XL-MIMO Near-Field Channel Estimation With LAMP and Sparse Pilots
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Solution Overview
Problem
The challenge of high pilot overhead in traditional channel estimation methods for extra-large-scale massive multiple-input multiple-output (XL-MIMO) systems affects the accuracy of channel state information, which is crucial for 6G communications.
Innovation Solution
A compressed sensing-based near-field channel estimation method using a deep neural network and a learned approximate message passing (LAMP) algorithm to reduce pilot overhead, involving a two-stage training process to optimize transformation matrices and parameters for accurate channel estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pilot design is used in XL-MIMO systems, then channel estimation can be performed, but the pilot overhead becomes huge which affects accuracy of channel estimation
Solution Approach 1:
The patent changes the fundamental parameters of channel estimation by transitioning from traditional pilot-based methods to compressed sensing-based methods. This involves changing the measurement approach from full-rank pilot matrices to sparse sampling, and altering the estimation algorithm from conventional least squares to compressed sensing algorithms that exploit sparsity in the channel representation.
Solution Approach 2:
The patent extracts only the essential information needed for channel estimation by using compressed sensing techniques. Instead of using full-rank pilot matrices that transmit all possible channel state information, the method extracts only the sparsest representation of the channel, removing redundant information while maintaining estimation accuracy.
2Productivity
If the quantity of antennas is dramatically increased in XL-MIMO, then system capacity is improved, but traditional pilot design faces huge overhead
Solution Approach 1:
The patent applies dimensionality change by transforming the channel representation from the spatial domain to the angular-spatial domain using Fourier transforms and polar coordinate transformations. This dimensional transformation enables compression of the channel representation, allowing accurate estimation with far fewer pilot signals than the number of antennas.
Solution Approach 2:
The patent changes the fundamental parameters of channel estimation by transitioning from traditional pilot-based methods to compressed sensing-based methods. This involves changing the measurement approach from full-rank pilot matrices to sparse sampling, and altering the estimation algorithm from conventional least squares to compressed sensing algorithms that exploit sparsity in the channel representation.
3Quantity of substance
If compressed sensing-based near-field channel estimation is used, then pilot overhead is reduced, but estimation accuracy must be maintained
Solution Approach 1:
The patent incorporates feedback mechanisms in the form of iterative refinement and validation steps. The compressed sensing algorithm iteratively refines the channel estimate by comparing with received signals and adjusting the sparse representation, providing feedback that ensures accuracy while maintaining reduced pilot overhead.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the received signals to enhance the sparsity of the channel representation before applying compressed sensing. This includes preliminary Fourier transforms, polar coordinate transformations, and signal preprocessing that prepare the data for more efficient compressed sensing estimation.
Data Source
AI summary
Provided are a compressed sensing-based near-field channel estimation method and apparatus for extra-large-scale massive multiple-input multiple-output (XL-MIMO), a device, and a storage medium. The method includes: obtaining a channel vector of a compressed sensing-based near-field channel on which estimation is to be performed; inputting the channel vector into a constructed channel estimation model, such that the channel estimation model converts the channel vector into a polar-domain channel vector; compressing the polar-domain channel vector into a signal vector; adding noise to the signal vector, and obtaining a received signal vector; inputting the received signal vector into a built-in learned approximate message passing (LAMP) algorithm layer, such that the LAMP algorithm layer performs iteration on the received signal vector and calculates an estimated polar-domain channel vector corresponding to the polar-domain channel vector; and converting the estimated polar-domain channel vector into an estimate of the compressed sensing-based near-field channel.