For future 5G systems significant performance benefits are expected from massive MIMO, especially in combination with tight inter-cell cooperation including joint-transmission using cooperative multi-point transmission. Most massive MIMO evaluations concentrate on the base station side with the goal to achieve high spectral efficiency by MU-MIMO or large coverage by strong beamforming gains. Especially for the, here interesting, below 6 GHz RF-bands user equipment (UE) sided analysis is typically limited to four or mostly eight antenna elements per UE, which can be justified by the limited space to place more antenna elements as well as the related UE complexity. At the same time, there would be many benefits from UE-sided beamforming, ranging from improved channel estimation and prediction accuracy, effective interference suppression up to coverage and spectral efficiency gains on the system level. In our previous work, we have already proposed virtual beamforming for channel estimation and prediction, while here we extend the concept to user data transmission over virtually generated beams. Virtual beamforming directly applied to user data can be very inefficient, a challenge we overcome by parallel transmission over a set of coded virtual beams.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Virtual Massive MIMO Beamforming Gains for 5G User Terminals


    Contributors:


    Publication date :

    2016-09-01


    Size :

    381382 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    User Selection and Rank Adaptation for Multi-User Massive MIMO with Hybrid Beamforming

    Miyazaki, Hiroyuki / Suyama, Satoshi / Okuyama, Tatsuki et al. | IEEE | 2017


    Hybrid Beamforming for Downlink Massive MIMO Systems with Multiantenna User Equipment

    Payami, Sohail / Ghoraishi, Mir / Dianati, Mehrdad | IEEE | 2017



    Robust Non-Coherent Beamforming for FDD Downlink Massive MIMO

    Rottenberg, Francois / Lee, Ming-Chun / Choi, Thomas et al. | IEEE | 2020


    Generalizing Hybrid Beamforming Solutions for Massive MIMO Systems

    Alarfaj, Mohammed / Liu, Huaping | IEEE | 2017