In this work, we propose SVDNet, a novel deep learning (DL) architecture that utilizes singular value decomposition (SVD), for transmit power control in a multiuser multiple input multiple output (MU-MIMO) system. We propose a novel method of training SVDNet in a supervised manner for the power control task by using binary cross-entropy loss functions. SVDNet requires fewer computations than traditional power control algorithms such as weighted minimum mean squared error (WMMSE). Our simulation results show that the proposed SVDNet provides over a 50% increase in sum-rate performance as compared to similar supervised DL-based power control schemes while being significantly more computationally efficient than WMMSE.


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    Title :

    SVDNet: Deep Power Control for Multiuser MIMO


    Contributors:


    Publication date :

    2023-06-01


    Size :

    2407483 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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