The patent designs the machine learning models to process multiple types of input data (channel quality indicators, signal strength, interference levels, historical performance data) through a unified framework. This multi-functionality allows the system to extract maximum value from each reporting instance, improving link adaptation timeliness without proportionally increasing reporting frequency or network overhead.
Data Source
AI summary
Use of a data-driven approach that assimilates historical signal to interference-plus noise ratio (SINR) and channel estimation data along with location-map of the cell in which base station equipment is situated to better define the relationship between SINR and the user-channel environmental map and spatio-temporal changes to it to achieve more granular, cell site-specific modeling is disclosed herein. This data-driven approach estimates SINR using variational autoencoders. Variational encoders typically consist of two sections, an encoder section and decoder section. The encoder section learns the distribution on the low-dimensional latent space over the input data samples. The decoder section is a generative model that learns the joint distribution of the latent variables and input data.