Wideband MIMO Beamforming with Low-Complexity Eigenvector Extraction
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Solution Overview
Problem
In massive MIMO systems, obtaining accurate wideband SRS channels is challenging due to limited power at terminal devices, leading to poor performance of narrowband beamforming, and frequency hopping extends the period needed for channel estimation, aggravating frequency selective fading and channel aging.
Innovation Solution
A low-complexity wideband beamforming algorithm (LCWBB) is employed, involving calculating an approximate effective channel matrix and using eigenvalue decomposition (EVD) to determine eigenvectors for beamforming, reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency hopping is used for channel estimation, then channel estimation coverage is improved, but the period needed for channel estimation is extended, aggravating frequency selective fading and channel aging
Solution Approach 1:
The patent performs preliminary channel estimation at multiple frequency points before final beamforming decision-making. By pre-obtaining channel state information across the bandwidth and storing it, the system avoids the need for time-consuming channel estimation during the actual beamforming process, thus resolving the contradiction between comprehensive channel estimation and time delay.
Solution Approach 2:
The patent divides the wideband channel estimation process into multiple narrowband segments at different frequency points. Each segment is estimated independently and then aggregated to form the complete wideband channel state information. This segmentation allows parallel processing and reduces the overall estimation time while maintaining accuracy.
2Reliability
If traditional SVD-based beamforming is used, then beamforming performance is improved, but computational complexity increases significantly
Solution Approach 1:
The patent extracts only the essential eigenvectors from the channel matrix that are most critical for beamforming performance, rather than performing complete singular value decomposition. By identifying and using only the dominant eigenvectors that contribute significantly to the signal, the system achieves good beamforming performance with reduced computational complexity.
Solution Approach 2:
The patent changes the approach from computing the full SVD to computing only the dominant eigenvectors through iterative methods or truncated decomposition. This parameter change in the computational approach maintains the essential beamforming functionality while dramatically reducing the computational burden, especially for massive MIMO systems with many antennas.
Data Source
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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