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Journal of Global Optimization

, Volume 62, Issue 4, pp 877–886 | Cite as

Some interesting properties for zero-forcing beamforming under per-antenna power constraints in rural areas

  • Bin Li
  • Hai Huyen Dam
  • Antonio Cantoni
  • Kok Lay Teo
Article

Abstract

Providing broadband services in the rural area is a challenging work. Multi-user multiple-input multiple-output (MU-MIMO) systems can be applied to increase spectral efficiency. One of the most commonly used method in MU-MIMO broadcast channel is zero-forcing beamforming (ZFBF) since it provides a good trade off between complexity and performance. In this paper, we consider the ZFBF under the per-antenna power constraints. Particularly, a deterministic line-of-sight modeling of the downlink channels is adopted. To reduce the computational complexity, we consider the problem in which all the equality constraints are eliminated. By examining the KKT optimality conditions and the properties of the channel, some interesting properties are revealed.

Keywords

Zero-forcing beamforming Per-antenna power constraints  MIMO Line-of-sight Rural areas 

Notes

Acknowledgments

This work was supported by a grant from the Australia Research Council (No. DP120103859).

References

  1. 1.
    Caire, G., Shamai, S.: On the achievable throughput of multiatenna Gaussian broadcast channel. IEEE Trans. Inf. Theory. 49, 1691–1706 (2003)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Joham, M., Utschick, W., Nossek, S.: Linear transmit processing in MIMO communications system. IEEE Trans. Signal Process. 538, 2700–2712 (2005)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Sung, H., Lee, S.R., Lee, I.: Genearlized channel inversion methods for multiuser MIMO systems. IEEE Trans. Commun. 57, 3489–3499 (2009)CrossRefGoogle Scholar
  4. 4.
    Udupa, P.S., Lehnert, J.S.: Optimizing zero-forcing precoders for MIMO broadcast systems. IEEE Trans. Commun. 55(8), 1516–1524 (2007)CrossRefGoogle Scholar
  5. 5.
    Wiesel, A., Eldar, Y.C., Shamai, S.: Linear precoding via conic optimizaiton for fixed MIMO receivers. IEEE Trans. Signal Process. 54, 161–176 (2006)CrossRefGoogle Scholar
  6. 6.
    Wiesel, A., Eldar, Y.C., Shamai, S.: Zero-forcing precoding and generalized inverses. IEEE Trans. Signal Process. 56(9), 4409–4418 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Li, B., Dam, H.H., Cantoni, A., Teo, K.L.: A primal–dual interior point method for optimal zero-forcing beamformer design under per-antenna power constraints. Optim. Lett. 8(6), 1829–1843 (2014)Google Scholar
  8. 8.
    Karakayali, K., Yates, R., Foschini, G., Valenzuela, R.: Optimal zero-forcing beamforming with per-antenna power constraints. In: IEEE International Symposium on Information Theory. Nice, France, pp. 101–105 (2007)Google Scholar
  9. 9.
    Lee, S.R., Kim, J.S., Moon, S.H., Kong, H.B., Lee, I.: Zero-forcing beamforming in multiuser MISO downlink systems under per-antenna power constraint and equal-rate metric. IEEE Trans. Wireless Commun. 12(1), 228–236 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Bin Li
    • 1
  • Hai Huyen Dam
    • 2
  • Antonio Cantoni
    • 1
  • Kok Lay Teo
    • 2
  1. 1.School of Electrical, Electronic and Computer EngineeringThe University of Western AustraliaCrawleyAustralia
  2. 2.Department of Mathematics and StatisticsCurtin UniversityPerthAustralia

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