Antenna-Domain Port Reduction for Low-Complexity Channel Estimation
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently reducing the dimensionality of antenna ports for channel estimation, leading to high computational complexity and resource constraints, particularly in 5G networks with large numbers of antenna elements.
Innovation Solution
Implementing port reduction techniques using maximum ratio combining and canonical models for demodulation reference signals (DMRS) to reduce the antenna domain dimension, followed by frequency and time domain interpolation for channel estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If port reduction is performed to reduce antenna domain dimension, then computational complexity is reduced, but channel estimation accuracy may deteriorate
Solution Approach 1:
The patent segments the channel estimation process into two distinct stages: first performing port reduction on the channel frequency response information to reduce dimensionality, then performing channel estimation on the reduced-dimension data. This segmentation allows the system to manage computational complexity while preserving estimation accuracy through the structured two-step approach.
Solution Approach 2:
The patent applies preliminary port reduction to the channel frequency response information before performing channel estimation. By pre-processing the data to reduce the antenna domain dimension before the actual estimation, the system prepares optimized input data that reduces subsequent computational complexity while maintaining the essential channel characteristics needed for accurate estimation.
2Quantity of substance
If the total number of antenna ports is reduced to a target number, then resource requirements are reduced, but information loss may occur
Solution Approach 1:
The patent changes the parameter of antenna port dimension from the total number to a target number through port reduction. This parameter transformation is applied specifically to the antenna domain dimension of the channel frequency response information, allowing the system to reduce the number of ports while using mathematical transformations to preserve the essential channel information needed for accurate estimation.
3Productivity
If port reduction is applied to DMRS symbols, then processing efficiency is improved, but signal processing accuracy may deteriorate
Solution Approach 1:
The patent segments the signal processing into distinct phases: first applying port reduction to the DMRS symbols and channel frequency response information, then performing channel estimation and interpolation on the reduced data. This segmentation allows efficient processing through dimensionality reduction while maintaining accuracy in the subsequent estimation and interpolation stages.
Solution Approach 2:
The patent uses an intermediary approach by first reducing ports on the DMRS symbols to create compressed channel frequency response information, which then serves as input for the channel estimation process. This intermediary reduced-dimension representation maintains the essential signal characteristics while enabling more efficient processing in subsequent steps.
Data Source
AI summary
An embodiment includes applying port reduction to two CFR inputs and a channel estimate is applied to the received CFR with the reduced dimension in the antenna domain, in particular, the channel estimator uses the outputs from a port reduction block whose antenna dimension is reduced by the port reduction algorithm, such as canonical model (CM)-based port reduction or a maximum-ratio combining (MRC)-based port reduction, and the channel estimator may use least squares estimators to apply the channel estimation in the reduced dimension in the antenna domain.