PMI Determination Using CFR and Beam Angle Codebooks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining a precoding matrix indicator (PMI) in wireless communication systems consume unnecessary computational resources and time by calculating data throughput for all possible PMIs, which is inefficient.
Innovation Solution
A method and apparatus that utilize channel frequency response (CFR) to convert matrices associated with multiple antennas into values representing beam angles, allowing for the determination of an optimal PMI without calculating data throughput for all PMIs, thereby reducing computational resources and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data throughput is calculated for all possible PMIs to determine the optimal PMI, then PMI accuracy is improved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent performs preliminary calculations of channel frequency response (CFR) and beam angle values before PMI determination. By pre-computing these parameters and storing them in codebooks, the system avoids redundant calculations during actual PMI selection, significantly reducing the time required while maintaining accuracy.
Solution Approach 2:
The patent extracts only the essential parameters (CFR and beam angles) needed for PMI determination from the complete channel state information. By focusing on these key parameters and representing them through compact codebooks, the system reduces computational complexity while preserving the most important information for accurate PMI selection.
2Measurement precision
If data throughput is calculated for all possible PMIs to determine the optimal PMI, then PMI accuracy is improved, but computational resources are excessively consumed
Solution Approach 1:
The patent uses compact codebooks that can be quickly generated and discarded for each channel state. Instead of maintaining complex, persistent computational models, the system creates lightweight codebook representations of channel characteristics, enabling fast PMI determination with minimal computational resource consumption.
Solution Approach 2:
The patent transforms channel state information into different parameter representations (CFR and beam angles) that are more suitable for efficient PMI determination. By changing the parameter domain and using codebook-based quantization, the system reduces the computational burden while maintaining measurement accuracy.
3Use of energy by moving object
If phase differences alone are used to determine PMI, then computational resources are reduced, but PMI accuracy decreases
Solution Approach 1:
The patent merges multiple channel characteristics (amplitude and phase information from CFR, spatial distribution from beam angles) into a unified codebook representation. By combining these different aspects of channel state into a single comprehensive structure, the system achieves accurate PMI determination with reduced computational complexity compared to analyzing all parameters separately.
Solution Approach 2:
The patent introduces beam angles as an additional dimension for representing channel state, transforming the problem from phase-only analysis to a multi-dimensional representation involving CFR and spatial beam characteristics. This dimensional expansion enables more accurate PMI determination while maintaining computational efficiency through codebook-based processing.
Data Source
AI summary
A method performed by a receiving end in a wireless communication system is provided. The method includes receiving one or more reference signals (RSs) from a transmitting end, identifying, based on channel frequency response (CFR) identified based on the received one or more RSs, one or more matrixes associated with a plurality of antennas, which are included in the receiving end or the transmitting end and are for transmitting downlink data, converting the identified one or more matrixes into values associated with angles of beams formed by the plurality of antennas, determining, based on the values associated with the angles of the beams, one or more precoding matrix indicators (PMIs), which can be applied to the plurality of antennas, and identifying a first PMI corresponding to a maximum downlink data throughput from among the one or more PMIs.