Robust MIMO precoding for several classes of channel uncertainty
2013 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 12, 3056-3070 p.Article in journal (Refereed) Published
The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty. In this paper, following the worst-case robust philosophy, we consider a robust MIMO precoding design with deterministic imperfect CSIT, formulated as a maximin problem, to maximize the worst-case received signal-to-noise ratio or minimize the worst-case error probability. Given different types of imperfect CSIT in practice, a unified framework is lacking in the literature to tackle various channel uncertainty. In this paper, we address this open problem by considering several classes of uncertainty sets that include most deterministic imperfect CSIT as special cases. We show that, for general convex uncertainty sets, the robust precoder, as the solution to the maximin problem, can be efficiently computed by solving a single convex optimization problem. Furthermore, when it comes to unitarily-invariant convex uncertainty sets, we prove the optimality of a channel-diagonalizing structure and simplify the complex-matrix problem to a real-vector power allocation problem, which is then analytically solved in a waterfilling manner. Finally, for uncertainty sets defined by a generic matrix norm, called the Schatten norm, we provide a fully closed-form solution to the robust precoding design, based on which the robustness of beamforming and uniform-power transmission is investigated.
Place, publisher, year, edition, pages
2013. Vol. 61, no 12, 3056-3070 p.
Convex uncertainty set, imperfect CSIT, maximin, MIMO, minimax, saddle point, Schatten norm, unitarily-invariant uncertainty set, worst-case robustness
IdentifiersURN: urn:nbn:se:kth:diva-134266DOI: 10.1109/TSP.2013.2258016ISI: 000320135200004ScopusID: 2-s2.0-84878291145OAI: oai:DiVA.org:kth-134266DiVA: diva2:666218
FunderEU, European Research Council, 228044
QC 201311212013-11-222013-11-202013-12-09Bibliographically approved