ML Decodability Prediction for Low-Latency Wireless Decoding
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Solution Overview
Problem
Current wireless communication systems face challenges in predicting decodability, which affects latency due to time-consuming decoding processes, especially in high-throughput and low-latency services.
Innovation Solution
An apparatus and method utilizing a machine learning model with trainable parameters to predict decodability by classifying received modulated symbols, determining the number of decoding rounds needed, and deciding on retransmissions based on channel quality and complexity, employing techniques like cross entropy loss and Monte-Carlo simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional decoding processes are used to ensure accurate data reception, then reliability is improved, but latency increases due to time-consuming decoding
Solution Approach 1:
The system performs preliminary classification of received modulated symbols using a machine learning model to predict decodability and estimate the number of decoding rounds needed before actual decoding begins. This preliminary action allows the system to prepare appropriate decoding configurations in advance, reducing the actual decoding time while maintaining reliability.
Solution Approach 2:
The system dynamically adjusts the decoding process based on the predicted decodability and estimated number of rounds. The decoding configuration is adapted in real-time according to the channel conditions and signal characteristics, allowing the system to optimize between reliability and latency for each specific transmission.
2Reliability
If the number of decoding rounds is increased to improve decoding accuracy, then reliability is improved, but processing time increases
Solution Approach 1:
The machine learning model estimates the number of decoding rounds needed before decoding begins, allowing the system to perform the necessary number of rounds in advance rather than using a fixed or excessive number of rounds. This preliminary estimation ensures that decoding accuracy is maintained while minimizing processing time.
Solution Approach 2:
The system changes the number of decoding rounds parameter dynamically based on the predicted decodability and channel conditions. Instead of using a static number of rounds, the system adapts this parameter to match the actual difficulty of decoding each transmission, optimizing both accuracy and processing time.
3Productivity
If machine learning models are used to predict decodability, then processing efficiency is improved, but device complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary between the received signal and the decoding process. It analyzes the modulated symbols and provides predictions about decodability and required processing rounds, simplifying the overall system architecture by separating the prediction function from the decoding function and improving processing efficiency through intelligent decision-making.
Data Source
AI summary
Different solutions for an apparatus comprising a predictor predicting decodability of received symbols are disclosed. Decoding is performed based on the prediction.