MURAL - Maynooth University Research Archive Library



    CFO Estimation for OFDM-based Massive MIMO Systems in Asymptotic Regime


    Sabeti, Parna and Farhang, Arman and Marchetti, Nicola and Doyle, Linda E. (2018) CFO Estimation for OFDM-based Massive MIMO Systems in Asymptotic Regime. In: 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE. ISBN 9781538647523

    [img]
    Preview
    Download (130kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    Massive multiple input multiple output (MIMO) plays a pivotal role in the fifth generation (5G) wireless networks. However, the carrier frequency offset (CFO) estimation is a challenging issue in the uplink of multi-user massive MIMO systems. In fact, frequency synchronization can impose a considerable amount of computational complexity to the base station (BS) due to a large number of BS antennas. In this paper, thanks to the properties of massive MIMO in the asymptotic regime, we develop a simple synchronization technique and derive a closed form equation for CFO estimation. We show that the phase information of the covariance matrix of the received signals is solely dependent on the users’ CFOs. Hence, if a real-valued pilot is chosen, the CFO values can be straightforwardly calculated from this matrix. Hence, there is no need to deal with a complex optimization problem like the other existing CFO estimation techniques in the literature. Our simulation results testify the efficacy of our proposed CFO estimation technique. As we have shown, the performance of our method does not deteriorate as the number of users increases.

    Item Type: Book Section
    Keywords: CFO Estimation; OFDM-Based Massive MIMO Systems; Asymptotic Regime;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 13426
    Identification Number: https://doi.org/10.1109/SAM.2018.8448654
    Depositing User: Arman Farhang
    Date Deposited: 08 Oct 2020 14:05
    Publisher: IEEE
    Refereed: Yes
    URI:
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads