  We propose a parallel constructive interference (CI)-based symbol-level
precoding (SLP) approach for massive connectivity in the downlink of multiuser
multiple-input single-output (MU-MISO) systems, with only local channel state
information (CSI) used at each processor unit and limited information exchange
between processor units. By reformulating the power minimization (PM) SLP
problem and exploiting the separability of the corresponding reformulation, the
original problem is decomposed into several parallel subproblems via the ADMM
framework with closed-form solutions, leading to a substantial reduction in
computational complexity. The sufficient condition for guaranteeing the
convergence of the proposed approach is derived, based on which an adaptive
parameter tuning strategy is proposed to accelerate the convergence rate. To
avoid the large-dimension matrix inverse operation, an efficient algorithm is
proposed by employing the standard proximal term and by leveraging the singular
value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted
to fully eliminate the matrix inversion, and a parallel inverse-free SLP
(PIF-SLP) algorithm is finally obtained. Numerical results validate our
derivations above, and demonstrate that the proposed PIF-SLP algorithm can
significantly reduce the computational complexity compared to the
state-of-the-arts.
