Paper Info
Title
Pilot Optimization for Structured Compressive Sensing Based Channel Estimation in Large-Scale MIMO Systems with Superimposed Pilot Pattern.
Abstract
Compressive sensing (CS) has attracted much attention in wireless communications due to its ability to attain acceptable channel estimates with a small number of pilots. To further reduce the pilot overhead in multi-input multi-output (MIMO) systems, CS-based channel estimation may employ superimposed pilot pattern. Previous works on superimposed pilot design generally allocate pilots randomly, which may give ill-posed measurement matrices. In this paper, we focus on deterministic pilot allocation for large-scale MIMO systems with superimposed pilot pattern to improve the performance of structured CS based channel estimation. By exploiting the spatial common sparsity and the error bound of block sparse reconstruction, a new criterion is firstly proposed to optimize the pilots in the Hadamard space. The proposed criterion makes full use of the information about the principal angles across the blocks in the measurement matrix, which can enhance the average recovery ability and exclude the worst pilots simultaneously. Secondly, a genetic algorithm is proposed to minimize the merit factor of the proposed criterion efficiently. Simulation results show that the proposed optimized pilots outperform the random pilots in terms of mean-squared error by about 3 dB. Moreover, the proposed criterion is more likely to achieve better measurement matrices than the traditional criteria.
Year
DOI
Venue
2018
https://doi.org/10.1007/s11277-018-5361-x
Wireless Personal Communications
Keywords
Field
DocType
Large-scale MIMO,Channel estimation,Structured compressive sensing,Pilot allocation,Genetic algorithm
Small number,Hadamard space,Wireless,Matrix (mathematics),Computer science,MIMO,Communication channel,Algorithm,Real-time computing,Genetic algorithm,Compressed sensing
Journal
Volume
Issue
ISSN
100
3
0929-6212
Citations 
PageRank 
References 
0
0.34
24
Authors
3
Name
Order
Citations
PageRank
Jun Cai137339.29
Xueyun He2502.95
Rongfang Song315925.85