Wireless Transmission Error Rate Prediction for URLLC
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Solution Overview
Problem
Existing wireless packet scheduling and transmission techniques require significant computing resources and time, and there is a need for improved approaches to enhance efficiency and reliability, particularly for ultra-reliable low-latency communications (URLLC) packets.
Innovation Solution
The system predicts a block error rate (BLER) and generates a packet transmission order metric for URLLC packets based on the predicted BLER, using signal-to-noise (SNR) measurements to select an appropriate modulation and coding scheme (MCS) and number of resource blocks (RBs) for transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional wireless packet scheduling and transmission techniques are used, then transmission reliability can be maintained, but computational overhead and processing time increase significantly
Solution Approach 1:
The patent extracts the BLER prediction function from the traditional complex scheduling algorithm, implementing it as a separate, simplified module that uses only SNR measurements and pre-stored calibration data. This isolation reduces the computational burden on the main scheduling system while maintaining reliable transmission through accurate error rate prediction.
Solution Approach 2:
The system performs preliminary calibration by pre-computing and storing BLER values for different SNR conditions, MCS levels, and packet lengths in lookup tables. During actual transmission scheduling, the system only needs to query these pre-computed values rather than performing complex real-time calculations, significantly reducing computational overhead while maintaining prediction accuracy.
2Reliability
If complex scheduling algorithms are used to optimize URLLC packet transmission, then transmission reliability improves, but processing time increases
Solution Approach 1:
The patent creates simplified copies of BLER prediction models for different packet types and channel conditions, storing them as pre-computed lookup tables. During URLLC scheduling, the system quickly retrieves the appropriate pre-computed model rather than performing full calculations, dramatically reducing processing time while maintaining the reliability needed for URLLC applications.
Solution Approach 2:
The system performs preliminary calibration and model generation offline, preparing BLER prediction models for various SNR conditions, MCS levels, and packet configurations before actual URLLC transmission. This pre-computation eliminates the need for complex real-time calculations during time-critical URLLC scheduling operations.
3Productivity
If BLER prediction and MCS selection are performed in real-time, then transmission optimization improves, but computational resources are consumed
Solution Approach 1:
The system performs comprehensive BLER calibration and model generation in advance, storing results in pre-computed lookup tables that map SNR values, MCS levels, and packet lengths to predicted BLER values. During real-time transmission optimization, the system only performs simple table lookups and comparisons, dramatically reducing computational resource consumption while maintaining high transmission optimization efficiency.
Solution Approach 2:
The patent uses lightweight, pre-computed BLER prediction models that can be quickly generated and discarded for different channel conditions, rather than maintaining complex persistent models. These simple prediction models consume minimal computational resources during real-time operation while providing sufficient accuracy for MCS selection and transmission optimization.
Data Source
AI summary
Apparatuses, systems, and techniques to predict wireless transmission error rates. In at least one embodiment, a processor includes one or more circuits to predict one or more wireless transmission error rates based, at least in part, on one or more signal to noise ratio (SNR) measurements.