Radio Link Parameter Control for CSI Delay and BLER Prediction
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Solution Overview
Problem
Existing radio communication systems face delays and inaccuracies in determining radio parameters like MCS due to discrepancies between the time of CSI measurement and transmission, leading to potential mismatches in target communication quality.
Innovation Solution
A control apparatus predicts a probability distribution of radio quality using machine learning, calculates expected BLER or reliability, and adjusts transmission parameters to ensure target communication quality by considering statistical variations in radio conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If MCS is determined based on CSI report received from radio terminal, then downlink transmission can be performed, but there is a delay between CSI measurement and MCS determination leading to radio quality mismatch
Solution Approach 1:
The system performs preliminary actions by predicting the probability distribution of radio quality at the time of transmission before actually transmitting. This prediction is made in advance using the CSI measurement and statistical models, allowing the base station to select MCS based on predicted future conditions rather than relying solely on past measurements, thus reducing the effective delay impact.
Solution Approach 2:
The system uses feedback from CSI reports and actual transmission outcomes to continuously refine the statistical models and probability distributions. By incorporating feedback about actual BLER rates and channel conditions, the system improves its predictions over time, maintaining reliability despite the inherent delay in the measurement-determination cycle.
2Device complexity
If MCS is determined based on single point CSI measurement, then determination is simple, but measurement errors cause radio quality mismatch
Solution Approach 1:
The system changes the approach from using a single deterministic CSI measurement value to using a probability distribution of possible radio quality values. This parameter transformation allows the system to account for measurement uncertainties and variations by working with statistical parameters (mean, variance) rather than single point estimates, improving robustness without significantly increasing complexity.
Solution Approach 2:
The probability distribution acts as an intermediary between the noisy CSI measurement and the final MCS selection. Instead of directly mapping a potentially erroneous single measurement to MCS, the system first transforms the measurement into a probability distribution that captures uncertainty, then uses this distribution to make more robust MCS decisions that are less sensitive to measurement errors.
3Measurement precision
If probability distribution prediction is performed, then BLER estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system implements partial probability distribution prediction by focusing computational resources on the most relevant aspects of the distribution needed for MCS selection. Rather than computing the complete distribution with all possible details, the system calculates only the necessary statistical moments (mean, variance) that directly impact BLER estimation and MCS decision-making, achieving sufficient accuracy with reduced computational burden.
Data Source
AI summary
A control apparatus predicts a probability distribution of radio quality when a downlink transmission from a base station to a radio terminal or an uplink transmission from a radio terminal to a base station is to be performed. The control apparatus calculates an expected value of a block error rate or reliability in the downlink or uplink transmission using the predicted probability distribution. The control apparatus determines a value of each of one or more parameters related to the downlink or uplink transmission, taking into account the calculated expected value.