TY - GEN
T1 - Channel Estimation for Millimeter Wave MIMO Systems over Frequency Selective Channels via PARAFAC Decomposition
AU - Zhou, Zhou
AU - Fang, Jun
AU - Li, Hongbin
AU - Blum, Rick S.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - In this paper, the downlink channel estimation for millimeter wave (mmWave) MIMO systems over frequency selective channels is considered, where both the base station (BS) and the mobile station (MS) are equipped with massive number of antennas. We assume hybrid analog and digital beamforming structures are employed at BS and MS. To overcome the frequency selective fading, we employ orthogonal frequencydivision multiplexing (OFDM) in transmission. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for downlink channel estimation. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. Simulation results show that the proposed method presents a clear advantage over the compressed sensing-based method in terms of both estimation accuracy and computational complexity.
AB - In this paper, the downlink channel estimation for millimeter wave (mmWave) MIMO systems over frequency selective channels is considered, where both the base station (BS) and the mobile station (MS) are equipped with massive number of antennas. We assume hybrid analog and digital beamforming structures are employed at BS and MS. To overcome the frequency selective fading, we employ orthogonal frequencydivision multiplexing (OFDM) in transmission. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for downlink channel estimation. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. Simulation results show that the proposed method presents a clear advantage over the compressed sensing-based method in terms of both estimation accuracy and computational complexity.
KW - CANDECOMP/PARAFAC (CP) decomposition
KW - Channel estimation
KW - MmWave MIMO-OFDM systems
UR - http://www.scopus.com/inward/record.url?scp=85040538773&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040538773&partnerID=8YFLogxK
U2 - 10.1109/VTCSpring.2017.8108358
DO - 10.1109/VTCSpring.2017.8108358
M3 - Conference contribution
AN - SCOPUS:85040538773
T3 - IEEE Vehicular Technology Conference
BT - 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings
T2 - 85th IEEE Vehicular Technology Conference, VTC Spring 2017
Y2 - 4 June 2017 through 7 June 2017
ER -