K-Best MIMO Detection with Merge Sorting for High Throughput
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
MIMO systems face complexity and low throughput in signal detection due to the need to process multiple signals from multiple transmitter outputs, making it challenging to maintain high reliability and efficiency in digital communications and storage systems.
Innovation Solution
A scalable K-Best MIMO detector is implemented, which computes metrics for possible symbols, sorts them using a merge sort algorithm, and selects a predetermined number of likely combinations to estimate transmitted digital information, reducing complexity and increasing throughput by processing signals in stages with an upper triangular channel matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a MIMO detector processes all possible symbol combinations to maintain high reliability, then detection accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the detection process into multiple stages, where each stage processes a subset of metrics rather than all possible symbol combinations at once. The detector divides the search space by processing metrics in stages, selecting K best metrics at each stage, which reduces the computational complexity from exponential to manageable levels while maintaining detection accuracy through systematic exploration of the most likely symbol combinations.
2Reliability
If a MIMO detector processes all possible symbol combinations to ensure high reliability, then detection accuracy is improved, but throughput decreases due to extensive computation
Solution Approach 1:
The patent applies preliminary action by pre-computing and sorting metrics before full detection is required. The system calculates metrics for all possible symbol combinations in advance, sorts them by magnitude, and selects the K best metrics. This preliminary sorting and selection enables the detector to work with a reduced set of high-probability candidates during actual detection, significantly improving throughput while maintaining the reliability needed for accurate detection.
3Measurement precision
If the detector considers all possible symbol combinations from multiple transmit antennas, then information recovery accuracy is improved, but the processing time increases
Solution Approach 1:
The patent changes the parameter of metric selection by using magnitude-based sorting to identify and prioritize the K best metrics from the complete set of possible symbol combinations. By transforming the parameter selection criterion to focus on metric magnitude rather than exhaustive evaluation, the system achieves accurate information recovery by concentrating computational resources on the most promising candidates, thereby reducing processing time while maintaining precision.
Data Source
AI summary
A high throughput and scalable MIMO detector can use a K-Best detection algorithm to find K combinations of transmit symbols that are likely to be the symbols that were actually transmitted. The K-best MIMO detector can include a plurality of stages, where each stage may correspond to a transmit antenna, and each stage can find K best symbol combinations based on information from a previous stage. To find the new K best symbol combinations, at each stage, a plurality of metrics for potential combinations are computed and sorted by magnitude. The MIMO detector preferably uses a high throughput, merge sorting algorithm to sort the metrics.