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- W4288418410 abstract "In this paper, we propose a low-complexity method to approximately solve the SINR-constrained optimization problem of symbol-level precoding (SLP). First, assuming a generic modulation scheme, the precoding optimization problem is recast as a standard non-negative least squares (NNLS). Then, we improve an existing closed-form SLP (CF-SLP) scheme using the conditions for nearly perfect recovery of the optimal solution support, followed by solving a reduced system of linear equations. We show through simulation results that in comparison with the CF-SLP method, the improved approximate solution of this paper, referred to as ICF-SLP, significantly enhances the performance with a negligible increase in complexity. We also provide comparisons with a fast-converging iterative NNLS algorithm, where it is shown that the ICF-SLP method is comparable in performance to the iterative algorithm with a limited maximum number of iterations. Analytic discussions on the complexities of different methods are provided, verifying the computational efficiency of the proposed method. Our results further indicate that the ICF-SLP scheme performs quite close to the optimal SLP, particularly in the large system regime." @default.
- W4288418410 created "2022-07-29" @default.
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- W4288418410 date "2019-03-08" @default.
- W4288418410 modified "2023-09-23" @default.
- W4288418410 title "An Approximate Solution for Symbol-Level Multiuser Precoding Using Support Recovery" @default.
- W4288418410 doi "https://doi.org/10.48550/arxiv.1903.03377" @default.
- W4288418410 hasPublicationYear "2019" @default.
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