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Jamming-Robust Uplink Transmission for Spatially Correlated Massive MIMO Systems
Iran Univ Sci and Technol, Iran.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5954-434X
Iran Univ Sci and Technol, Iran.
2020 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 68, no 6, p. 3495-3504Article in journal (Refereed) Published
Abstract [en]

In this paper, we consider how the uplink transmission of a spatially correlated massive multiple-input multiple-output (MIMO) system can be protected from a jamming attack. To suppress the jamming, we propose a novel framework including a new optimal linear estimator in the training phase and a bilinear equalizer in the data phase. The proposed estimator is optimal in the sense of maximizing the spectral efficiency of the legitimate system attacked by a jammer, and its implementation needs the statistical knowledge about the jammers channel. We derive an efficient algorithm to estimate the jamming information needed for implementation of the proposed framework. Furthermore, we demonstrate that optimized power allocation at the legitimate users can improve the performance of the proposed framework regardless of the jamming power optimization. Our proposed framework can be exploited to combat jamming in scenarios with either ideal or non-ideal hardware at the legitimate users and the jammer. Numerical results reveal that using the proposed framework, the jammer cannot dramatically affect the performance of the legitimate system.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2020. Vol. 68, no 6, p. 3495-3504
Keywords [en]
Jamming; MIMO communication; Channel estimation; Covariance matrices; Uplink; Interference; Hardware; Massive MIMO; spatial correlation; jamming suppression; hardware impairments
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-167684DOI: 10.1109/TCOMM.2020.2978192ISI: 000543389800018OAI: oai:DiVA.org:liu-167684DiVA, id: diva2:1454707
Note

Funding Agencies|ELLIIT and Centrum for Industriell Informationsteknologi (CENIIT)

Available from: 2020-07-20 Created: 2020-07-20 Last updated: 2020-07-20

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • Other locale
More languages
Output format
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