MIMO Receiver Decoding Complexity Reduction via MMSE Subset Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional signal decoding techniques in MIMO communication systems are computation-intensive, leading to high power consumption and resource utilization, especially when dealing with interference from multiple data streams.
Innovation Solution
A method that selects a subset of decoding constellation points based on minimum mean square error (MMSE) detection and squared Euclidean distance, reducing the number of calculations required for accurate decoding by using a reduced set of constellation points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signal decoding techniques are used to decode MIMO signals, then accurate data decoding is achieved, but computational load and power consumption increase significantly
Solution Approach 1:
The patent segments the complete set of constellation points into multiple subsets based on MMSE detection results. Instead of evaluating all constellation points, the decoder divides them into groups and selectively evaluates only relevant subsets, thereby reducing computational load and power consumption while maintaining decoding accuracy.
Solution Approach 2:
The patent applies partial action by evaluating only a subset of constellation points rather than the complete set. The MMSE detection results are used to identify and evaluate only the most relevant constellation point subsets, avoiding unnecessary computations and reducing power consumption while achieving sufficient decoding accuracy.
2Measurement precision
If conventional signal decoding techniques are used to decode MIMO signals, then accurate data decoding is achieved, but device complexity and resource utilization increase
Solution Approach 1:
The decoder complexity is reduced by segmenting the constellation points into multiple subsets based on MMSE detection. This segmentation allows the decoder to focus computational resources on evaluating only the relevant subsets rather than processing the entire constellation set, thereby simplifying the decoding process and reducing device complexity.
Solution Approach 2:
The patent performs preliminary MMSE detection to identify and prioritize relevant constellation point subsets before the main decoding evaluation. This preliminary action filters out less relevant points, reducing the complexity of the subsequent decoding process while maintaining accuracy.
3Productivity
If a reduced set of constellation points is used for decoding, then computational load is reduced, but decoding accuracy may be compromised
Solution Approach 1:
The patent changes the parameter selection criterion from evaluating all constellation points uniformly to selectively evaluating subsets based on MMSE detection results. This parameter change in the selection process ensures that the most relevant constellation points are prioritized, maintaining decoding accuracy while improving efficiency.
Solution Approach 2:
The MMSE detection results serve as feedback to guide the selection of constellation point subsets for evaluation. This feedback mechanism ensures that the decoding process focuses on the most relevant points, maintaining accuracy while improving efficiency by avoiding evaluation of less relevant points.
Data Source
AI summary
A receiver decodes received data streams based on a subset of candidate decoding constellation points. A first stage of a decoder of the receiver selects a subset of candidate decoding constellation points by identifying a decoded value for an initial data stream of the set of data streams. A second stage then applies MMSE error detection to each of the constellation points in the selected subset, and calculates an error metric based on the MMSE error detection results. The decoder selects the constellation points having the lowest error metrics, and uses the selected constellation points as an initial set of points for decoding the next data stream to be decoded.