Iterative Data Detection With Adaptive Decoding Depth
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Solution Overview
Problem
Existing data transfer systems often require multiple iterations for data detection and decoding, which can be wasteful and insufficient, leading to power consumption and latency issues, especially when the input data fails to converge in a single iteration.
Innovation Solution
A variable iteration data processing system with a first detector, a second detector, a decoder, and a queuing buffer that performs data detection and decoding only if the initial output fails to converge, allowing for dynamic adjustment of iterations based on convergence and resource availability, minimizing unnecessary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If two iterations are performed for each input data set, then the possibility of convergence is heightened, but power consumption increases and latency is introduced
Solution Approach 1:
The system dynamically adjusts the number of iterations based on convergence status. The controller monitors whether the output data converged after the first iteration and selectively enables the second iteration only when needed, transforming the static two-iteration approach into a dynamic adaptive process that optimizes power consumption while maintaining convergence capability.
2Reliability
If two iterations are performed for each input data set, then the possibility of convergence is heightened, but latency increases
Solution Approach 1:
The system implements dynamic iteration control where the second iteration is conditionally executed based on convergence status. This reduces latency for data sets that converge in the first iteration while maintaining the ability to perform a second iteration when convergence is not achieved, thus optimizing the time-performance tradeoff.
Solution Approach 2:
The system applies different processing depths to different data sets based on their individual convergence characteristics. Converged data sets receive minimal processing (one iteration) while non-converged data sets receive extended processing (two iterations), avoiding uniform over-processing and reducing overall latency.
3Use of energy by moving object
If a single iteration is performed, then power consumption is reduced and latency is minimized, but convergence may not be achieved
Solution Approach 1:
The system incorporates feedback mechanisms where the controller monitors the convergence status of the output data after each iteration. Based on this feedback, the controller dynamically decides whether to enable the second iteration, ensuring that power is not wasted on converged data while sufficient processing is applied to non-converged data.
4Reliability
If fixed two-stage detection and decoding is implemented, then convergence is improved, but device complexity increases
Solution Approach 1:
The system uses dynamic control logic to manage the complexity of having two detection and decoding stages. The controller adaptively enables or disables the second stage based on convergence status, effectively managing the complexity-convergence tradeoff by activating additional processing resources only when necessary.
Data Source
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AI summary
Various embodiments of the present invention provide systems and methods for data processing. For example, a variable iteration data processing system is disclosed that includes at least a first detector, a second detector, a decoder, and a queuing buffer. The first detector is operable to perform a data detection on an input data set at a first time. The decoder receives a derivation of an output from the first detector and performs a decoding process. Where the decoding process fails to converge, the decoder output is passed to the second detector for a subsequent detection and decoding process at a second time.