Robust Optimization and Designs

Oct 1, 2019

The design of communication systems depends strongly on the degree of knowledge of the channel state information (CSI). The best spectral efﬁciency and/or performance is obviously achieved when perfect CSI is available at both sides of the link. However, in practical communication systems, imperfect CSI arises from a variety of sources such as channel estimation errors, quantization of the channel estimate in the feedback channel, and outdated channel estimates with respect to the current channel (for time-varying channels). When the CSI is imperfect, it is necessary to model such imperfections or uncertainties and develop robust designs that take them into account.
There are two main philosophies for the design of systems robust to uncertainties: the worst-case approach, which considers that the uncertainty is within a given set around the nominal estimated value, and the Bayesian approach, which models the uncertainty statistically. The worst-case design guarantees a certain system performance for any possible channel sufﬁciently close to the estimated one, whereas the Bayesian design guarantees a certain system performance averaged over the channel realizations. We consider both perspectives in the design of robust MIMO communication systems.

Papers

Yang Yang, Gesualdo Scutari, Peiran Song, and Daniel P. Palomar, “Robust MIMO Cognitive Radio Systems Under Interference Temperature Constraints,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2465-2482, Nov. 2013.

Jiaheng Wang, Mats Bengtsson, Björn Ottersten, and Daniel P. Palomar, “Robust MIMO Precoding for Several Classes of Channel Uncertainty,” IEEE Trans. on Signal Processing, vol. 61, no. 12, pp. 3056-3070, June 2013.
Yongwei Huang, Daniel P. Palomar, and Shuzhong Zhang, “Lorentz-Positive Maps and Quadratic Matrix Inequalities with Applications to Robust MISO Transmit Beamforming,” IEEE Trans. on Signal Processing, vol. 61, no. 5, pp. 1121-1130, March 2013.
Jiaheng Wang, Gesualdo Scutari, and Daniel P. Palomar, “Robust MIMO Cognitive Radio via Game Theory,” IEEE Trans. on Signal Processing, vol. 59, no. 3, pp. 1183-1201, March 2011.
Jiaheng Wang and Daniel P. Palomar, “Robust MMSE Precoding in MIMO Channels with Pre-Fixed Receivers,” IEEE Trans. on Signal Processing, vol. 58, no. 11, pp. 5802-5818, Nov. 2010.

Jiaheng Wang and Daniel P. Palomar, “Worst-Case Robust MIMO Transmission with Imperfect Channel Knowledge,” IEEE Trans. on Signal Processing, vol. 57, no. 8, pp. 3086-3100, Aug. 2009.
Xi Zhang, Daniel P. Palomar, and Björn Ottersten, “Statistically Robust Design of Linear MIMO Transceivers,” IEEE Trans. on Signal Processing, vol. 56, no. 8, pp. 3678-3689, Aug. 2008.
A. Pascual-Iserte, Daniel P. Palomar, Ana I. Pérez-Neira, and Miguel A. Lagunas, “A Robust Maximin Approach for MIMO Communications with Partial Channel State Information Based on Convex Optimization,” IEEE Trans. on Signal Processing, vol. 54, no. 1, pp. 346-360, Jan. 2006.
Daniel P. Palomar, John M. Cioffi, and Miguel Angel Lagunas, “Uniform Power Allocation in MIMO Channels: A Game-Theoretic Approach,” IEEE Trans. on Information Theory, vol. 49, no. 7, pp. 1707-1727, July 2003.