  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.
