High Rank MIMO Symbol Detection via Channel Decomposition
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Solution Overview
Problem
High rank multiple-input multiple-output (MIMO) symbol detectors face increased complexity and performance degradation due to the complexity of existing detectors, making them impractical for high rank scenarios like rank-8 MIMO transmission in LTE and 5G systems.
Innovation Solution
The method involves decomposing a high rank channel into multiple virtual channels using preprocessors, performing symbol detection on each virtual channel with lower rank detectors, and combining the results to obtain decoded symbol values, allowing for low complexity and high flexibility in high rank MIMO receivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing non-linear symbol detectors (ML, ZF/MMSE-SIC, SD) are used for high rank MIMO, then detection performance is improved, but device complexity becomes too high for practical implementation
Solution Approach 1:
The patent divides the high rank MIMO detection problem into multiple lower rank sub-problems by segmenting the channel matrix into smaller blocks. Each block is processed by a separate lower complexity detector, and the results are combined to achieve the final detection. This segmentation allows the system to attain high detection performance equivalent to full-rank detectors while maintaining practical implementation complexity.
2Device complexity
If existing linear symbol detectors (ZF, MMSE) are used for high rank MIMO, then device complexity is reduced, but detection performance degrades significantly
Solution Approach 1:
The patent merges multiple lower rank detection results through a combining mechanism to achieve high rank detection performance. By processing the channel matrix in segmented blocks and combining the detection outputs, the system achieves performance comparable to complex non-linear detectors while maintaining the lower complexity advantage of linear detectors.
3Reliability
If ML detection is used for high rank MIMO, then detection performance is maximized, but computational complexity increases prohibitively as all symbols in constellation must be considered
Solution Approach 1:
The patent segments the high rank channel matrix into multiple lower rank sub-channels, allowing ML detection to be performed on smaller subsets of symbols rather than the entire constellation. This segmentation dramatically reduces the computational complexity from exponential growth with rank to a manageable level, while the combination of sub-channel results maintains high detection performance.
Data Source
AI summary
A method and an apparatus in a multiple-input multiple-output (MIMO) wireless communication system are provided. A signal is received over a channel. The channel is decomposed into a plurality of virtual channels. Symbol detection is performed on each of the plurality of virtual channels. Values are obtained for the channel. Decoding is performed using the values to output a decoded symbol value of the received signal.