Sparse Subspace Decomposition for Millimeter Wave MIMO Channel Estimation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publication2016 IEEE Global Communications Conference (GLOBECOM)
Subtitle of host publicationProceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
ISBN (Electronic)9781509013272
ISBN (Print)9781509013289
Publication statusPublished - Dec 2016

Conference

Title59th IEEE Global Communications Conference (IEEE GLOBECOM 2016)
PlaceUnited States
CityWashington
Period4 - 8 December 2016

Abstract

Millimeter wave multiple-input multiple-output (MIMO) communication systems must operate over sparse wireless links and will require large antenna arrays to provide high throughput. To achieve sufficient array gains, these systems must learn and adapt to the channel state conditions. However, conventional MIMO channel estimation can not be directly extended to millimeter wave due to the constraints on cost-effective millimeter wave operation imposed on the number of available RF chains. Sparse subspace scanning techniques that search for the best subspace sample from the sounded subspace samples have been investigated for channel estimation. However, the performance of these techniques starts to deteriorate as the array size grows, especially for the hybrid precoding architecture. The millimeter wave channel estimation challenge still remains and should be properly addressed before the system can be deployed and used to its full potential. In this work, we propose a sparse subspace decomposition (SSD) technique for sparse millimeter wave MIMO channel estimation. We formulate the channel estimation as an optimization problem that minimizes the subspace distance from the received subspace samples. Alternating optimization techniques are devised to tractably handle the non-convex problem. Numerical simulations demonstrate that the proposed method outperforms other existing techniques with remarkably low overhead.

Citation Format(s)

Sparse Subspace Decomposition for Millimeter Wave MIMO Channel Estimation. / Zhang, Wei; Kim, Taejoon; Love, David J.

2016 IEEE Global Communications Conference (GLOBECOM): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1-6 7842278.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review