Matched Filter Bank Decoding Using Prior Symbols for Higher Sensitivity
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Solution Overview
Problem
Digital radio receivers face challenges in increasing sensitivity without exponentially increasing complexity and power consumption due to the need for longer filter lengths, which is undesirable for power-constrained devices.
Innovation Solution
The method involves using the value of previously decoded symbols to influence the decoding of current symbols, effectively increasing the demodulation length without adding more filters, by feeding back earlier decoded symbols to the matched filter bank or decision unit, allowing earlier samples to impact later symbol decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the bit-length K of each filter is increased to improve sensitivity, then the sensitivity of the receiver is improved, but the number of filters and computational complexity increase exponentially
Solution Approach 1:
The patent applies preliminary action by using previously decoded symbols to inform current symbol decoding. The decoded symbols from earlier time positions are stored and fed back to influence the decoding of current symbols, allowing the system to effectively utilize historical information without requiring exponentially more filters. This resolves the contradiction by achieving enhanced sensitivity through temporal reuse of decoded information rather than increasing filter bank size.
Solution Approach 2:
The patent implements feedback by taking decoded symbols from previous time positions and feeding them back into the decoding process for current symbols. The decision unit outputs decoded symbols that are then used as inputs for subsequent decoding operations, creating a feedback loop that accumulates information over time. This feedback mechanism allows the system to improve sensitivity by incorporating historical decoding results without exponentially increasing the number of filters, thus resolving the technical contradiction.
2Measurement precision
If the bit-length K of each filter is increased to improve sensitivity, then the sensitivity of the receiver is improved, but the power consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-decoding symbols at earlier time positions and storing these decoded values for later use. Instead of increasing filter length K which would require more computational resources and power, the system performs decoding operations in stages, using previously completed decoding work to assist current decoding. This temporal reuse of computational results improves sensitivity without proportionally increasing power consumption.
Solution Approach 2:
The patent implements feedback by routing decoded symbols from previous time positions back to the decoding input for current symbols. This feedback loop allows the system to improve sensitivity by accumulating decoded information over time rather than requiring all information to be processed simultaneously through longer filters. The incremental nature of this feedback-based approach reduces power consumption compared to increasing filter length, as each decoding operation builds on previous work rather than requiring complete re-processing.
3Measurement precision
If the bit-length K of each filter is increased to improve sensitivity, then the sensitivity of the receiver is improved, but the overall system complexity increases
Solution Approach 1:
The patent applies preliminary action by performing decoding of earlier symbols before current symbols and storing these results. This staged approach allows current symbol decoding to leverage previously completed decoding work, effectively increasing the information available for sensitivity improvement without requiring the computational resources that would be needed if all symbols were decoded simultaneously through longer filters. The computational complexity is distributed over time rather than concentrated in a single operation.
Solution Approach 2:
The patent implements feedback by using decoded symbols from previous time positions as inputs for current symbol decoding. This creates a recursive decoding process where each decoding operation benefits from previously accumulated decoded information. The feedback mechanism reduces computational complexity compared to increasing filter length because it reuses previously computed results rather than requiring new computational resources. The system achieves improved sensitivity through iterative refinement rather than brute-force increase in filter bank size.
Data Source
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AI summary
A radio receiver (1) comprises a matched filter bank (5) and a decision unit (6). The matched filter bank (5) has a plurality of filter modules for generating correlation- strength data from a sampled radio signal, each filter module being configured to cross-correlate the sampled signal with data representing a respective filter sequence. The decision unit (6) is configured to use the correlation-strength data to generate a sequence of decoded symbols from the sampled signal. The matched filter bank (5) and/or decision unit (6) are configured to determine the value of each symbol in the sequence in part based on the value of a respective earlier decoded symbol from the sequence of decoded symbols.