Wideband MIMO Beamforming with Low-Complexity Channel Decomposition
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately estimating wideband sounding reference signals (SRS) due to limited power at terminal devices, especially at cell edges, leading to poor performance in beamforming, particularly in massive MIMO systems.
Innovation Solution
A low-complexity wideband beamforming algorithm (LCWBB) is employed, involving eigenvalue decomposition (EVD) of an approximate effective channel matrix to derive eigenvectors, which are used for beamforming, reducing computational complexity and improving beamforming accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MIMO beamforming is used with limited terminal power, then spectrum efficiency is maintained, but beamforming accuracy deteriorates especially at cell edges
Solution Approach 1:
The patent segments the wideband SRS channel estimation into multiple narrowband component estimations across different frequency subbands. By dividing the wideband channel into narrower frequency segments and estimating each separately, the system achieves more accurate channel state information without requiring increased terminal power, thus resolving the contradiction between beamforming accuracy and power consumption.
2Productivity
If massive MIMO with tens or hundreds of antennas is deployed, then spectrum efficiency is improved through spatial degrees of freedom, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the massive MIMO channel matrix into multiple smaller subband matrices for independent processing. This reduces the computational complexity of eigenvalue decomposition and singular value decomposition operations by working with smaller matrix dimensions in each frequency subband, while still utilizing the full spatial degrees of freedom of the massive antenna array to maintain high spectrum efficiency.
Solution Approach 2:
The patent implements partial action by performing beamforming optimization on a subset of frequency subbands rather than processing the entire wideband spectrum simultaneously. This selective processing approach reduces computational burden while maintaining adequate beamforming performance by focusing computational resources on the most critical frequency regions.
3Measurement precision
If wideband SRS channel estimation is performed with low power, then power consumption is reduced, but estimation accuracy deteriorates
Solution Approach 1:
The patent segments the wideband channel estimation task into multiple narrowband estimations across frequency subbands. This segmentation allows the system to achieve accurate channel state information by accumulating measurements across multiple subbands, compensating for the low power of individual SRS transmissions without requiring high instantaneous power that would increase energy consumption.
Solution Approach 2:
The patent implements continuous useful action by performing channel estimation across multiple continuous frequency subbands and time instances. By continuously accumulating and processing channel measurements across the wideband spectrum in a segmented manner, the system maintains accurate channel estimation despite low individual measurement power, as the useful information accumulates across the continuous frequency and time dimensions.
Data Source
AI summary
According to an aspect, there is provided an apparatus configured to perform the following. The apparatus obtains an approximate effective channel matrix for a radio channel between the apparatus, acting as a transmitter, and a receiver. The apparatus calculates an eigenvalue decomposition, EVD, of a matrix product of the approximate effective channel matrix and a conjugate transpose of the approximate effective channel matrix and determines, based on the EVD, a left singular matrix of a singular value decomposition, SVD, of the approximate effective channel matrix and a diagonal matrix of singular values of the SVD of the approximate effective channel matrix. The apparatus calculates eigenvectors of the approximate effective channel matrix based on the approximate effective channel matrix and the left singular matrix of the SVD.