Lattice Reduction-Aided Perturbed Additive Demapper for MIMO Signal Detection
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Solution Overview
Problem
Current MIMO signal demapping techniques are computationally complex, power-intensive, and require significant hardware resources, especially when dealing with high-data-rate transmissions and large signal constellations, due to their inability to effectively account for inter-stream interference and channel variations.
Innovation Solution
A processor-implemented method using machine learning techniques to demap signals by generating a candidate set of codes around a seed point in the signal constellation, reducing computational complexity and improving accuracy through lattice reduction and perturbed additive demapping, allowing for flexible implementation across varying channel conditions and constellation sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probabilistic estimate methods are used to demap MIMO signals, then demapping accuracy is improved, but computational complexity and power consumption increase significantly
Solution Approach 1:
The patent segments the signal constellation into multiple clusters and processes each cluster separately using parallel processing paths. This divides the complex probabilistic estimation problem into smaller, more manageable sub-problems that can be solved with reduced computational resources while maintaining overall demapping accuracy.
Solution Approach 2:
The patent applies partial action by using simplified detection methods for certain signal components while reserving full probabilistic processing for critical components. This selective approach reduces overall computational complexity while maintaining sufficient accuracy for the most important signal parameters.
2Measurement precision
If full probabilistic processing is applied to all signal components, then demapping accuracy is improved, but power consumption increases
Solution Approach 1:
The patent divides the processing into multiple stages with different power consumption levels. Critical signal components undergo full probabilistic processing, while less critical components use simplified detection methods, thereby reducing overall power consumption while maintaining necessary accuracy.
Solution Approach 2:
The patent implements dynamic power management by adjusting the level of processing applied to different signal components based on their importance and current channel conditions. This allows the system to optimize power consumption in real-time while maintaining demapping accuracy when needed.
3Measurement precision
If complex detectors are used to account for inter-stream interference, then signal detection accuracy is improved, but hardware area and implementation complexity increase
Solution Approach 1:
The patent segments the MIMO signal processing into multiple parallel detection paths, each handling specific spatial streams or signal components. This segmentation allows the use of simpler detectors in each path while collectively achieving accurate interference cancellation through the combined parallel processing architecture.
Solution Approach 2:
The patent transforms the complex multi-dimensional MIMO detection problem into multiple lower-dimensional sub-problems that can be solved using simpler hardware. By processing signals in transformed domains or using dimensional decomposition, the patent reduces hardware area requirements while maintaining detection accuracy.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for demapping a signal to a point in a signal constellation. An example method generally includes identifying a seed point in a signal constellation from a received signal. A candidate set of codes for the signal is generated based on a seed point and an additive perturbation applied to the seed point. A point in the signal constellation corresponding to the value of the received signal is identified based on a probability distribution generated over the candidate set of codes. Generally, the identified point corresponds to a code in the candidate set of codes having a highest probability in the probability distribution. The point in the signal constellation is output as the value of the received signal.