The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Linear precoding via conic optimization for fixed MIMO receivers
Abstract
In this paper, the problem of designing linear precoders for fixed multiple-input-multiple-output (MIMO) receivers is considered. Two different design criteria are considered. In the first, the transmitted power is minimized subject to signal-to-interference-plus-noise-ratio (SINR) constraints. In the second, the worst case SINR is maximized subject to a power constraint. It is shown that both problems can be solved using standard conic optimization packages. In addition, conditions are developed for the optimal precoder for both of these problems, and two simple fixed-point iterations are proposed to find the solutions that satisfy these conditions. The relation to the well-known uplink-downlink duality in the context of joint transmit beamforming and power control is also explored. The proposed precoder design is general, and as a special case, it solves the transmit rank-one beamforming problem. Simulation results in a multiuser system show that the resulting precoders can significantly outperform existing linear precoders.
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Cites
Matrix Analysis and Applied Linear Algebra
Meyer, C. (2000) · doi:10.1137/1.9780898719512 · Cited by 3,565
Robust optimization ? methodology and applications
Ben-Tal, A., & Nemirovski, A. (2002) · Mathematical Programming · doi:10.1007/s101070100286 · Cited by 1,409
Cited by
An introduction to convex optimization for communications and signal processing
Luo, Z.-Q., & Yu, W. (2006) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2006.879347 · Cited by 562
Group Sparse Beamforming for Green Cloud-RAN
(2014) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2014.040214.131770 · Cited by 468
Foundations of MIMO Communication
Heath Jr, R. W., & Lozano, A. (2018) · Cambridge University Press eBooks · doi:10.1017/9781139049276 · Cited by 426
A Deep Learning Framework for Optimization of MISO Downlink Beamforming
Xia, W., Zheng, G., Zhu, Y., Zhang, J., Wang, J., & Petropulu, A. P. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2960361 · Cited by 345
“Practical algorithms to find optimal solutions are available for P1 [8], [12], [47] and P2 [5], [7], [8], [12], [13], thus supervised learning can be adopted for those problems.”
BackgroundMethodTransfer Learning and Meta Learning-Based Fast Downlink Beamforming Adaptation
Yuan, Y., Zheng, G., Wong, K.-K., Ottersten, B., & Luo, Z.-Q. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3035843 · Cited by 133
Metadata
- Authors
- Wiesel, A., Eldar, Y. C., & Shamai, S.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2006-01
- Volume
- 54
- Issue
- 1
- Pages
- 161-176
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2109005286
- Cited by
- 974
- References
- 2
- FWCI
- 26.69
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wiesel, A., Eldar, Y. C., & Shamai, S. (2006). Linear precoding via conic optimization for fixed MIMO receivers. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2005.861073
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