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Symbol-level precoding (SLP), which converts the harmful multi-userinterference (MUI) into beneficial signals, can significantly improvesymbol-error-rate (SER) performance in multi-user communication systems. Whileenjoying symbolic gain, however, the complicated non-linear symbol-by-symbolprecoder design suffers high computational complexity exponential with thenumber of users, which is unaffordable in realistic systems. In this paper, wepropose a novel low-complexity grouped SLP (G-SLP) approach and developefficient design algorithms for typical max-min fairness and power minimizationproblems. In particular, after dividing all users into several groups, theprecoders for each group are separately designed on a symbol-by-symbol basis byonly utilizing the symbol information of the users in that group, in which theintra-group MUI is exploited using the concept of constructive interference(CI) and the inter-group MUI is also effectively suppressed. In order tofurther reduce the computational complexity, we utilize the Lagrangian dual,Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM)method to transform the resulting problems into more tractable forms, anddevelop efficient algorithms for obtaining closed-form solutions to them.Extensive simulation results illustrate that the proposed G-SLP strategy anddesign algorithms dramatically reduce the computational complexity withoutcausing significant performance loss compared with the traditional SLP schemes.

Low-Complexity Designs of Symbol-Level Precoding for MU-MISO Systems