Lattice parameter estimation from multivariate sparse, noisy measurements

Barry G. Quinn, I. Vaughan L Clarkson

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

    Abstract

    In current standards for MIMO wireless communication, considerable energy is expended in providing training from the transmitter so that the channel matrix can be estimated with sufficient accuracy at the receiver. What if this training could be significantly reduced or, in some cases, eliminated? If the communication system uses QAM as its underlying modulation technique, the received signals can be modelled as points on a lattice translate, displaced by noise. Estimation of the lattice basis is closely related to estimation of the channel matrix. We propose a statistical model - MIMO signal transmission being an important example - in which lattice points are observed with noise. The aim is to accurately estimate the lattice basis. We examine a generalisation of the Bartlett point-process periodogram for this purpose. Under appropriate conditions, we show that the estimated basis vectors converge almost surely to the true ones and we derive a central-limit theorem. We demonstrate excellent agreement with the theoretical results through simulation studies.
    LanguageEnglish
    Title of host publication2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages1-5
    Number of pages5
    ISBN (Electronic)9781509030095
    ISBN (Print)9781509030088, 9781509030101
    DOIs
    Publication statusPublished - 2017
    EventIEEE International Workshop on Signal Processing Advances in Wireless Communications (18th : 2017) - Sapporo, Japan
    Duration: 3 Jul 20176 Jul 2017

    Publication series

    Name
    PublisherIEEE
    ISSN (Electronic)1948-3252

    Conference

    ConferenceIEEE International Workshop on Signal Processing Advances in Wireless Communications (18th : 2017)
    Abbreviated titleSPAWC 2017
    CountryJapan
    CitySapporo
    Period3/07/176/07/17

    Fingerprint

    MIMO systems
    Parameter estimation
    Lattice constants
    Quadrature amplitude modulation
    Transmitters
    Communication systems
    Modulation
    Communication
    Statistical Models

    Keywords

    • MIMO channel matrix
    • blind detection
    • dual lattice
    • Bartlett point-process periodogram
    • central-limit theorem

    Cite this

    Quinn, B. G., & Clarkson, I. V. L. (2017). Lattice parameter estimation from multivariate sparse, noisy measurements. In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (pp. 1-5). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SPAWC.2017.8227669
    Quinn, Barry G. ; Clarkson, I. Vaughan L. / Lattice parameter estimation from multivariate sparse, noisy measurements. 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications. Institute of Electrical and Electronics Engineers (IEEE), 2017. pp. 1-5
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    Quinn, BG & Clarkson, IVL 2017, Lattice parameter estimation from multivariate sparse, noisy measurements. in 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications. Institute of Electrical and Electronics Engineers (IEEE), pp. 1-5, IEEE International Workshop on Signal Processing Advances in Wireless Communications (18th : 2017), Sapporo, Japan, 3/07/17. https://doi.org/10.1109/SPAWC.2017.8227669

    Lattice parameter estimation from multivariate sparse, noisy measurements. / Quinn, Barry G.; Clarkson, I. Vaughan L.

    2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications. Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 1-5.

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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    Quinn BG, Clarkson IVL. Lattice parameter estimation from multivariate sparse, noisy measurements. In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications. Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 1-5 https://doi.org/10.1109/SPAWC.2017.8227669