MIMO Stream Partitioning for Joint Processing Complexity Reduction
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Solution Overview
Problem
Multiple-Input-Multiple-Output (MIMO) communication systems face increased processing complexity due to the need to separate received MIMO streams, with existing methods like Maximum Likelihood Detection imposing significant processing and memory requirements, and there is a need for improved subset selection in joint processing to enhance receiver performance.
Innovation Solution
A method and apparatus for optimizing the partitioning of MIMO streams into subsets for joint processing, where channel estimates are used to calculate selection metrics for candidate partitions, identifying the best partition to process a subset jointly and suppress the remaining streams as interference, thereby improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Maximum Likelihood Detection (MLD) is used for multi-stream processing, then detection accuracy is improved, but processing complexity and memory requirements increase significantly
Solution Approach 1:
The patent segments the MIMO stream processing into multiple stages, where each stage processes a subset of streams using joint processing. This divides the complex MLD problem into smaller, more manageable sub-problems, reducing the computational burden at each stage while maintaining overall detection accuracy through sequential processing of partitioned stream sets.
2Reliability
If joint processing is used for MIMO streams, then receiver performance is improved, but processing complexity increases due to subset partitioning requirements
Solution Approach 1:
The patent employs dynamic subset selection where the composition of streams processed jointly at each stage is adaptively determined based on channel conditions and performance metrics. This dynamic approach allows the system to optimize the partitioning strategy in real-time, improving receiver performance while managing processing complexity through intelligent, condition-based subset formation rather than fixed partitioning.
3Measurement precision
If all MIMO streams are processed jointly, then detection accuracy is maximized, but processing complexity and memory requirements become prohibitive
Solution Approach 1:
The patent segments the complete set of MIMO streams into multiple subsets that are processed jointly in separate stages. This segmentation reduces the number of streams that need to be held in memory simultaneously for joint processing, thereby reducing peak memory requirements while still achieving high detection accuracy through the cumulative effect of processing all streams across multiple stages.
Solution Approach 2:
The patent performs preliminary processing and filtering of MIMO streams before the joint processing stages. By pre-processing the signals to separate and suppress interfering streams in advance, the system reduces the complexity and memory requirements of subsequent joint processing stages, as fewer streams need to be simultaneously analyzed in detail.
Data Source
AI summary
The teachings herein provide a method and apparatus for partitioning sets of MIMO streams, for joint processing. In particular, there is an optimum or otherwise best partitioning of a set of MIMO streams into a first subset to be jointly processed and a second subset to be suppressed as interference with respect to that joint processing. Of course, more than one partitioning may be used, e.g., across different joint processing stages and/or at different times, such that all streams of interest are processed. Correspondingly, the present invention provides a method and apparatus for selecting an optimum or otherwise relative “best” subset of MIMO streams for processing together in a joint process, from among a larger set of MIMO streams. The method and apparatus may, for example, be employed in a multi-stage joint processing receiver, where subset selections are performed on a per-stage basis.