MIMO Receiver SPRI Detector for Interference Suppression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional MIMO receivers face significant challenges in suppressing interference in multipath channels, particularly inter-layer and multiple access interference, which limits their applicability in practical communication environments.
Innovation Solution
The improved MIMO receiver combines classical estimation techniques with Bayesian estimation, incorporating an N-antenna array, an M-output space-time equalizer based on the linear minimum mean-square error criterion, and a Signal-Plus-Residual-Interference (SPRI) detector, which generates signal models for desired and interfering signals, allowing for effective suppression of inter-layer interference and enabling detection of multiple data streams with fewer receive antennas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional space-time equalization is used to suppress inter-layer interference, then some interference suppression is achieved, but multiple access interference and inter-symbol interference due to multipath cannot be suppressed
Solution Approach 1:
The received signal is segmented into multiple time instances (F1 and F2 parameters define the length of the signal vector), allowing the receiver to process a block of signal samples rather than individual samples. This segmentation enables the space-time equalizer to suppress multiple access interference and inter-symbol interference by utilizing temporal correlations across multiple time instances, thereby improving reliability in multipath channels while still suppressing inter-layer interference.
2Object-affected harmful factors
If ordered successive interference cancellation is used to detect data streams, then interference cancellation is achieved, but error propagation occurs and implementation complexity increases significantly
Solution Approach 1:
Instead of completely canceling interference through successive interference cancellation, the patent applies space-time equalization that partially suppresses multiple access interference and inter-symbol interference in a single processing stage. This partial action approach avoids the need for multiple successive cancellation stages, thereby reducing implementation complexity while still achieving effective interference suppression and eliminating error propagation issues.
3Measurement precision
If Maximum likelihood detector is used to search for best symbol combination, then detection accuracy is improved, but multiple access interference and inter-symbol interference due to multipath are not suppressed
Solution Approach 1:
The patent merges the functions of interference suppression and accurate detection into a unified space-time equalizer. The equalizer combines spatial filtering across multiple antennas with temporal filtering across multiple time instances, simultaneously suppressing multiple access interference, inter-symbol interference, and inter-layer interference. This merged approach achieves both interference suppression and detection accuracy without requiring separate processing stages.
4Object-affected harmful factors
If more receive antennas are used to cancel interfering signals, then interference suppression capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent adds the time dimension to the spatial processing by utilizing signal samples from multiple time instances (F1 and F2 parameters). This space-time processing approach effectively increases the degrees of freedom available for interference suppression without requiring additional receive antennas. By exploiting temporal correlations in multipath channels, the system achieves enhanced interference suppression capability while maintaining a fixed number of antennas, thereby reducing device complexity and cost.
Data Source
AI summary
A method is disclosed to obtain M final symbol decisions for signals received through N receive antennas that were transmitted in M parallel data layers, using a same spreading code from M transmit antennas. The method includes space-time equalizing the N received signals to generate M output signals from which at least inter-symbol interference is substantially removed and inter-layer interference is suppressed; despreading each of the M output signals for generating M soft symbol estimates; and processing the M soft symbol estimates to derive M final symbol decisions that are made in consideration of modeled residual inter-layer interference present in the space-time equalized M output signals. Processing includes operating a signal-plus-residual-interference (SPRI) detector that operates in accordance with a maximum likelihood (ML) technique, while space-time equalizing employs a linear minimum mean-square error (LMMSE) criterion. Transmitting may occur at a base station having the M transmit antennas, and receiving may occur at a mobile station having the N receive antennas.