Scalable MIMO Detector Using Segmented Core and Residual Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing MIMO detection methods, such as Maximum-Likelihood (ML) and Vertical Bell Laboratories Layered Space Time (VBLAST), face high computational complexity, especially in large systems, which limits their practicality and performance.
Innovation Solution
The proposed scalable MIMO detector divides the large MIMO system into smaller sub-modules with 2×2 dimensions, using simplified ML detection for the core part and VBLAST for the residual part, significantly reducing computational complexity while maintaining performance by avoiding matrix inversion and iteratively using basic modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ML detection is used to achieve high performance, then detection accuracy is improved, but computational complexity increases rapidly with antenna number and constellation size
Solution Approach 1:
The patent segments the large MIMO detection problem into multiple smaller sub-problems by dividing the channel matrix into multiple blocks. Each block corresponds to a subset of transmit antennas and is detected separately using simplified ML detection. This segmentation reduces the exponential complexity growth while maintaining detection accuracy through block-by-block processing.
2Device complexity
If VBLAST detection is used to reduce complexity, then computational complexity is reduced, but detection performance deteriorates compared to ML detection
Solution Approach 1:
The patent applies local quality by using simplified ML detection with reduced constellation sizes for each block rather than a uniform approach. Each block's detection complexity is tailored to its specific dimensions and channel characteristics, achieving better performance-complexity tradeoff compared to conventional VBLAST while avoiding the prohibitive complexity of full ML detection.
3Productivity
If large MIMO system dimensions are used to increase channel capacity, then data rate is improved, but detection complexity increases making it impractical
Solution Approach 1:
The patent enables large MIMO systems to be practically implemented by segmenting the detection process into multiple manageable blocks. This allows the system to scale to large dimensions (e.g., 16x16 or larger) while keeping per-block complexity manageable, thus maintaining practicality while achieving high channel capacity through spatial multiplexing.
Solution Approach 2:
The patent transforms the detection problem by introducing a block dimension, converting the original two-dimensional detection problem (antennas x constellations) into a multi-stage process with an additional block dimension. This dimensional transformation allows systematic reduction of complexity while preserving the ability to handle large MIMO systems.
Data Source
AI summary
A scalable Multiple-Input Multiple-Output (MIMO) detector, comprises an ordering block, a group interference suppression block, a core detector and a residual detector. The ordering block determines an order of the columns of a channel matrix including received streams based on the power thereof. The group interference suppression block coupled to the ordering block groups received streams into a core part and a residual part, the core part including a first received stream and a second received stream corresponding to the first two columns of the channel matrix in the order, the first received stream and the second received stream forming a received signal vector, and the residual part including the rest of the received streams. The core detector detects the core part based on a 2×2 Simplified Maximum Likelihood (SML) detection. The residual detector detects the residual part by Vertical Bell Laboratories Layered Space Time (VBLAST) detection.