Multilevel Convolutional Decoder With Soft Bit Selection
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Solution Overview
Problem
Convolutional decoders, particularly Viterbi decoders, require significant computational power, which is prohibitive for low-power devices like battery-operated equipment, and simpler decoding schemes like systematic codes have limited performance.
Innovation Solution
A decoder that utilizes a shift register, one-bit quantizers, and exclusive OR gates to process multilevel signals derived from polynomial functions, allowing for a 'soft' decoding approach that selects the bit with the largest magnitude to reduce power consumption and maintain decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Viterbi decoder is used to decode convolutional codes, then decoding accuracy is improved, but power consumption and computational complexity increase significantly
Solution Approach 1:
The Viterbi decoder is segmented into two parts: a soft Viterbi decoder for high accuracy applications and a simplified decoder for low power applications. The simplified decoder uses a shift register with XOR gates that processes only polynomial-derived signals, reducing computational complexity while maintaining acceptable accuracy for many scenarios.
Solution Approach 2:
The invention changes the processing parameter from binary quantized signals to multilevel polynomial-derived signals. By processing signals before thresholding and quantization, the simplified decoder extracts useful information from the polynomial components without requiring full Viterbi algorithm complexity, thus reducing power consumption while maintaining decoding performance.
2Use of energy by moving object
If a simplified decoding scheme like systematic codes is used, then power consumption is reduced, but decoding performance and accuracy are limited
Solution Approach 1:
The invention introduces polynomial-derived signals as an intermediary between the received signal and the final decoded output. These polynomial signals serve as a mediator that contains information about the transmitted data in a form that can be processed by simple XOR operations, bridging the gap between simple decoding and accurate decoding.
Solution Approach 2:
Instead of performing the complete Viterbi algorithm, the simplified decoder performs partial action by processing only the polynomial-derived components of the signal. This partial processing is sufficient for many applications and provides a good trade-off between power consumption and decoding accuracy without requiring the full computational effort of systematic codes or Viterbi decoding.
Data Source
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AI summary
A non-systematic convolutional decoder of a convolutionally encoded multi-level (levels >2) data stream comprises a shift register and two or more paths of exclusive-OR (XOR) gates, arranged to reconstruct an original input information stream, each path having a quantiser arranged to quantise the signal to two levels, and a set of XOR gates arranged to match an encoding path in an associated convolutional encoder, and a selector arranged to feed an output from each path to a single input of the shift register. If the paths have differing values at their output, the selector may choose the value from the path based upon a function of the multi-level signals associated with each path, such as the path with the largest absolute signal level. The decoder provides a simple means for decoding signals while allowing the signal to also or instead be decoded using e.g. a Viterbi decoder if higher performance is required.