Massive multiple-input multiple-output (MIMO) precoders are typically designed by minimizing the transmit power subject to a quality-of-service (QoS) constraint. However, current sustainability goals incentivize more energy-efficient solutions and thus it is of paramount importance to minimize the consumed power directly. Minimizing the consumed power of the power amplifier (PA), one of the most consuming components, gives rise to a convex, non-differentiable optimization problem, which has been solved in the past using conventional convex solvers. Additionally, this problem can be solved using a proximal gradient descent (PGD) algorithm, which suffers from slow convergence. In this work, in order to overcome the slow convergence, a deep unfolded version of the algorithm is proposed, which can achieve close-to-optimal solutions in only 20 iterations as compared to the 3500 plus iterations needed by the PGD algorithm. Results indicate that the deep unfolding algorithm is three orders of magnitude faster than a conventional convex solver and four orders of magnitude faster than the PGD.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Unfolding for Fast Linear Massive MIMO Precoders under a PA Consumption Model


    Contributors:


    Publication date :

    2023-06-01


    Size :

    866838 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Unfolding-based Detection for Quantized Massive MU-MIMO-OFDM Systems

    Liu, Changjiang / Thompson, John / Arslan, Tughrul | IEEE | 2022


    Deep Unfolding Scheme for Grant-Free Massive-Access Vehicular Networks

    Dang, Xiaobing / Xiang, Wei / Yuan, Lei et al. | IEEE | 2023


    Hybrid Beamforming in mmWave MIMO-OFDM Systems via Deep Unfolding

    Chen, Kuan-Yuan / Chang, Hsin-Yuan / Chang, Ronald Y. et al. | IEEE | 2022


    Geometrical Model for Massive MIMO Systems

    Cheng, Xudong / He, Yejun | IEEE | 2017