MIMO Receiver Codebook Vector Selection for SINR Optimization
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Solution Overview
Problem
In Multiple-Input Multiple-Output (MIMO) communication systems, user terminals face challenges in accurately feeding back channel direction information (CDI) and channel quality information (CQI due to limited capacity and hardware complexity, leading to quantization errors and suboptimal data transmission rates.
Innovation Solution
A receiver is designed with a channel vector measuring unit, candidate codebook vector selecting unit, and selection codebook vector determining unit to measure and select codebook vectors based on signal-to-interference and noise ratio (SINR), reducing quantization errors and optimizing data transmission rates by determining selection codebook vectors for each data stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of receiving antennas is increased to improve theoretical capacity, then system capacity increases, but device complexity and cost increase
Solution Approach 1:
The patent segments the channel information feedback into two components: channel direction information (CDI) and channel quality information (CQI). By separating these functions, the system can achieve accurate feedback without requiring multiple receiving antennas, thus resolving the contradiction between capacity and device complexity.
Solution Approach 2:
The patent introduces codebook vectors as intermediaries to represent channel direction information. Instead of directly measuring and feedbacking the complete channel state from multiple antennas, the system uses codebook vectors as a simplified intermediary representation that captures the essential directional information, reducing the required hardware complexity.
2Device complexity
If codebook vectors are selected based only on quantization error to reduce feedback complexity, then feedback capacity is reduced, but accuracy of channel direction information deteriorates
Solution Approach 1:
The patent dynamically adjusts the selection criteria for codebook vectors based on the specific channel conditions and requirements. Instead of using a fixed quantization error threshold, the system adapts the selection process to balance between feedback complexity and accuracy, allowing the system to optimize the trade-off in real-time.
Solution Approach 2:
The patent changes the parameter used for codebook vector selection from solely quantization error to a combination of quantization error and channel quality information. This parameter change allows the system to maintain higher accuracy in channel direction information feedback while controlling feedback capacity through the CQI component.
3Device complexity
If base station uses zero-forcing beamforming to achieve high performance with less hardware complexity, then hardware complexity is reduced, but accuracy of channel state detection requires complex hardware
Solution Approach 1:
The patent implements a feedback mechanism where the user terminal feeds back both channel direction information (CDI) and channel quality information (CQI) to the base station. This feedback loop allows the base station to adjust its beamforming weights to compensate for any inaccuracies in channel state detection, maintaining high performance without requiring complex hardware for precise initial detection.
Data Source
AI summary
Provided is a receiver for feeding back channel information, which includes a channel vector measuring unit to measure channel vectors corresponding to a plurality of receiving antennas, respectively, that receive a plurality of data streams, a candidate codebook vector selecting unit to select at least two candidate codebook vectors from codebook vectors included in a codebook, by considering a quantization error based on the channel vectors with respect to each of the data streams, and a selection codebook vector determining unit to determine selection codebook vectors corresponding to the plurality of data streams respectively from the at least two candidate codebook vectors, based on a signal-to-interference and noise ratio (SINR) of each of the data streams that is calculated according to each of the candidate codebook vectors.