Convex Conic Formulations of Robust Downlink Precoder Designs With Quality of Service Constraints

We consider the design of linear precoders (beamformers) for broadcast channels with Quality of Service (QoS) constraints for each user, in scenarios with uncertain channel state information (CSI) at the transmitter. We consider a deterministically-bounded model for the channel uncertainty of each user, and our goal is to design a robust precoder that minimizes the total transmission power required to satisfy the users' QoS constraints for all channels within a specified uncertainty region around the transmitter's estimate of each user's channel. Since this problem is not known to be computationally tractable, we will derive three conservative design approaches that yield convex and computationally-efficient restrictions of the original design problem. The three approaches yield semidefinite program (SDP) formulations that offer different trade-offs between the degree of conservatism and the size of the SDP. We will also show how these conservative approaches can be used to derive efficiently-solvable quasi-convex restrictions of some related design problems, including the robust counterpart to the problem of maximizing the minimum signal-to-interference-plus-noise-ratio (SINR) subject to a given power constraint. Our simulation results indicate that in the presence of uncertain CSI the proposed approaches can satisfy the users' QoS requirements for a significantly larger set of uncertainties than existing methods, and require less transmission power to do so.

[1]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[2]  Peter Karlsson,et al.  Performance of multiple-receive multiple-transmit beamforming in WLAN-type systems under power or EIRP constraints with delayed channel estimates , 2002, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367).

[3]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[4]  Alexander Barg,et al.  Bounds on packings of spheres in the Grassmann manifold , 2002, IEEE Trans. Inf. Theory.

[5]  Nihar Jindal,et al.  MIMO broadcast channels with finite rate feedback , 2006, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[6]  Yue Rong,et al.  Robust linear receivers for multiaccess space-time block-coded MIMO systems: a probabilistically constrained approach , 2006, IEEE Journal on Selected Areas in Communications.

[7]  Melvyn Sim,et al.  Tractable Approximations to Robust Conic Optimization Problems , 2006, Math. Program..

[8]  Holger Boche,et al.  Solution of the multiuser downlink beamforming problem with individual SINR constraints , 2004, IEEE Transactions on Vehicular Technology.

[9]  Kung Yao,et al.  Robust downlink power-control for DS-CDMA system with multimedia services , 2003, 2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2003 (IEEE Cat. No.03EX689).

[10]  Leandros Tassiulas,et al.  Transmit beamforming and power control for cellular wireless systems , 1998, IEEE J. Sel. Areas Commun..

[11]  Shahram Shahbazpanahi,et al.  Robust Downlink Power Control in Wireless Cellular Systems , 2004, EURASIP J. Wirel. Commun. Netw..

[12]  Laurent El Ghaoui,et al.  Robust Solutions to Least-Squares Problems with Uncertain Data , 1997, SIAM J. Matrix Anal. Appl..

[13]  Ami Wiesel,et al.  Linear precoding via conic optimization for fixed MIMO receivers , 2006, IEEE Transactions on Signal Processing.

[14]  Yurii Nesterov,et al.  An interior-point method for generalized linear-fractional programming , 1995, Math. Program..

[15]  Wei Yu,et al.  Input optimization for multi-antenna broadcast channels with per-antenna power constraints , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[16]  Daniel Pérez Palomar,et al.  Unified framework for linear MIMO transceivers with shaping constraints , 2004, IEEE Communications Letters.

[17]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[18]  Jon Hamkins,et al.  Gaussian source coding with spherical codes , 2002, IEEE Trans. Inf. Theory.

[19]  Harry L. Van Trees,et al.  Optimum Array Processing , 2002 .

[20]  Stephen P. Boyd,et al.  Robust minimum variance beamforming , 2005, IEEE Transactions on Signal Processing.

[21]  Stephen P. Boyd,et al.  Method of centers for minimizing generalized eigenvalues , 1993, Linear Algebra and its Applications.

[22]  Laurent El Ghaoui,et al.  Robust Solutions to Uncertain Semidefinite Programs , 1998, SIAM J. Optim..

[23]  Zhi Ding,et al.  Robust blind multiuser detection against signature waveform mismatch based on second-order cone programming , 2005, IEEE Transactions on Wireless Communications.

[24]  John M. Cioffi,et al.  Optimum linear joint transmit-receive processing for MIMO channels with QoS constraints , 2004, IEEE Transactions on Signal Processing.

[25]  Leandros Tassiulas,et al.  Joint optimal power control and beamforming in wireless networks using antenna arrays , 1998, IEEE Trans. Commun..

[26]  L. C. Godara,et al.  Handbook of Antennas in Wireless Communications , 2001 .

[27]  Björn E. Ottersten,et al.  On downlink beamforming with indefinite shaping constraints , 2006, IEEE Transactions on Signal Processing.

[28]  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..

[29]  Andrea J. Goldsmith,et al.  Capacity limits of MIMO channels , 2003, IEEE J. Sel. Areas Commun..

[30]  Arkadi Nemirovski,et al.  Robust Convex Optimization , 1998, Math. Oper. Res..