TY - GEN
T1 - Compressive impulse response sensing of the sparse channel in multipath environments
AU - Wu, Fei Yun
AU - Yang, Kunde
AU - Tian, Tian
AU - Tong, Feng
AU - Hu, Yang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Channel impulse response (CIR) in noisy multipath environments can be obtained based on the least squares criterion. However, the CIR obtained is contaminated by pseudo paths. By exploiting the sparse structure of a sparse channel in multipath environments, this work presents an approach for compressed sensing that uses the Gram-Schmidt algorithm to find orthogonal bases (Gram-Schmidt matching pursuit, GSMP), which leads to a fast, orthogonal way of selecting the supports for the dictionaries. The probe signal is used to construct the dictionary matrix, whose column vectors are selected as the supports. The selected supports from dictionary matrix and the noisy received signal are used for recovering the CIR. The simulation results confirm that the proposed GSMP method, compared to LS, MP, CoSaMP, ROMP methods, provides superior performance in terms of mean square error (MSE).
AB - Channel impulse response (CIR) in noisy multipath environments can be obtained based on the least squares criterion. However, the CIR obtained is contaminated by pseudo paths. By exploiting the sparse structure of a sparse channel in multipath environments, this work presents an approach for compressed sensing that uses the Gram-Schmidt algorithm to find orthogonal bases (Gram-Schmidt matching pursuit, GSMP), which leads to a fast, orthogonal way of selecting the supports for the dictionaries. The probe signal is used to construct the dictionary matrix, whose column vectors are selected as the supports. The selected supports from dictionary matrix and the noisy received signal are used for recovering the CIR. The simulation results confirm that the proposed GSMP method, compared to LS, MP, CoSaMP, ROMP methods, provides superior performance in terms of mean square error (MSE).
KW - Channel impulse response (CIR)
KW - Compressive sensing
KW - Mean square error (MSE)
UR - http://www.scopus.com/inward/record.url?scp=85078900551&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC46631.2019.8960724
DO - 10.1109/ICSPCC46631.2019.8960724
M3 - 会议稿件
AN - SCOPUS:85078900551
T3 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
BT - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Y2 - 20 September 2019 through 22 September 2019
ER -