Iterative Equalization With Adaptive Decoder Iteration Control
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Solution Overview
Problem
Iterative equalization systems in communication and data storage often operate inefficiently, leading to unnecessary power usage due to extraneous iterations at the channel detector and iterative decoder, which is particularly undesirable in low power or mobile devices.
Innovation Solution
A method and circuitry for power-efficient iterative equalization that adjusts the number of decoding iterations based on a decision metric, such as syndrome weight or hard decisions, to reduce power consumption before satisfying a reliability criterion, allowing for early return to the channel detector or additional inner iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed number of iterations is used in the decoding process, then the decoding reliability is ensured, but the power consumption increases due to unnecessary iterations
Solution Approach 1:
The patent applies dynamics by making the number of decoding iterations adjustable rather than fixed. The iterative decoder dynamically changes the number of iterations based on a decision metric (such as syndrome weight or hard decisions) computed during the decoding process. This allows the system to adapt the decoding depth to the actual channel conditions, ensuring reliability when needed while reducing power consumption when the channel quality is good and fewer iterations suffice.
Solution Approach 2:
The patent changes the parameter of iteration count based on computed decision metrics. Instead of using a predetermined fixed number of iterations, the system computes metrics such as syndrome weight or hard decision confidence during the decoding process and adjusts the iteration count accordingly. This parameter change enables the system to terminate decoding early when reliability is already achieved, avoiding unnecessary power consumption from extra iterations.
2Reliability
If extraneous iterations are performed at the channel detector or iterative decoder, then decoding thoroughness is improved, but power usage increases unnecessarily
Solution Approach 1:
The patent implements feedback by computing a decision metric during the decoding process and using this metric to determine whether to continue or terminate iterations. The iterative decoder computes metrics such as syndrome weight or hard decision confidence after each iteration and feeds this information back to the control logic. Based on this feedback, the system decides whether to perform additional iterations or to stop, thereby avoiding extraneous iterations that would waste power while ensuring sufficient decoding thoroughness.
Solution Approach 2:
The decoding system performs self-service by autonomously determining when to terminate the decoding process based on internally computed decision metrics. The iterative decoder monitors its own decoding progress through metrics like syndrome weight and hard decisions, and automatically decides when the decoding is sufficient without requiring external control or fixed iteration counts. This self-service mechanism eliminates unnecessary iterations and reduces power consumption.
3Reliability
If the number of iterations is increased to ensure decoding accuracy, then error detection and correction capability is improved, but the system efficiency decreases
Solution Approach 1:
The patent applies dynamics by making the iteration count adaptive rather than static. The system dynamically adjusts the number of iterations based on real-time decoding conditions and computed metrics. When error patterns are simple or channel conditions are good, the system terminates decoding early, maintaining high efficiency. When errors are complex or channel conditions are poor, the system performs additional iterations to ensure accurate error detection and correction, thus maintaining both productivity and reliability.
Solution Approach 2:
The patent changes the iteration parameter based on computed decision metrics such as syndrome weight and hard decisions. This parameter change allows the system to optimize the trade-off between decoding accuracy and efficiency. By adjusting the iteration count according to actual decoding needs rather than using a fixed high number, the system maintains error detection and correction capability while significantly improving overall productivity and system efficiency.
Data Source
AI summary
Systems and methods for power efficient iterative equalization on a channel are provided. An iterative decoder decodes received data from a channel detector using a decoding process. The decoder computes a decision metric based on the decoded data and adjusts the number of iterations of the decoding process based on the decision metric. The adjustment occurs prior to a reliability criterion for the decoded data being satisfied. The decoder may pass control back to the channel detector if the adjusted number of iterations has occurred or if the reliability criterion is satisfied. Adjusting the number of iterations of the decoding process may include increasing the number of iterations from a predetermined number of iterations. The decision metric may be based on syndrome weight or hard decisions. The decision metric may be chosen to reduce average power consumption of the detector, the decoder, or circuitry including the detector and the decoder.