Empirical Mutual Information Estimation for Wireless Rate Control
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Solution Overview
Problem
Current wireless communication systems, particularly in 5G NR, face challenges in optimizing transmission rates due to non-Gaussian interference, where traditional rate estimation methods based on signal-to-interference ratio are ineffective, leading to suboptimal modulation and coding schemes.
Innovation Solution
The implementation of empirical mutual information (MI) estimation using pseudo-random data modulated with different orders, allowing wireless devices to determine optimal modulation and coding schemes based on actual interference conditions, thereby improving rate control and communication quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rate estimation methods based on signal-to-interference ratio are used, then the system is simple to implement, but the estimation accuracy deteriorates under non-Gaussian interference
Solution Approach 1:
The patent changes the estimation parameter from signal-to-interference ratio to empirical mutual information, which directly measures the achievable information transfer rate. This parameter change enables accurate rate estimation under non-Gaussian interference by capturing the actual information transfer capability rather than relying on statistical ratios that fail under non-Gaussian conditions
Solution Approach 2:
The patent replaces the traditional statistical estimation mechanism (signal-to-interference ratio) with an information-theoretic measurement mechanism (empirical mutual information). This substitution allows direct measurement of information transfer rates through training signals and mutual information calculation, bypassing the limitations of statistical ratio-based methods under non-Gaussian interference
2Productivity
If empirical MI estimation with multiple modulation orders is used, then the transmission rate optimization improves, but the measurement and estimation process becomes more complex
Solution Approach 1:
The patent performs preliminary actions by transmitting training signals with different modulation orders before actual data transmission. The receiving device calculates empirical mutual information for each modulation order in advance, identifies the optimal modulation order, and reports it back. This preliminary estimation enables the transmitting device to select the optimal modulation and coding scheme for subsequent high-rate transmission without real-time trial and error
Solution Approach 2:
The patent introduces training signals as an intermediary element that facilitates mutual information estimation. These training signals carry known data that enables the receiving device to calculate empirical mutual information accurately. The training signals act as a bridge between the transmitting and receiving devices, enabling rate optimization without requiring complex real-time measurements of actual data transmission
Data Source
AI summary
A first wireless device may generate a first pseudo-random data based on a seed known to a second wireless device, and may transmit a first training signal including first pseudo-random data to the second wireless device for a MI estimation at the second wireless device, the first pseudo-random data being modulated with a first modulation order. The second wireless device may estimate, based on the received first training signal and through the MI estimation, a reception quality associated with at least one modulation order lower than or equal to the first modulation order, and determine a second modulation order of the at least one modulation order lower than or equal to the first modulation order based on the MI estimation, the second modulation order being estimated to provide a reception quality greater than or equal to a reception quality threshold. The MI estimation may be periodic or aperiodic.