Decoder Success Prediction for Power-Efficient MIRS Scheduling
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Solution Overview
Problem
MIRS-based rate adaptation systems in wireless communication consume excessive power due to a high number of unsuccessful decoding attempts, leading to inefficient power consumption and hardware dimensioning.
Innovation Solution
Implement a decoder success predictor method in user equipment (UE) to identify likely failed code blocks, allowing the UE to avoid decoding them, and signal this capability to the network node, which optimizes MIRS retransmissions based on the prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If MIRS-based rate adaptation is implemented to improve communication reliability, then decoding success rate is improved, but power consumption increases due to excessive unsuccessful decoding attempts
Solution Approach 1:
The patent applies preliminary action by implementing a decoder success predictor that forecasts decoding outcomes before actual decoding attempts. The predictor analyzes code block characteristics and channel conditions in advance to identify likely failed decodings, allowing the system to skip unnecessary decoding operations and reduce power consumption while maintaining reliability
Solution Approach 2:
The patent implements feedback by using actual decoding results to update and refine the predictor model. The system continuously learns from past decoding successes and failures, adjusting prediction thresholds and parameters to improve accuracy over time, thereby optimizing the balance between reliability and power consumption
2Reliability
If MIRS-based rate adaptation is implemented to improve communication reliability, then decoding success rate is improved, but hardware requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the decoding process into two independent parts: a lightweight predictor stage that filters out likely failed decodings, and the actual decoding stage that only processes predicted successful blocks. This segmentation reduces hardware complexity by avoiding full decoding hardware for all code blocks while maintaining high reliability through selective decoding
3Use of energy by moving object
If decoder success prediction capability is added to avoid unsuccessful decoding attempts, then power consumption is reduced, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary predictor component that sits between the receiver and decoder. This intermediary analyzes code block metadata and channel state information to generate prediction decisions without requiring complex modifications to the core decoding hardware, thus reducing power consumption while adding minimal system complexity
Solution Approach 2:
The patent uses parameter changes by adjusting prediction thresholds and decision criteria based on channel conditions and traffic patterns. The system dynamically modifies predictor parameters to optimize performance for different scenarios, reducing power consumption without requiring fundamentally different system architectures
Data Source
AI summary
A user equipment (UE) may transmit, to a network node, an indication of support for a decoder success prediction capability. The network node may obtain the indication of support by the UE for the decoder success prediction probability. The network node may output a transmission for the UE including one or more parameters based on the indication of support by the UE for the decoder success prediction capability. The UE may receive the transmission including one or more code blocks based on the indication of support for the decoder success prediction capability. The network node may optimize a multiple incremental redundancy scheme (MIRS) schedule for at least one of transmitting the transmission or retransmitting the transmission based on the indication of support by the UE for the decoder success prediction capability. The transmission may include the MIRS schedule.