Future mobile radio systems, like 5G, are setting extremely demanding targets with respect to the number of served users, data rates, and latency etc. Advanced techniques such as massive MIMO and joint cooperation over several distributed radio stations are being developed to achieve these targets. Channel prediction has been deemed to be a potential main enabler for these techniques. Here, we exploit the correlation present in the multi-dimensional massive MIMO channel and apply HOSVD-based techniques to improve the prediction of weak channels by devising a denoising step before the state-of-the-art channel predictor. We show that at low signal-to-noise ratios and prediction horizons of 1 ms, there is a gain of more than 7 dB in prediction performance.


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    Titel :

    HOSVD-Based Denoising for Improved Channel Prediction of Weak Massive MIMO Channels


    Beteiligte:


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    369228 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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