RL-Enabled LDPC Decoder for Adaptive Channel Error Correction
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Solution Overview
Problem
Existing error correcting code (ECC) systems in data storage and transmission have static configurations that are sub-optimal and fail to adapt to variations in data or channels, leading to inefficient processing, reduced data throughput, and excessive power consumption.
Innovation Solution
A reinforcement learning-enabled low-density parity check (LDPC) decoder that uses machine learning algorithms to adaptively assist in the LDPC decoding process by interacting with an RL agent, generating LDPC state information and obtaining decoding parameters to improve decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static ECC configuration is used, then device complexity is reduced, but productivity decreases due to excessive processing and inability to adapt to channel variations
Solution Approach 1:
The patent implements dynamic configuration of LDPC decoder parameters by integrating a reinforcement learning agent that continuously adapts decoding parameters (such as threshold values and iteration counts) based on real-time channel conditions. This transforms the static decoder into a dynamic system that optimizes its operation according to varying signal-to-noise ratios and error patterns, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The reinforcement learning agent modifies key operational parameters of the LDPC decoder including threshold values for bit flipping, maximum iteration limits, and syndrome calculation parameters. By dynamically changing these parameters based on learned channel characteristics, the system achieves adaptability without requiring complete redesign of the decoder architecture, thus managing complexity while improving performance.
2Productivity
If static ECC configuration is used, then device complexity is reduced, but data throughput decreases due to inefficient processing
Solution Approach 1:
The reinforcement learning agent performs preliminary learning during idle periods or low-load conditions, storing optimized parameter configurations for various channel conditions. When actual decoding is required, the pre-learned parameters are directly applied, enabling fast adaptation without adding real-time processing overhead. This preliminary action resolves the contradiction by preparing optimization data in advance.
Solution Approach 2:
The system implements feedback loops where decoding performance metrics (success rate, iteration count, error patterns) are continuously monitored and fed back to the reinforcement learning agent. This feedback enables the agent to refine parameter selections and improve throughput over time without increasing immediate processing complexity, as the learning occurs iteratively based on accumulated performance data.
3Reliability
If more decoding iterations are performed, then decoding success rate increases, but loss of time increases
Solution Approach 1:
The reinforcement learning agent determines optimal iteration limits and threshold values that achieve sufficient decoding success without performing excessive iterations. By learning the minimum effective processing required for different channel conditions, the system avoids both insufficient decoding (low success rate) and excessive decoding (time loss), resolving the contradiction through optimized partial action.
Solution Approach 2:
The decoder operates with periodic iteration cycles determined by the reinforcement learning agent, which adjusts the number of iterations based on syndromes and error patterns detected during decoding. This periodic adaptation allows the system to terminate decoding early when successful (reducing time loss) while ensuring sufficient iterations when needed (maintaining success rate).
Data Source
AI summary
The present disclosure describes apparatuses and methods for implementing a reinforcement learning-enabled low-density parity check (LDPC) decoder. In aspects, an RL-enabled LDPC decoder processes, as part of a first decoding iteration, data of a channel to generate LDPC state information and provides the LDPC state information to a machine learning (ML) algorithm of an RL agent. The RL-enabled LDPC decoder is then configured with LDPC decoding parameters obtained from the ML algorithm and processes, as part of a second decoding operation, the data using the decoding parameters to generate subsequent LDPC state information. The RL-enabled LDPC decoder provides decoded data of the channel based on the subsequent LDPC state information. By using the LDPC decoding parameters provided by the ML algorithm of the RL agent, the RL-enabled LDPC decoder may decode channel data in fewer decoding iterations or with a higher success rate, thereby improving LDPC decoding performance.