MIMO Receiver Interference Cancellation via Frame Segmentation
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Solution Overview
Problem
MIMO transmission systems face challenges in efficiently receiving data due to interference and noise, requiring complex and computationally intensive space-time equalizer updates to maintain signal quality.
Innovation Solution
The implementation of successive interference cancellation (SIC) techniques at the receiver, using a front-end filter and combiner matrices to distinguish and cancel interference from on-time and non-on-time signal components, allowing for efficient data recovery and reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If space-time equalizer coefficients are updated dynamically to match fastest changes in signal properties, then signal quality is maintained, but receiver complexity becomes very high
Solution Approach 1:
The patent segments the received signal into multiple frames and processes them sequentially through interference cancellation stages. Instead of treating all signals uniformly with complex dynamic equalization, the receiver divides the problem into manageable frame segments, reducing the computational burden on the equalizer while maintaining signal quality through staged processing.
Solution Approach 2:
The patent applies preliminary interference cancellation before the main equalization process. By estimating and removing interference from previous frames in advance, the receiver reduces the complexity of the subsequent space-time equalizer operations, as the equalizer no longer needs to handle the full interference burden dynamically.
2Device complexity
If space-time equalizer coefficients are updated at a slower rate, then receiver complexity is reduced, but signal quality deteriorates
Solution Approach 1:
By segmenting the signal processing into frame-based stages with interference cancellation, the patent enables slower equalizer updates while maintaining quality. Each frame is processed with current interference estimates, effectively refreshing the signal quality without requiring continuous fast equalizer reconfiguration.
Solution Approach 2:
The patent implements feedback through iterative interference cancellation, where decoded data from previous frames is used to estimate and cancel interference in subsequent frames. This feedback mechanism maintains signal quality by continuously adapting to changing conditions without requiring fast equalizer coefficient updates.
3Reliability
If interference cancellation is applied to all signal components (full SIC), then signal quality is maximized, but computational complexity increases significantly
Solution Approach 1:
The patent extracts and cancels only the dominant interference components from previous frames that most significantly affect current frame detection. Rather than applying full SIC to all signal components, the receiver selectively removes the most harmful interference, achieving good signal quality with reduced computational effort.
Solution Approach 2:
The patent applies partial interference cancellation focused on the most critical interference sources rather than exhaustive cancellation of all components. This partial action approach achieves sufficient signal quality improvement without the prohibitive computational cost of complete SIC across all signal components.
Data Source
AI summary
Techniques for receiving a MIMO transmission are described. A receiver processes received data for the MIMO transmission based on a front-end filter to obtain filtered data. The receiver further processes the filtered data based on at least one first combiner matrix to obtain detected data for a first frame. The receiver demodulates and decodes this detected data to obtain decoded data for the first frame. The receiver then processes the filtered data based on at least one second combiner matrix and the decoded data for the first frame to cancel interference due to the first frame and obtain detected data for a second frame. The receiver processes this detected data to obtain decoded data for the second frame. The front-end filter processes non on-time signal components in the received data. Each combiner matrix combines on-time signal components in the filtered data to obtain detected data for a channelization code.