We investigate constructive interference (CI)-based symbol-level precoding (SLP) in large-scale systems with massive connectivity of users to minimize the transmit power subject to the instantaneous signal-to-interference-plus-noise-ratio (SINR) and CI constraints. By converting the considered problem into a novel separable formulation, we reveal the existence of separability in SLP, which is therefore well-suited for decomposition. The proximal Jacobian alternating direction method of multipliers (PJ-ADMM) framework is adopted to decompose the reformulated problem into multiple subproblems, which can be solved in parallel with closed-form solutions. We further linearize the second-order terms by approximation, which leads to a parallelizable first-order fast solution to SLP. Our derivations are validated by simulation results, which also show that our algorithm can provide optimal performance with substantially lower computational complexity than state-of-the-art algorithms.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Parallelizable First-Order Fast Algorithm for Symbol-Level Precoding in Lage-Scale Systems


    Beteiligte:
    Yang, Junwen (Autor:in) / Li, Ang (Autor:in) / Liao, Xuewen (Autor:in) / Masouros, Christos (Autor:in)


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    1084592 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A parallelizable load balancing algorithm

    LOEHNER, RAINALD / RAMAMURTI, RAVI / MARTIN, DOROTHEE | AIAA | 1993


    SYMBOL VS BLOCK LEVEL PRECODING IN MULTI-BEAM SATELLITE SYSTEMS

    Kayhan, F. / Haqiqatnejad, A. / Grotz, J. et al. | TIBKAT | 2019



    Symbol Level Beam Selection and Precoding in mm-wave Beamspace MU-MISO Systems

    Choi, Yongin / Oh, Jinwoo / Kwon, Yangsoo et al. | IEEE | 2021


    Unsupervised Learning Based Symbol-Level Precoding Design for Amplitude Phase Modulation

    Zhao, Liangyuan / Ju, Hao / Ou, Xiaowu et al. | IEEE | 2024