CFO estimation based on Taylor MVDR approach using particle swarm optimization for interleaved OFDMA uplink systems
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605469598 | ||
| 003 | CHVBK | ||
| 005 | 20210128100323.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151001xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1446-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1446-y | ||
| 100 | 1 | |a Chang |D Jhih-Chung |u Department of Information Technology, Ling Tung University, 408, Taichung, Taiwan |4 aut | |
| 245 | 1 | 0 | |a CFO estimation based on Taylor MVDR approach using particle swarm optimization for interleaved OFDMA uplink systems |h [Elektronische Daten] |c [Jhih-Chung Chang] |
| 520 | 3 | |a The problem of carrier frequency offset (CFO) estimation based on the Taylor minimum variance distortionless response (MVDR) criterion in interleaved orthogonal frequency division multiple access uplink systems was investigated. However, in the presence of large CFOs, more iteration is required for the iterative search technique. Therefore, a new CFO vector, based on the Taylor series expansion of the vector initially given, is proposed. The problem of finding the new CFO vector is formulated as the closed form of a generalized eigenvalue problem, which is readily solved. A two-stage method for increasing the accuracy of the residual CFO estimation featuring a low computational load is presented in this paper. A proper initial CFO estimate is required for the Taylor MVDR estimator. First, an initial CFO estimate is determined using a particle swarm optimization estimator. The predominant CFO estimate is then sent to the Taylor MVDR estimator to form an estimate. The proposed estimator can estimate CFOs with a lower computational load. Several computer simulation results are provided to illustrate the effectiveness of the proposed estimation approach. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Carrier frequency offset |2 nationallicence | |
| 690 | 7 | |a Minimum variance distortionless response |2 nationallicence | |
| 690 | 7 | |a Orthogonal frequency division multiple access |2 nationallicence | |
| 690 | 7 | |a Particle swarm optimization |2 nationallicence | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/10(2015-10-01), 2845-2859 |x 1432-7643 |q 19:10<2845 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1446-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1446-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 100 |E 1- |a Chang |D Jhih-Chung |u Department of Information Technology, Ling Tung University, 408, Taichung, Taiwan |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/10(2015-10-01), 2845-2859 |x 1432-7643 |q 19:10<2845 |1 2015 |2 19 |o 500 | ||