- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | YANG Liu, BAI Chaoyuan, FAN Pingzhi. Co-Optimization Algorithm for Measurement Matrix of Compressive Sensing[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230032
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For the compressive sensing algorithm, the correlation between measurement matrix and sparse base always determines the accuracy of signal recovery. In order to improve the performance of the compressive sensing algorithm in signal reconstruction in large-scale communication scenarios, the measurement matrix was improved based on matrix decomposition and equiangular tight frame (ETF) theory. Firstly, a dictionary matrix was constructed based on the measurement matrix and sparse base, and a Gram matrix was constructed. Eigenvalue decomposition was used to reduce the average correlation of the Gram matrix. Then, based on the ETF theory and gradient reduction theory, the Gram matrix was pushed to approach the ETF matrix to reduce the maximum value of the non-principal diagonal elements of the Gram matrix and the maximum correlation between the measurement matrix and the sparse basis. The orthogonal matching pursuit (OMP) algorithm was used as the reconstruction algorithm for simulation and verification, and the simulation results show that after optimization, the correlation coefficient of the matrix is reduced by 40%–50%. In channel estimation and active user detection, the estimation error of active user number by the proposed algorithm is more than 50% lower than that by other optimization algorithms under high sparsity; compared with other matrices, the mean square error of channel estimation is improved by 3 dB, and the bit error rate performance is improved by 2 dB.
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