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Distributed CSI Acquisition and Coordinated Precoding for TDD Multicell MIMO Systems
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-7724-5896
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3599-5584
2016 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, no 5, 2890-2906 p.Article in journal (Refereed) Published
Abstract [en]

Several distributed coordinated precoding methods exist in the downlink multicell MIMO literature, many of which assume perfect knowledge of received signal covariance and local effective channels. In this work, we let the notion of channel state information (CSI) encompass this knowledge of covariances and effective channels. We analyze what local CSI is required in the WMMSE algorithm for distributed coordinated precoding, and study how this required CSI can be obtained in a distributed fashion. Based on pilot-assisted channel estimation, we propose three CSI acquisition methods with different tradeoffs between feedback and signaling, backhaul use, and computational complexity. One of the proposed methods is fully distributed, meaning that it only depends on over-the-air signaling but requires no backhaul, and results in a fully distributed joint system when coupled with the WMMSE algorithm. Naively applying the WMMSE algorithm together with the fully distributed CSI acquisition results in catastrophic performance however, and therefore we propose a robustified WMMSE algorithm based on the well known diagonal loading framework. By enforcing properties of the WMMSE solutions with perfect CSI onto the problem with imperfect CSI, the resulting diagonally loaded spatial filters are shown to perform significantly better than the naive filters. The proposed robust and distributed system is evaluated using numerical simulations, and shown to perform well compared with benchmarks. Under centralized CSI acquisition, the proposed algorithm performs on par with other existing centralized robust WMMSE algorithms. When evaluated in a large scale fading environment, the performance of the proposed system is promising.

Place, publisher, year, edition, pages
IEEE Press, 2016. Vol. 65, no 5, 2890-2906 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-166426DOI: 10.1109/TVT.2015.2432051ISI: 000376094500005Scopus ID: 2-s2.0-84969915743OAI: oai:DiVA.org:kth-166426DiVA: diva2:811008
Funder
Swedish Research Council, 621-2012-4134
Note

QC 20150521

Available from: 2015-05-09 Created: 2015-05-09 Last updated: 2017-12-04Bibliographically approved

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