Robust Monotonic Optimization Framework for Multicell MISO Systems
2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 5, 2508-2523 p.Article in journal (Refereed) Published
The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are nonconvex and NP-hard, even under simplifying assumptions such as perfect channel knowledge, homogeneous channel properties among users, and simple power constraints. We establish a general optimization framework that systematically solves these problems to global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles general multicell downlink systems with single-antenna users, multiantenna transmitters, arbitrary quadratic power constraints, and robustness to channel uncertainty. A robust fairness-profile optimization (RFO) problem is solved at each iteration, which is a quasiconvex problem and a novel generalization of max-min fairness. The BRB algorithm is computationally costly, but it shows better convergence than the previously proposed outer polyblock approximation algorithm. Our framework is suitable for computing benchmarks in general multicell systems with or without channel uncertainty. We illustrate this by deriving and evaluating a zero-forcing solution to the general problem.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2012. Vol. 60, no 5, 2508-2523 p.
Branch-reduce-and-bound, Network MIMO, dynamic cooperation clusters, fairness-profile, optimal resource allocation, performance region, worst-case robustness
IdentifiersURN: urn:nbn:se:kth:diva-66508DOI: 10.1109/TSP.2012.2184099ISI: 000302938800033ScopusID: 2-s2.0-84860000286OAI: oai:DiVA.org:kth-66508DiVA: diva2:484173
FunderEU, European Research Council, 228044ICT - The Next Generation
QC 201205252012-01-262012-01-262014-03-03Bibliographically approved