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Phase-Noise Mitigation at 60 GHz with a Novel Hybrid MIMO Architecture

Published: 01 October 2018 Publication History

Abstract

Hybrid MIMO architectures provide a simple and effective way to implement millimeter-wave multi-user MIMO, which can deliver extremely high throughput by combining spatial multiplexing and a large bandwidth. Despite both theoretical and practical results being already available, the performance of these systems when including non-idealities is still being analyzed. In this paper we focus on phase noise originating from the multiple PLLs that causes two main problems: symbol rotation and inter-user interference. Although symbol rotation can be mitigated with conventional receiver phase noise tracking schemes, these schemes need to be jointly designed with the transmitted frame, which needs to support multi-user precoding. We analyze different receiver mitigation schemes based either on known training sequences or using blind estimation. Moreover, we propose a novel hybrid base station architecture which feeds back partial information of the status of the transmitter PLLs. Using the PLL status information, baseband compensation can be used to minimize inter-user interference improving the receiver EVM by around 5\,dB.

References

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Cited By

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  • (2023)High-speed Machine Learning-enhanced Receiver for Millimeter-Wave SystemsIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10229087(1-10)Online publication date: 17-May-2023
  • (2022)Model-free machine learning of wireless SISO/MIMO communicationsComputer Communications10.1016/j.comcom.2021.09.033181:C(192-202)Online publication date: 1-Jan-2022
  • (2022)Analysis of Phase Noise Issues in Millimeter Wave Systems for 5G CommunicationsWireless Personal Communications10.1007/s11277-022-09810-y126:2(1601-1619)Online publication date: 4-Jul-2022
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    cover image ACM Conferences
    mmNets '18: Proceedings of the 2nd ACM Workshop on Millimeter Wave Networks and Sensing Systems
    October 2018
    75 pages
    ISBN:9781450359283
    DOI:10.1145/3264492
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 01 October 2018

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    Author Tags

    1. 60 ghz
    2. hybrid beamforming
    3. hybrid mimo
    4. mimo
    5. mm-wave
    6. multi-user
    7. phase noise

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    View all
    • (2023)High-speed Machine Learning-enhanced Receiver for Millimeter-Wave SystemsIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10229087(1-10)Online publication date: 17-May-2023
    • (2022)Model-free machine learning of wireless SISO/MIMO communicationsComputer Communications10.1016/j.comcom.2021.09.033181:C(192-202)Online publication date: 1-Jan-2022
    • (2022)Analysis of Phase Noise Issues in Millimeter Wave Systems for 5G CommunicationsWireless Personal Communications10.1007/s11277-022-09810-y126:2(1601-1619)Online publication date: 4-Jul-2022
    • (2020)A Mixture Density Channel Model for Deep Learning-Based Wireless Physical Layer DesignProceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems10.1145/3416010.3423229(53-62)Online publication date: 16-Nov-2020

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