Applications of Robust Optimization in Signal Processing: Beamforming and Power Control Fall 2012

In this presentation, we study the applications of robust optimization in signal processing: robust beamforming in array and power control in wireless networks. First, the background of beamforming and the traditional diagonal loading (DL) method are introduced. Then, the robust beamforming designs are proposed by considering the worst-case distortionless and probabilistic constraints of the steering vector, respectively. It is shown that the robust beamforming design can be formulated as a seconder order cone programming (SOCP) problem or a semidenite programming (SDP) problem, and then can be solved eciently. In the last, the robust power control problem in cognitive radio networks can be formulated as a SOCP problem by considering the worst-case uncertainties in the wireless channel gains.

[1]  Shunqiao Sun,et al.  Robust Power Control in Cognitive Radio Networks: A Distributed Way , 2011, 2011 IEEE International Conference on Communications (ICC).

[2]  Nikos D. Sidiropoulos,et al.  Convex Optimization-Based Beamforming , 2010, IEEE Signal Processing Magazine.

[3]  Sergiy A. Vorobyov,et al.  On the Relationship Between Robust Minimum Variance Beamformers With Probabilistic and Worst-Case Distortionless Response Constraints , 2008, IEEE Transactions on Signal Processing.

[4]  Stephen P. Boyd,et al.  Robust Beamforming via Worst-Case SINR Maximization , 2008, IEEE Transactions on Signal Processing.

[5]  Laurent El Ghaoui,et al.  Robust Optimization , 2021, ICORES.

[6]  Zhi-Quan Luo,et al.  Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem , 2003, IEEE Trans. Signal Process..

[7]  Stephen P. Boyd,et al.  Robust minimum variance beamforming , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.