Generative SINR Estimation for Dynamic 5G Link Adaptation
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately and efficiently adapting to dynamic channel conditions due to time-varying impairments and user demand variations, particularly in 5G NR environments, leading to inefficiencies in resource allocation and user throughput optimization.
Innovation Solution
A system utilizing a convolutional neural network (CNN) and long short-term memory (LSTM) cascade for generative signal-to-interference-plus-noise ratio (SINR) estimation, combined with a variational autoencoder, to predict SINR values for tessellated cellular network sectors, enabling dynamic link adaptation and resource allocation based on real-time user equipment data and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional link adaptation methods are used to maintain constant target BLER, then communication reliability is preserved, but data transmission rate optimization deteriorates due to time-varying channel conditions
Solution Approach 1:
The patent implements dynamic link adaptation by using machine learning models to predict future channel conditions and proactively adjust modulation and coding schemes. Instead of reacting to channel changes after they occur, the system dynamically adapts transmission parameters based on predicted SINR values, allowing it to maintain reliability while optimizing data rates through forward-looking adjustments.
Solution Approach 2:
The system performs preliminary action by predicting channel conditions before actual transmission occurs. The machine learning model estimates future SINR values and pre-adjusts link adaptation parameters, enabling the system to prepare for upcoming channel variations and maintain optimal performance without waiting for channel degradation to occur.
2Measurement precision
If frequent channel quality measurements are performed to capture time-varying conditions, then link adaptation accuracy improves, but system complexity and processing overhead increase
Solution Approach 1:
The patent introduces machine learning models as intermediary components that process channel quality information. Instead of directly using raw measurements for link adaptation decisions, the ML models act as intermediaries that learn from historical data and provide refined predictions, reducing the need for frequent measurements while maintaining or improving accuracy.
Solution Approach 2:
The system uses machine learning models to create virtual copies or predictions of channel conditions based on historical patterns. Rather than continuously measuring actual channel quality, the system generates predicted channel states that mirror real conditions, reducing measurement overhead while maintaining adaptation accuracy.
3Reliability
If conservative modulation and coding schemes are used to ensure reliable transmission, then block error rate is maintained, but spectral efficiency deteriorates under good channel conditions
Solution Approach 1:
The system dynamically adjusts modulation and coding schemes based on predicted channel conditions. When channel quality is good, it uses higher-order modulation and more aggressive coding to maximize spectral efficiency. When conditions deteriorate, it transitions to more conservative schemes, maintaining reliability only when necessary rather than consistently.
Solution Approach 2:
The patent changes transmission parameters (modulation order, coding rate) based on predicted SINR values. The machine learning model enables the system to identify when parameter changes are beneficial, allowing aggressive parameter changes during good conditions for maximum efficiency while maintaining conservative parameters only when reliability is at risk.
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.