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Transmit beamforming and power control for cellular wireless systems
Abstract
Joint power control and beamforming schemes are proposed for cellular systems where adaptive arrays are used only at base stations. In the uplink, mobile power and receiver diversity combining vectors at the base stations are calculated jointly. The mobile transmitted power is minimized, while the signal-to-interference-and-noise ratio (SINR) at each link is maintained above a threshold. A transmit diversity scheme for the downlink is also proposed where the transmit weight vectors and downlink power allocations are jointly calculated such that the SINR at each mobile is above a target value. The proposed algorithm achieves a feasible solution for the downlink if there is one and minimizes the total transmitted power in the network. In a reciprocal network it can be implemented in a decentralized system, and it does not require global channel response measurements. In a nonreciprocal network, where the uplink and downlink channel responses are different, the proposed transmit beamforming algorithm needs to be implemented in a centralized system, and it requires a knowledge of the downlink channel responses. The performances of these algorithms are compared with previously proposed algorithms through numerical studies.
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Citations
Cited by
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Goldsmith, A., Jafar, S. A., Jindal, N., & Vishwanath, S. (2003) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2003.810294 · Cited by 2,379
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
Rank-Constrained Separable Semidefinite Programming With Applications to Optimal Beamforming
Huang, Y., & Palomar, D. P. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2009.2031732 · Cited by 561
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
“…problems, instead of estimating the beamforming matrix with NK elements, where N is the number of the transmit antennas at the BS and K is the number of users, we exploit the uplink-downlink duality of solutions [5, 6, 12] and predict the virtual uplink power allocation vector with only K elements.”
Background
Metadata
- Authors
- Rashid-Farrokhi, F., Liu, K. J. R., & Tassiulas, L.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 1998-10
- Volume
- 16
- Issue
- 8
- Pages
- 1437-1450
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W2121233130
- Cited by
- 867
- References
- 0
- FWCI
- 7.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Transmit beamforming and power control for cellular wireless systems. https://app.cochat.ai/papers/10.1109/49.730452
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- Rashid-Farrokhi, F., Liu, K. J. R., & Tassiulas, L. (1998). Transmit beamforming and power control for cellular wireless systems. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/49.730452
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