Beam Training Channel Estimation With Compressive Sensing
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Solution Overview
Problem
The existing beam training procedures in NR systems suffer from high overheads due to the need for multiple signal transmissions and measurements, with wide beams often not including optimal narrow beams, leading to inefficiencies and suboptimal performance.
Innovation Solution
A communication method that involves sending configuration information including reference signals, spatial filtering parameters, and a channel sparse basis matrix, allowing for channel estimation and beam training with reduced reference signal overheads using compressive sensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If wide beam sweeping is used in the first stage, then coverage is improved, but optimal narrow beam may not be included and overheads increase
Solution Approach 1:
The patent applies preliminary action by performing channel estimation using compressive sensing before beam training. The receiving device estimates the channel based on received reference signals and spatial filtering parameters, obtaining preliminary channel state information that guides subsequent beam training. This preliminary estimation reduces the search space and enables faster convergence to optimal beams without exhaustive wide beam sweeping.
Solution Approach 2:
The patent changes parameters by introducing compressive sensing methodology to estimate channel state information from limited reference signal measurements. Instead of traditional methods requiring extensive beam sweeping, the system uses compressive sensing algorithms to reconstruct channel information from undersampled data, changing the fundamental approach from spatial domain sweeping to compressed domain estimation.
2Measurement precision
If multiple reference signals are transmitted for beam training, then beam accuracy is improved, but reference signal overheads increase
Solution Approach 1:
The patent applies asymmetry by treating transmit and receive operations differently. The transmitting device sends reference signals with specific spatial filtering parameters, while the receiving device performs compressive sensing estimation to derive channel information. This asymmetric approach allows the receiver to obtain accurate channel state information without requiring the transmitter to send an equal number of reference signals for each possible beam direction.
Solution Approach 2:
The patent uses copying by creating virtual channel observations through compressive sensing reconstruction. Instead of physically transmitting reference signals for all possible beam directions, the system reconstructs channel information from a subset of measurements, creating a compressed representation that captures essential channel characteristics without requiring complete spatial sampling.
3Adaptability or versatility
If traditional beam training procedures are used, then compatibility is maintained, but time overheads are large
Solution Approach 1:
The patent applies universality by designing a beam training method that can work with existing reference signal structures and spatial filtering parameter configurations. The compressive sensing-based channel estimation can be applied to various reference signal types (SSB, CSI-RS) and spatial filtering schemes, making the approach universally applicable across different deployment scenarios while maintaining compatibility with existing systems.
Data Source
AI summary
A communication method and apparatus are provided. The method includes: receiving configuration information from a first device, wherein the configuration information includes first information, second information, and third information, the first information is indication information of K reference signals, the second information is indication information of K first spatial filtering parameters corresponding to the K reference signals, and the third information is indication information of a channel sparse basis matrix of channels between the first device and a second device; receiving the K reference signals from the first device; and sending channel state indication information to the first device, where the channel state indication information is determined based on K received measurement values of the K reference signals, the K first spatial filtering parameters, and the channel sparse basis matrix.