The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Transmit beamforming for physical-layer multicasting
Abstract
This paper considers the problem of downlink transmit beamforming for wireless transmission and downstream precoding for digital subscriber wireline transmission, in the context of common information broadcasting or multicasting applications wherein channel state information (CSI) is available at the transmitter. Unlike the usual "blind" isotropic broadcasting scenario, the availability of CSI allows transmit optimization. A minimum transmission power criterion is adopted, subject to prescribed minimum received signal-to-noise ratios (SNRs) at each of the intended receivers. A related max-min SNR "fair" problem formulation is also considered subject to a transmitted power constraint. It is proven that both problems are NP-hard; however, suitable reformulation allows the successful application of semidefinite relaxation (SDR) techniques. SDR yields an approximate solution plus a bound on the optimum value of the associated cost/reward. SDR is motivated from a Lagrangian duality perspective, and its performance is assessed via pertinent simulations for the case of Rayleigh fading wireless channels. We find that SDR typically yields solutions that are within 3-4 dB of the optimum, which is often good enough in practice. In several scenarios, SDR generates exact solutions that meet the associated bound on the optimum value. This is illustrated using measured very-high-bit-rate Digital Subscriber line (VDSL) channel data, and far-field beamforming for a uniform linear transmit antenna array.
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Citations
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Cited by
Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C. K., & Zhang, J. C. (2014) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2014.2328098 · Cited by 8,106
Semidefinite Relaxation of Quadratic Optimization Problems
Luo, Z.-Q., Ma, W.-K., So, A., Ye, Y., & Zhang, S. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.936019 · Cited by 3,715
Self-citationMIMO Broadcasting for Simultaneous Wireless Information and Power Transfer
Zhang, R., & Ho, C. K. (2013) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2013.031813.120224 · Cited by 2,739
Federated Learning via Over-the-Air Computation
Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084
“A natural way to address it is adopting the matrix lifting technique [40].”
Learning to Optimize: Training Deep Neural Networks for Interference Management
Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2866382 · Cited by 874
Self-citation
Metadata
- Authors
- Sidiropoulos, N. D., Davidson, T. N., & Luo, Z.-Q.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2006-06
- Volume
- 54
- Issue
- 6
- Pages
- 2239-2251
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W1981044874
- Cited by
- 1,335
- References
- 1
- FWCI
- 31.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sidiropoulos, N. D., Davidson, T. N., & Luo, Z.-Q. (2006). Transmit beamforming for physical-layer multicasting. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2006.872578
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