Hybrid digital-to-analog precoding design for mm-wave systems

Osama Alluhaibi, Qasim Ahmed, Junyuan Wang, Huiling Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Hybrid digital-to-analog (D-A) precoding is a promising technology to reduce the number of radio frequency (RF) chains in a millimetre-wave (mm-Wave) multiple-input multiple-output (MIMO) system. To reap the full scale benefits of the hybrid D-A precoding, in this paper, two algorithms are proposed to maximise the capacity of the hybrid D-A mm-Wave MIMO system. The first algorithm is based on the principle of manifold optimisation (MO). The second algorithm operates on particle swarm optimisation (PSO). These two algorithms are compared with three existing hybrid D-A precoding algorithms in the literature. The simulation results show that the proposed algorithms achieve higher capacity than the existing hybrid D-A precoding algorithms with lower computational complexity.
LanguageEnglish
Title of host publication2017 IEEE International Conference on Communications (ICC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781467389990
DOIs
Publication statusPublished - 31 Jul 2017
Event2017 IEEE International Conference on Communications: Bridging People, Communities and Cultures - Palais des Congrès - Porte Maillot, Paris, France
Duration: 21 May 201725 May 2017
http://icc2017.ieee-icc.org/ (Link to Conference Website )

Publication series

Name
ISSN (Electronic)1938-1883

Conference

Conference2017 IEEE International Conference on Communications
Abbreviated titleICC'17
CountryFrance
CityParis
Period21/05/1725/05/17
Internet address

Fingerprint

Millimeter waves
Particle swarm optimization (PSO)
Computational complexity

Cite this

Alluhaibi, O., Ahmed, Q., Wang, J., & Zhu, H. (2017). Hybrid digital-to-analog precoding design for mm-wave systems. In 2017 IEEE International Conference on Communications (ICC) Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2017.7996401
Alluhaibi, Osama ; Ahmed, Qasim ; Wang, Junyuan ; Zhu, Huiling. / Hybrid digital-to-analog precoding design for mm-wave systems. 2017 IEEE International Conference on Communications (ICC). Institute of Electrical and Electronics Engineers Inc., 2017.
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Alluhaibi, O, Ahmed, Q, Wang, J & Zhu, H 2017, Hybrid digital-to-analog precoding design for mm-wave systems. in 2017 IEEE International Conference on Communications (ICC). Institute of Electrical and Electronics Engineers Inc., 2017 IEEE International Conference on Communications, Paris, France, 21/05/17. https://doi.org/10.1109/ICC.2017.7996401

Hybrid digital-to-analog precoding design for mm-wave systems. / Alluhaibi, Osama; Ahmed, Qasim; Wang, Junyuan; Zhu, Huiling.

2017 IEEE International Conference on Communications (ICC). Institute of Electrical and Electronics Engineers Inc., 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Hybrid digital-to-analog (D-A) precoding is a promising technology to reduce the number of radio frequency (RF) chains in a millimetre-wave (mm-Wave) multiple-input multiple-output (MIMO) system. To reap the full scale benefits of the hybrid D-A precoding, in this paper, two algorithms are proposed to maximise the capacity of the hybrid D-A mm-Wave MIMO system. The first algorithm is based on the principle of manifold optimisation (MO). The second algorithm operates on particle swarm optimisation (PSO). These two algorithms are compared with three existing hybrid D-A precoding algorithms in the literature. The simulation results show that the proposed algorithms achieve higher capacity than the existing hybrid D-A precoding algorithms with lower computational complexity.

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Alluhaibi O, Ahmed Q, Wang J, Zhu H. Hybrid digital-to-analog precoding design for mm-wave systems. In 2017 IEEE International Conference on Communications (ICC). Institute of Electrical and Electronics Engineers Inc. 2017 https://doi.org/10.1109/ICC.2017.7996401