Joint Decoding Engine for MIMO Signal Processing
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently decoding multiple-input multiple-output (MIMO) signals due to high computational complexity, which can lead to resource consumption and interference cancellation issues.
Innovation Solution
A joint decoding engine is employed in wireless devices to process MIMO signals using max log maximum a posteriori (MLM) processing, whitening, and successive interference cancellation (SIC), which involves determining symbol streams, scaling channel estimates, and performing MLM processing to produce decoded data streams efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional decoding methods are used for MIMO signals, then decoding can be performed, but computational complexity becomes excessively high
Solution Approach 1:
The joint decoding process is segmented into distinct functional modules: a whitening module that transforms the received signal to eliminate noise correlation, a max log MAP processing module that performs approximate maximum a posteriori decoding, and a de-rate matching module that adjusts the decoded stream to the original data rate. This segmentation allows each module to be optimized independently, reducing overall computational complexity while maintaining decoding reliability.
Solution Approach 2:
The patent employs max log MAP processing as an approximation of the optimal but computationally prohibitive exact MAP decoding. This approximate method uses simplified calculations that are computationally cheaper and can be executed efficiently, sacrificing minimal decoding performance for significant reductions in computational burden.
2Productivity
If signal processing techniques are added to improve communication quality, then communication efficiency improves, but device resources are consumed
Solution Approach 1:
The patent transforms the received MIMO signal through parameter changes in the whitening process, where the noise covariance matrix is computed and used to whiten the received signal. This parameter transformation simplifies subsequent decoding operations by eliminating noise correlation, thereby improving communication efficiency without proportionally increasing device resource consumption.
3Ease of operation
If multiple symbol streams are decoded separately, then processing is simpler, but interference cancellation is insufficient
Solution Approach 1:
The patent implements joint decoding that merges the processing of multiple symbol streams into a unified decoding framework. The max log MAP processor simultaneously processes multiple streams while accounting for inter-stream interference, producing de-rate matched streams that have had interference effectively canceled. This merging approach maintains processing tractability while significantly improving interference cancellation compared to separate decoding.
Data Source
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AI summary
A method for using a joint decoding engine in a wireless device is disclosed. A first symbol stream and a second symbol stream in a received multiple input multiple output (MIMO) signal is determined. A scaled channel estimate for a wireless transmission channel and a scaled noise covariance of the MIMO signal are also determined. The scaled channel estimate and the first symbol stream are whitened. Max log maximum a posteriori (MLM) processing is performed on the whitened first symbol stream to produce a first data stream. The first data stream may be de-rate matched and decoded to produce a decoded first data stream.