Cascade Pre-encoding and Pre-decoding Apparatus for Signal Processing
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Solution Overview
Problem
Current communication systems face high computation complexity in signal decoding, which hinders real-time transmission and is unsuitable for error-prone channels, while linear detection methods have low correction rates, making them inadequate for reliable data recovery.
Innovation Solution
A pre-encoding and pre-decoding apparatus and method that utilize a cascade structure with multiple encoding and interleaving units at the transmitter and corresponding decoding and de-interleaving units at the receiver, reducing computation complexity and improving error correction rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maximum a posteriori (MAP) estimation or maximum likelihood (ML) estimation is used to obtain the best solution, then the correction rate is improved, but the computation complexity becomes very high (O(2^m))
Solution Approach 1:
The patent divides the signal decoding process into multiple stages by using multiple encoders and decoders in series. Each encoder/decoder pair processes a specific subset of signals, breaking down the complex O(2^m) computation into multiple simpler operations. This segmentation allows the system to achieve high correction rates while reducing the computational burden at each stage.
Solution Approach 2:
The patent applies pre-encoding and pre-decoding operations before the main signal processing. By performing encoding and interleaving in advance at the transmitter, and corresponding decoding and de-interleaving at the receiver, the system prepares the signal in a way that simplifies subsequent processing. This preliminary action reduces the complexity of the main decoding operation while maintaining high correction performance.
2Device complexity
If zero forcing (ZF) equalization technology is used to reduce computation complexity, then the computation complexity is reduced to O(m^3), but the correction rate is not as high as ML method
Solution Approach 1:
The patent combines multiple encoding and decoding operations in series, where each operation contributes to both complexity reduction and correction rate improvement. By merging multiple simple encoding/decoding stages, the system achieves overall complexity reduction comparable to ZF equalization while maintaining higher correction rates through the cumulative effect of multiple processing stages.
Solution Approach 2:
The pre-encoding and pre-decoding operations prepare the signal in advance, reducing the complexity of subsequent processing. This preliminary processing allows the system to achieve low computation complexity similar to ZF equalization while maintaining better correction rates through the preparatory transformations applied to the signal.
3Reliability
If minimum mean square error (MMSE) equalization technology is used, then the correction rate is improved compared to ZF, but the computation complexity remains O(m^3) and correction rate is still not as high as ML method
Solution Approach 1:
The patent segments the signal processing into multiple independent encoder/decoder pairs, where each pair processes a specific subset of signals. This segmentation reduces the overall computation complexity from O(m^3) to a lower complexity level by distributing the processing workload across multiple simpler stages, while maintaining high correction rates through the cumulative effect of all stages.
Solution Approach 2:
By performing pre-encoding and pre-decoding operations in advance, the patent reduces the computational burden of subsequent processing. This preliminary action transforms the signal in a way that simplifies the main processing operation, achieving lower computation complexity while maintaining correction rates comparable to or better than MMSE equalization.
4Reliability
If iterative detection methods (VBLAST, iterative MUD, sphere decoding) are used to enhance correction rate, then the correction rate is improved, but the computation complexity increases significantly
Solution Approach 1:
The patent divides the iterative detection process into multiple discrete encoder/decoder stages, where each stage processes a specific subset of signals independently. This segmentation reduces the overall computation complexity by breaking down the iterative process into simpler, more manageable operations, while maintaining high correction rates through the cumulative effect of all stages.
Solution Approach 2:
The pre-encoding and pre-decoding operations prepare the signal in advance, reducing the computational requirements of subsequent iterative detection. By performing these operations beforehand, the patent simplifies the main processing operation, achieving lower computation complexity while maintaining correction rates comparable to iterative detection methods.
Data Source
AI summary
A pre-encoding apparatus and a pre-decoding apparatus are provided. The pre-encoding apparatus adopts a cascade structure constituted by a plurality of pre-encoding units and a plurality of interleavers for pre-encoding, and the pre-decoding apparatus adopts a cascade structure constituted by a plurality of pre-decoding units and a plurality of de-interleavers for pre-decoding. Therefore, the pre-decoding apparatus is featured with a lower error rate. Also, each of the pre-decoding units can be alternatively composed of a plurality of low dimensional pre-decoders so that a computation complexity of the pre-decoding apparatus can be reduced accordingly.