Pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing

By using the pilot optimization method of structured compressed sensing in large-scale MIMO-OFDM systems to optimize pilot positions and symbol sequences, the problem of insufficient channel estimation performance is solved and the channel estimation performance is significantly improved.

CN106452534AInactive Publication Date: 2017-02-22NANJING UNIV OF POSTS & TELECOMM
4 Cites 10 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING UNIV OF POSTS & TELECOMM
Filing Date
2016-11-23
Publication Date
2017-02-22
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In massive MIMO-OFDM systems, the performance of channel estimation is affected by the selection and placement of pilot symbols, and traditional methods fail to effectively optimize pilots, resulting in high mean square errors of channel estimation.

Method used

The pilot optimization method based on structured compressed sensing is used to reduce the complete inter-block correlation value by establishing a channel estimation model and optimizing the pilot position and symbol sequence to improve the performance of channel estimation. Specific steps include establishing a channel estimation model with overlapping pilot placement, simplifying it into a structured compressed sensing model, and solving the optimal pilot position and symbol sequence through a random search algorithm.

Benefits of technology

The mean square error (MSE) of channel estimation is significantly reduced and the performance of channel estimation is improved. The simulation results show that using the optimized pilot matrix can reduce MSE by about 3dB.

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Abstract

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.
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Citation Information

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