Channel Estimation in Large-Scale Multi-Antenna Systems for 5G and Beyond - Novel Pilot Structures and Algorithms

Karthik Upadhya

    Research output: ThesisDoctoral ThesisCollection of Articles


    Efficient use of the limited quantity of available spectrum to cater to the exponentially increasing demand for throughput has been the focus of communication and signal processing engineers for the past few decades. With the advent of technologies such as the Internet of things (IoT) or machine-type communications (MTC), devices and appliances around us which have predominantly been offline are being equipped with sensors that generate data and are now driving the demand for throughput. The forthcoming fifth generation (5G) standard is being developed to cater to these use cases and to also increase throughput for conventional mobile users. One of the enabling technologies of 5G is the use of antenna arrays with orders of magnitude more elements than in conventional fourth generation (4G) transceivers. Large-scale multi-antenna systems impose constraints on channel training and transceiver architecture. In this thesis, we consider the problem of channel estimation in large-scale multi-antenna systems at conventional sub-6 GHz and millimeter-wave (mmWave) frequencies. In coherent receivers, channel state information (CSI) is obtained using training, which involves sending known pilots from the transmitter. In multi-cell networks, these pilots will have to be reused in different cells in order to limit the channel estimation overhead, resulting in a detrimental phenomenon known as pilot contamination. Pilot contamination, which causes interference and decreases throughput, is a fundamental challenge in large-scale multi-antenna systems. In the first part of this thesis, we address the issue of pilot contamination and propose using superimposed pilots for avoiding/mitigating interference. We also consider variants of superimposed pilots such as the hybrid system and staggered pilots to improve throughput. Next, we address the problem of estimating spatial covariance matrices (SCMs) in massive MIMO systems in the presence of pilot contamination. SCMs are useful for mitigating the effects of pilot contamination, but have to be estimated from contaminated observations of the user channels, and consequently, are also contaminated. In the second part of this thesis, we propose a novel pilot structure for estimating contamination-free SCMs. The shift to mmWave frequencies opens up large swathes of spectrum for communication, enabling the large throughputs that 5G demands. However, the channel propagation characteristics at these frequencies are markedly different from sub-6 GHz channels and communicating at mmWave frequencies imposes significant constraints on the transceiver architecture. Both factors in turn influence the design of signal processing algorithms. In the third part of the thesis, we address the problem of channel tracking in mmWave transceivers and develop novel semi-blind algorithms to track the channel with a low overhead.
    Translated title of the contributionChannel Estimation in Large-Scale Multi-Antenna Systems for 5G and Beyond - Novel Pilot Structures and Algorithms
    Original languageEnglish
    QualificationDoctor's degree
    Awarding Institution
    • Aalto University
    • Vorobyov, Sergiy, Supervising Professor
    • Vorobyov, Sergiy, Thesis Advisor
    Print ISBNs978-952-60-8100-7
    Electronic ISBNs978-952-60-8101-4
    Publication statusPublished - 2018
    MoE publication typeG5 Doctoral dissertation (article)


    • massive MIMO
    • millimeter wave
    • pilot contamination
    • superimposed pilots
    • staggered pilots
    • channel estimation
    • covariance matrix estimation
    • channel tracking

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