The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Open accessAbstract
We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the MS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP, and the channel parameters can be estimated from the associated factor matrices. 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. We also develop Cramér-Rao bound (CRB) results for channel parameters and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs and present a clear advantage over the compressed sensing-based method.
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Cites
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Beck, A., & Teboulle, M. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080716542 · Cited by 11,952
Tensor Decompositions and Applications
Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,301
Fundamentals of statistical signal processing: Estimation theory
Prasad, K. V. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90195-3 · Cited by 7,902
Pati, Y. C., Rezaiifar, R., & Krishnaprasad, P. S. (2002) · Proceedings of 27th Asilomar Conference on Signals, Systems and Computers · doi:10.1109/acssc.1993.342465 · Cited by 4,323
Spatially Sparse Precoding in Millimeter Wave MIMO Systems
Ayach, O. E., Rajagopal, S., Abu-Surra, S., Pi, Z., & Heath, R. W. (2014) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2014.011714.130846 · Cited by 3,557
Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
Alkhateeb, A., El Ayach, O., Leus, G., & Heath, R. W. (2014) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2014.2334278 · Cited by 2,562
Millimeter Wave Channel Modeling and Cellular Capacity Evaluation
Akdeniz, M. R., Liu, Y., Samimi, M. K., Sun, S., Rangan, S., Rappaport, T. S., & Erkip, E. (2014) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2014.2328154 · Cited by 2,476
Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges
Rangan, S., Rappaport, T. S., & Erkip, E. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2299397 · Cited by 2,428
Kruskal, J. B. (1977) · Linear Algebra and its Applications · doi:10.1016/0024-3795(77)90069-6 · Cited by 1,619
Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., & PHAN, H. A. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2297439 · Cited by 1,384
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Millimeter Wave Communications for Future Mobile Networks
Xiao, M., Mumtaz, S., Huang, Y., Dai, L., Li, Y., Matthaiou, M., Karagiannidis, G. K., Bjornson, E., Yang, K., I, C.-L., et al. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2719924 · Cited by 1,122
Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field?
Cui, M., & Dai, L. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3146400 · Cited by 871
Hybrid Analog and Digital Beamforming for mmWave OFDM Large-Scale Antenna Arrays
Sohrabi, F., & Yu, W. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2698958 · Cited by 484
Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems
Dong, P., Zhang, H., Li, G. Y., Gaspar, I. S., & NaderiAlizadeh, N. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2925975 · Cited by 437
Beam Alignment in mmWave V2X Communications: A Survey
Tan, J., Luan, T. H., Guan, W., Wang, Y., Peng, H., Zhang, Y., Zhao, D., & Lu, N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3383093 · Cited by 75
Metadata
- Authors
- Zhou, Z., Fang, J., Yang, L., Li, H., Chen, Z., & Blum, R. S.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2017-07
- Volume
- 35
- Issue
- 7
- Pages
- 1524-1538
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W2547350840
- Cited by
- 309
- References
- 39
- FWCI
- 10.40
- Type
- journal article
- Language
- en
- Open access
- green
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